apraga/org - Change 7NDZXAGUWT2JOXEDHCP7OGVNLC7XUNJ2MNEBKDJKSJ2ZFL5HF4YAC

All tasks are now in the default agenda

Created by Alexis Praga on July 30, 2023

7NDZXAGUWT2JOXEDHCP7OGVNLC7XUNJ2MNEBKDJKSJ2ZFL5HF4YAC

Dependencies

In channels

main

Change contents

Deletion in projects.org at line 1 [5.123895]

B:BD[5.123895] → [6.14:34]

∅:D[6.34] → [7.7607:7707]

B:BD[7.7607] → [7.7607:7707]

B:BD[7.7707] → [8.17:152]

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∅:D[13.599] → [11.4675:5100]

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B:BD[13.607] → [14.339:358]

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∅:D[14.810] → [7.8575:8591]

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∅:D[14.961] → [7.8648:8664]

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∅:D[14.1112] → [7.8721:8737]

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B:BD[7.8737] → [14.1113:1410]

∅:D[14.1410] → [7.8848:8907]

B:BD[7.8848] → [7.8848:8907]

B:BD[7.8907] → [16.334:431]

B:BD[16.431] → [17.12:28]

B:BD[17.28] → [18.847:958]

∅:D[18.958] → [19.92:128]

B:BD[19.92] → [19.92:128]

B:BD[19.128] → [20.314:351]

B:BD[18.1049] → [18.1049:1768]

B:BD[18.1768] → [21.14:59]

∅:D[21.59] → [6.35:63]

B:BD[18.1813] → [6.35:63]

∅:D[6.63] → [18.1841:2146]

B:BD[18.1841] → [18.1841:2146]

B:BD[18.2146] → [20.352:378]

B:BD[20.378] → [6.64:92]

∅:D[6.92] → [20.379:456]

B:BD[18.2200] → [20.379:456]

∅:D[20.456] → [22.160:835]

B:BD[22.160] → [22.160:835]

∅:D[22.835] → [18.2200:2249]

B:BD[18.2200] → [18.2200:2249]

B:BD[18.2249] → [6.93:121]

∅:D[6.121] → [18.2277:2304]

B:BD[18.2277] → [18.2277:2304]

B:BD[18.2304] → [6.122:132]

B:BD[6.132] → [23.14:103]

B:BD[23.103] → [14.1411:1533]

∅:D[23.103] → [6.190:251]

∅:D[14.1533] → [6.190:251]

B:BD[6.190] → [6.190:251]

* Projets personnel
** Assistant
:PROPERTIES:
:CATEGORY: assistant
:END:
*** DONE Regarder ce qu'Yvain a fait
** CentoX
*** Raw data
**** DONE Ne plus utiliser les .size mais récupérer directement la taille sur le serveur
CLOSED: [2022-07-26 Tue 17:36]
**** DONE Faire fonctionner taches récurrente avec Windows
CLOSED: [2022-11-19 Sat 17:34]
Erreur 0x1 : a priori résolu en démarrant le script dans le bon répertoire...
*** Notes
**** Extraction de données
- Trop compliqué de travailles sur la structure du pdf
- Extraire le tableau avec python ?
- Comparaison
  - pdftotext : bon résultats sur page 1. Sur les ségrégations, il n’y a pas de retour à la ligne avant l’indication
- pdfminer donne des résultats légèrement supérieurs, beaucoup d’espaces sur les ségrégations
  - pypdf2 : trop de retours à la lignes intempestifs dans le texte
La structure du  tableau est perdue dans tous les cas
**** Parser
Liste de parser : https://tomassetti.me/parsing-in-python/
* Learning Haskell :haskell:
** [#A] [[https://www.reddit.com/r/haskell/comments/npxfba/comment/h084wwa/?utm_source=share&utm_medium=web2x&context=3][Reddit suggestion]]]
*** Learn Foundational building blocks
- [X] [[https://mmhaskell.com/monads/functors][Functor]]
- [X] [[https://mmhaskell.com/monads/applicatives][Applicatives]]
- [X] [[https://mmhaskell.com/monads/tutorial][Monads]]
- [X] [[https://mmhaskell.com/monads/reader-writer][Reader, writer]]
- [X] [[https://mmhaskell.com/monads/state][State]]
- [X] [[https://mmhaskell.com/monads/transformers][Transformers]]
- [ ] [[https://mmhaskell.com/monads/laws][Laws]]
*** Real-world example
**** STRT Look at the example
- [X] Database
- [ ] API
**** Relax for a few days and watch how interactive programs are being composed
**** Get back to the real-world example and make it a complete Cabal project.
**** [[https://mmhaskell.com/testing/test-driven-development][Testing]]
*** [#A] Best resource : [[https://downloads.haskell.org/~ghc/8.10.4/docs/html/users_guide/glasgow_exts.html#language-options][Language Reference]]
whenever you see an unknown language extension or a compilation flag, look it up in Language Reference and try to understand it. You don't have to fully understand them though, just read about them and keep them on your mind. One day they will begin to automatically click into a sound set of concepts.
Language Reference is one of the most underappreciated sources of information (it's almost universally overlooked in language communities - it was the case for Python, and I find it to be true for Haskell as well). You mentioned that you don't like REPL examples, and neither do I. Luckily, the User Guide/Reference has introductory sections for people like us. Once I knew how to compile a single file and to run it, the rest was just a matter of getting to know things by their name in a new ecosystem.
*** DONE Learn to compose things
When you already know how to compile and run single-module interactive console programs, it takes about a day to understand basics of Cabal, and about a week to learn about input parsing and output formatting. Do you need CLI args? Use optparse-applicative. Env vars? Use envy. JSON? Use aeson and a cheatsheet. Don't think about performance and/or API conventions, that's not what you should be concerned of at this point, as you are just learning to compose things together from individual parts.
*** Experiment with various libraires, read haskell planetarium
At this point you have enough knowledge to begin experimenting with various libraries and APIs. Learn how to use Hoogle, and read as much as you can/want on Haskell Planetarium.
** KILL Learn Haskell for your greater good
   :PROPERTIES:
   :CUSTOM_ID: kill-learn-haskell-for-your-greater-good
   :END:
50%
** HOLD [[books.org::Haskell%20Programming%20From%20First%20Principles][Haskell programming from first principles]]
** GHC
*** GHC commentary
Notamment Ollie Charles's 24 days of GHC Extensions,
*** Lire [[https://www.aosabook.org/en/ghc.html]]
** Vidéos
*** STRT https://www.youtube.com/watch?v=re96UgMk6GQ
** Articles historiques
1. [[https://watermark.silverchair.com/320098.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAsYwggLCBgkqhkiG9w0BBwagggKzMIICrwIBADCCAqgGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMHXfjdjwhGI2t4bLLAgEQgIICeQjZ-I8gmuaFqBktP4IOifHODtMAHcNF_LwRYyq7NswQ7vT6LJho9P_junCAORLGMV9dgq9JMePH2PFKNxXxrEP1VY7rIDG0gzoeObSkgMDn4MXalrIxD3ejY8vsGYy6vce8Kh70J_UJ8RamO1l3BNNUzy2W6VRaa_cMQr_ekdwcz0oihz0BVKn_bgm_8DjiiPhzj8uU9flVhi13t_oIFA6b3At2QMmPe7Z9OyfLkXivKkmKKNoHwSS7AnTIYAKCO383e4kG6NzZ_elai-XMAJs2Nk0vcgaltld1KeaW3269104DdIlFGevJUVNgwE_4LIheSYRZr9Gr0yRR6TROxdsyxrmgQ22Pzxxpnl8-KdjkW6aRSCKNk_yb5hYcPoRa3ldc5yPV15j8i4t9Mv4U_mBwmIRtMIKPdEHeMvcRx6c8_8uT4RV2esuOPfZlA05bzBgJhMS87M8myxisH-exkTMkm58o6nzHf1lGxzn_JS1VSHbhJCUl82ubzzOWjvl3QJM_vv805XTbn_G-fcRi0d9EQIRTqoObWVFyXW-pz16bWoZPZnBQ1gOmc3hPTGBMZjFR6p9VEAO7bKcK8o0yQDjVWEELNwfAAHc-oF_wLiEjXDNBoUttghgQzzvymKY_jSZhcU8TraVu2i551fpuDNEjSJd0qY5Rg3J6eWU550nJmnoWmX6o7KGiYp0vVMfOoFYXJ1trZWSGoRhDQP2LOLIOt3t2idlj6kV_MoCY3BRnkbxf4XIH7gLJf6Dky6hXFbTU8Fjsn8XHBeKSmaAYJ-sbmGB_BdZO8hHyvHvPv0lTtGcSuKywoJhMbblXRzyuacj_6mZQl5j3tAWhy][Why functional programming matters]]
   Très lisible
2. [[https://dl.acm.org/doi/pdf/10.1145/91556.91592][Comprehending monads]]
   Introduction du concept
3. [[https://dl.acm.org/doi/pdf/10.1145/158511.158524][Imperative functional programming]]
   Application des monads poru résoudre le problème IO
* Langues
** Japonais
:PROPERTIES:
:CATEGORY: japonais
:END:
*** Miura [7/7]
**** DONE Leçon 1 [/]
***** DONE Lire
***** DONE Anki
**** KILL Leçon 2 [2/2]
CLOSED: [2023-07-07 Fri 18:48]
***** DONE Lire
***** KILL Anki
CLOSED: [2023-07-07 Fri 18:48]
****** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:48]
**** KILL Leçon 3
CLOSED: [2023-07-07 Fri 18:44]
***** DONE Lire
***** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
****** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
**** KILL Leçon 4
CLOSED: [2023-07-07 Fri 18:44]
***** DONE Lire
***** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
****** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
**** KILL Leçon 5
CLOSED: [2023-07-07 Fri 18:44]
***** DONE Lire
***** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
****** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
**** KILL Leçon 6
CLOSED: [2023-07-07 Fri 18:44]
***** DONE Lire
***** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
****** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
**** KILL Leçon 7
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Lire
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
****** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
**** Lire
*** Leçon Aya
:PROPERTIES:
:CATEGORY: aya
:END:
**** KILL Lire dialogue fin leçon 10
CLOSED: [2022-12-03 Sat 12:34] SCHEDULED: <2022-07-30 Sat>
* Julia :julia:
** KILL XAM.jl: PR pour modification record :julia:
CLOSED: [2023-05-29 Mon 15:40] SCHEDULED: <2023-05-28 Sun>
/Entered on/ [2023-05-27 Sat 22:39]
** TODO XAMscissors.jl :xamscissors:
Modification de la séquence dans BAM.
*Pas de mise à jour de CIGAR*
On convertit en fastq et on lance le pipeline pour "corriger"
#+begin_src sh
cd /home/alex/code/bisonex/out/63003856/preprocessing/mapped
samtools view 63003856_S135.bam NC_000022.11 -o 63003856_S135_chr22.bam
cd /home/alex/recherche/bisonex/code/BamScissors.jl
cp ~/code/bisonex/out/63003856/preprocessing/mapped/63003856_S135_chr22.bam .
samtools index 63003856_chr22.bam
#+end_src
Le script va modifier le bam, le trier et générer le fastq. !!!
Attention: ne pas oublier l'option -n !!!
#+begin_src sh
time julia --project=.. insertVariant.jl
scp 63003856_S135_chr22_{1,2}.fq.gz meso:/Work/Users/apraga/bisonex/tests/bamscissors/
#+end_src
*** WAIT Implémenter les SNV avec VAF :snv:
SCHEDULED: <2023-07-07 Fri>
Stratégie :
1. calculer la profondeur sur les positions
2. créer un dictionnaire { nom du reads : position dataframe }
3. itérer sur tous les reads et changer ceux marqués
**** DONE VAF = 1
CLOSED: [2023-05-29 Mon 15:34]
**** DONE VAF selon loi normale
CLOSED: [2023-05-29 Mon 15:35]
Tronquée si > 1
**** WAIT Tests unitaires
SCHEDULED: <2023-07-07 Fri>
***** DONE NA12878: 1 gène sur chromosome 22
CLOSED: [2023-05-30 Tue 23:55]
root = "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/"
#+begin_src sh
samtools view project.NIST_NIST7035_H7AP8ADXX_NA12878.bwa.markDuplicates.bam  chr22 -o project.NIST_NIST7035_H7AP8ADXX_NA12878_chr22.bam
samtools view project.NIST_NIST7035_H7AP8ADXX_NA12878_chr22.bam chr22:19419700-19424000 -o NIST7035_H7AP8ADXX_NA12878_chr22_MRPL40_hg19.bam
#+end_src
***** WAIT Pull request formatspeciment
https://github.com/BioJulia/FormatSpecimens.jl/pull/8
***** DONE Formatspecimens
CLOSED: [2023-05-29 Mon 23:03]
****** DONE 1 read
CLOSED: [2023-05-29 Mon 23:02]
****** DONE VAF sur 1 exon
CLOSED: [2023-05-29 Mon 23:03]
*** TODO Implémenter les indel avec VAF :indel:
SCHEDULED: <2023-07-07 Fri>
*** TODO Soumission paquet
* Famille
** DONE Cadeaux Maxence + Emy
CLOSED: [2023-07-02 Sun 18:20] SCHEDULED: <2023-07-02 Sun>
/Entered on/ [2023-07-02 Sun 10:53]
Maxence : Éveil musical
** DONE Cadeaux Maxence + Emy
CLOSED: [2023-07-07 Fri 18:40]
/Entered on/ [2023-07-02 Sun 10:53]
Maxence : Éveil musical

Deletion in projects.org at line 32 [5.123895]
∅:D[21.1219] → [17.63:79]
B:BD[7.9023] → [17.63:79]
∅:D[17.79] → [7.9379:9392]
∅:D[24.957] → [7.9379:9392]
B:BD[7.9379] → [7.9379:9392]
B:BD[7.9392] → [17.80:98]
∅:D[17.98] → [7.9410:9416]
B:BD[7.9410] → [7.9410:9416]
```
* Comptabilité
:PROPERTIES:
:CATEGORY: ledger
:END:
```
Replacement in projects.org at line 50 [5.123895]
∅:D[25.127] → [7.10513:10526]
B:BD[7.10513] → [7.10513:10526]
```
** NF1 :nf1:
```
[25.127]
[26.17]
```
** NF1
:PROPERTIES:
:CATEGORY: nf1
:END:
```
Replacement in projects.org at line 265 [5.123895]
B:BD[27.3298] → [3.133:160]
∅:D[3.160] → [21.1357:1385]
B:BD[21.1357] → [21.1357:1385]
```
***** WAIT Soumission AJMG
SCHEDULED: <2023-07-26 Wed>
```
[27.3298]
[3.161]
```
***** DONE Soumission AJMG
CLOSED: [2023-07-30 Sun 14:50] SCHEDULED: <2023-07-26 Wed>
```

Deletion in projects.org at line 308 [5.123895]

∅:D[3.727] → [28.1890:1891]

B:BD[28.1890] → [28.1890:1891]

∅:D[29.396] → [7.10811:10832]

∅:D[30.495] → [7.10811:10832]

∅:D[31.737] → [7.10811:10832]

∅:D[28.1891] → [7.10811:10832]

∅:D[32.1957] → [7.10811:10832]

∅:D[27.3300] → [7.10811:10832]

B:BD[7.10811] → [7.10811:10832]

B:BD[7.10832] → [33.69:81]

B:BD[33.81] → [34.14:52]

∅:D[34.52] → [33.114:201]

B:BD[33.114] → [33.114:201]

∅:D[33.201] → [35.175:220]

B:BD[35.175] → [35.175:220]

B:BD[35.220] → [36.117:162]

∅:D[36.162] → [37.1153:1154]

B:BD[35.788] → [37.1153:1154]

B:BD[37.1154] → [34.53:89]

∅:D[34.89] → [38.86:884]

B:BD[38.86] → [38.86:884]

∅:D[38.884] → [35.788:789]

B:BD[35.788] → [35.788:789]

B:BD[35.789] → [34.90:119]

∅:D[34.119] → [38.909:1591]

B:BD[38.909] → [38.909:1591]

B:BD[38.1591] → [14.1534:1617]

∅:D[14.1617] → [39.94:172]

B:BD[39.94] → [39.94:172]

∅:D[39.172] → [40.481:482]

B:BD[40.481] → [40.481:482]

B:BD[40.482] → [16.532:647]

∅:D[16.647] → [39.257:376]

B:BD[39.257] → [39.257:376]

B:BD[39.376] → [41.496:497]

B:BD[41.497] → [14.1618:1933]

∅:D[42.31] → [16.716:738]

∅:D[17.127] → [16.716:738]

∅:D[43.215] → [16.716:738]

∅:D[14.1933] → [16.716:738]

B:BD[16.716] → [16.716:738]

∅:D[39.376] → [40.482:483]

∅:D[16.738] → [40.482:483]

B:BD[40.482] → [40.482:483]

∅:D[44.96] → [45.397:434]

∅:D[46.308] → [45.397:434]

∅:D[40.483] → [45.397:434]

∅:D[38.1603] → [45.397:434]

∅:D[47.2470] → [45.397:434]

∅:D[48.2989] → [45.397:434]

B:BD[35.789] → [45.397:434]

B:BD[45.434] → [14.1934:2030]

∅:D[36.226] → [34.120:236]

∅:D[49.554] → [34.120:236]

∅:D[14.2030] → [34.120:236]

B:BD[50.80] → [34.120:236]

∅:D[51.68] → [35.789:808]

∅:D[52.146] → [35.789:808]

∅:D[34.236] → [35.789:808]

∅:D[45.496] → [35.789:808]

∅:D[38.1666] → [35.789:808]

B:BD[35.789] → [35.789:808]

B:BD[35.867] → [35.867:886]

B:BD[35.945] → [35.945:964]

B:BD[33.261] → [33.261:280]

B:BD[33.280] → [52.147:204]

B:BD[52.204] → [38.1667:1686]

B:BD[38.1686] → [53.81:100]

B:BD[53.100] → [50.81:231]

B:BD[50.231] → [36.227:265]

B:BD[36.265] → [49.555:1255]

B:BD[49.1255] → [14.2031:3934]

∅:D[14.3934] → [52.205:224]

B:BD[35.3187] → [52.205:224]

B:BD[52.224] → [14.3935:24417]


** Mustard :mustard:
*** Scripts
**** DONE Script pour données labkey
on convertit tous les pdf en png puis OCR avec tesseract pour les transformer en texte
On supprimer les header et footer à la main
Cf ~/code/scripts/python/mustard/courrier.py
**** DONE Renommer les dossiers PED
#+begin_src python :results output
import pandas as pd
import os
import os.path
dir1 = "/alexi/Documents/mustard/"
dir2 = "/alexi/Documents/mustard-new/"
p  = pd.read_csv(os.path.join(dir1, "Patients_2022-02-02_11-44-03.tsv"), sep='\t')
# id + p.nom + " " + p.prenom + " " + p.date_de_naissance
for i in p.index:
    split = p['patientID'][i].split(".")
    # Only store the index case
    if split[1] == "1":
        dest = p.nom[i].upper() + " " + p.prenom[i] + " " + p.date_de_naissance[i]
        print(f"ok {split[0]} {dest}")
        src = os.path.join(dir1, split[0])
        if os.path.exists(src):
            if p.nom[i] != "Non renseigné":
                os.rename(src, os.path.join(dir2, dest))
            else:
                os.rename(src, os.path.join(dir2, split[0]))
#+end_src
**** DONE Générer clinique
#+begin_src python :results output
import pandas as pd
import os
import os.path
dir = "/alexi/Documents/mustard/"
p  = pd.read_csv(os.path.join(dir, "Patients_2022-02-02_11-44-03.tsv"), sep='\t')
# id + p.nom + " " + p.prenom + " " + p.date_de_naissance
f = open(os.path.join(dir, "clinique2.csv"), 'w')
for i in p.index:
    split = p['patientID'][i].split(".")
    # Only store the index case
    if split[1] == "1":
        folder = p.nom[i].upper() + " " + p.prenom[i] + " " + p.date_de_naissance[i]
        if os.path.exists(os.path.join(dir, folder)):
            f.write(split[0] + ";" + p.nom[i].upper() + ";" + p.prenom[i] + ";" + p.date_de_naissance[i] + "\n")
#+end_src
**** KILL Stats sur la balance allélique pour Paul
CLOSED: [2023-07-07 Fri 18:47]
***** DONE Besançon seul : total, par variant
CLOSED: [2022-10-28 Fri 10:57]
***** DONE Dijon + Besançon seul : total, par variant et par type de prélèvement
CLOSED: [2022-12-03 Sat 12:35]
Dans "variations à vérifier". 1 seul variant normalement en miseq, parfois plusieurs en exome
AB = "allelic balance"
***** KILL Rajouter une colonne balance allélique
CLOSED: [2023-07-07 Fri 18:47]
****** KILL Ancien panel
CLOSED: [2023-07-07 Fri 18:44]
****** KILL Nouveau panel
CLOSED: [2023-07-07 Fri 18:44]
****** KILL Dijon
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Version executable pour paul
CLOSED: [2023-07-07 Fri 18:44]
Avec colonne dédiée
*** Données
**** DONE Import Labkey
**** KILL Clinique, OCR et nettoyage données labkey [1199/1199]
CLOSED: [2023-07-07 Fri 18:44]
DONE = sur scality (mis dans ~/annex/mustard/done)
SRT = traité, non transféré (en attente dans ~/annex/mustard)
***** DONE PED0052
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CLOSED: [2022-08-01 Mon 09:44]
***** DONE PED1034
CLOSED: [2022-08-01 Mon 09:44]
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CLOSED: [2022-08-01 Mon 09:44]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
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CLOSED: [2022-11-08 Tue 22:20]
***** DONE PED1077
CLOSED: [2022-11-08 Tue 22:20]
***** KILL PED1078
CLOSED: [2023-07-07 Fri 18:44]
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Deletion in projects.org at line 309 [5.123895]

B:BD[35.23689] → [54.110:152]

∅:D[54.152] → [55.1064:1095]

B:BD[55.1064] → [55.1064:1095]

B:BD[55.1095] → [54.153:249]

B:BD[54.249] → [41.618:697]

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B:BD[7.11142] → [7.11142:11191]

B:BD[7.11191] → [56.78:136]

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B:BD[7.11218] → [57.17:529]

**** DONE Fusionner exome dijon pour Paul
CLOSED: [2022-08-04 Thu 17:42]
***** DONE Enlever les doublons
CLOSED: [2022-09-13 Tue 21:36]
**** DONE Fusionner panel Dijons
**** DONE Fusion variants à vérifier de dijon
CLOSED: [2022-12-04 Sun 22:32]
**** KILL Dxcare
***** DONE Demande Dijon
***** KILL Demande DPO Besançon
**** KILL donnée pierre
**CLOSED: [2022-05-05 jeu. 17:53]
****** KILL Format de données final
CLOSED: [2023-07-07 Fri 18:44]
Voir avec Paul
*** Stockage
**** DONE Accès scality au travail
**** KILL VPN pour Jehanne
CLOSED: [2023-05-28 Sun 10:04]
*** Notes
- Sur phénotype mélanocytaire, il peut valoir le coup de faire de la CGH sur biopsie
  Inconvénient du panel : on passe à côté
  Inconvénient de l’exome : faible profondeur
  En général, pas d’ADN suffisant pour les 3 !
- Idée de pipeline : CNV (mais il faut les références)
- 2 approches : exome direct ou CGH + panel
- Exome envoyé à integragen (ou CNR): problème = perte de financement car plus de centre de référence à Dijon
  envoi dans le privé compliqué vu le coût...

Deletion in projects.org at line 326 [5.123895]
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B:BD[7.11224] → [7.11224:11242]
```
* Banque :banque:
```

Deletion in projects.org at line 572 [5.123895]

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** DONE DES [4/4]
CLOSED: [2023-07-07 Fri 18:47]
*** DONE Valider cours sur sides
*** KILL Vérifier que toutes les diapos sont sur one drive
CLOSED: [2023-07-07 Fri 18:47]
*** DONE Examen sur sides
*** KILL Lire les cours
CLOSED: [2023-07-07 Fri 18:47]
**** KILL Presentiel session 1 [9/9]
CLOSED: [2022-11-19 Sat 17:43]
***** DONE Introduction à la dysmorphologie
***** DONE Structuration du génome et mécanismes mutationnels
***** DONE Oncogénétique: introduction
***** KILL Diagnostic prénatal
CLOSED: [2022-11-19 Sat 17:35]
***** DONE Grandes technologies et bioinformatique
***** DONE Aspects réglementaires et éthiques
***** DONE Mucoviscidose
CLOSED: [2022-09-10 Sat 18:34]
***** KILL Bases sur le conseil génétique
CLOSED: [2022-11-19 Sat 17:35]
***** KILL SEPI et TD
CLOSED: [2022-11-19 Sat 17:35]
**** DONE E-learning session 1 [6/6]
***** DONE maladies endocriniennes et métabolisme
***** DONE anomalies de la croissance
***** DONE hématologie
***** DONE maladies du tissu conjonctif
***** DONE Oncogénétique
***** DONE dermatogénétique
**** KILL Presentiel session 2 [0/5]
CLOSED: [2022-11-19 Sat 17:43]
***** KILL Déficience intellectuelle
CLOSED: [2022-11-19 Sat 17:35]
***** KILL Génétique clinique et formelle
CLOSED: [2022-11-19 Sat 17:35]
***** KILL Pathologies fréquentes en génétique clinique
CLOSED: [2022-11-19 Sat 17:35]
***** KILL Génome humain : normal et pathologique
CLOSED: [2022-11-19 Sat 17:35]
***** KILL Maladies métaboliques
CLOSED: [2022-11-19 Sat 17:35]
**** KILL E-learning session 2 [6/6]
CLOSED: [2023-07-07 Fri 18:47]
***** DONE Infertilité
-> cours 1, diapo 31
***** DONE Syndromes microdélétionnels
***** DONE Dysgonosomies
***** DONE Cancer du colon: Maladie de Lynch et CMMRD
***** DONE Déficience intellectuelle
***** KILL Pathologies neuromusculaires
CLOSED: [2023-07-07 Fri 18:47]
** KILL DIU dysmorpho
:PROPERTIES:
:CATEGORY: dysmorpho
:END:
*** KILL Relire + notes [1/92]
**** KILL Intro dysmorpho - Verloes
CLOSED: [2023-07-07 Fri 18:43]
**** KILL Empreinte génomique
**** KILL Beckwith, Silver Russel
**** KILL Scoliose
**** KILL Syndromes cytogénétique - Salanville
**** KILL Dysostose mandibulo faciale
**** KILL Williams dup 7p11.2
**** KILL Pathologie génétique de la reproduction
**** KILL Malformations oculaires
**** KILL Comprendre les test génétiques
**** KILL Fente
**** KILL Gonosome
**** KILL Smith-Mangenis
**** KILL 22q11
**** KILL Dysmorpho nouveau-né
**** KILL Autopsie foetale
**** KILL Dysmorphologie - généralités (A Verloes)
**** KILL Dysmorphologie du nouveau né (M Vincent)
**** KILL Registre des malformations (N Lelong)
**** KILL Comprendre les tests génétiques - Mutations - NGS (Y Vial)
**** KILL Cytogénétique (C Missirian)
**** KILL NGS et syndromologie (F Tran-Mau-Them)
**** KILL Empreinte génomique (F Brioudé) (seq 15 Beckwith Wiedemann Syndrome et SRussel S)
**** KILL Autopsie foetale (F Guimiot)
**** KILL Tumeur et développement (H Cave)
**** KILL Dysmorphologie foetale (MH Saint Frison)
**** KILL Pathologie génétique de la reproduction (F Vialard)
**** KILL Le dysmorphologiste en prénatal (N Gruchy)
**** KILL Régulation génique et  anomalies du développement (F Petit)
**** KILL Echographie fœtale et dysmorphologie (C Rozel)
**** KILL Déficience intellectuelle (A Curie)
**** KILL Autisme et génétique (A Maruani)
**** KILL Tests neuropsy
**** KILL XLID(A Toutain)
**** KILL Anomalies du développement embryonnaire précoce (C Quelin)
**** KILL Anomalies de fermeture du tube neural (C Quelin)
**** KILL FAS (D Germanaud)
**** KILL Médicaments et grossesse (C Vauzelle)
**** KILL Syndromes avec fentes oro-faciales- (J Van-Gils)
**** KILL Syndromes avec craniosténose (C Collet)
**** KILL Dents & syndromes (I Bailleul)
**** KILL Dysostoses Mandibulo faciales (J Amiel)
**** KILL Avances staturales (A Putoux)
**** KILL Retards staturaux syndromiques (A Putoux)
**** KILL Syndromes avec obésité (G Diene)
**** KILL Spliceosomopathies (P Edery)
**** KILL Microcéphalies (S Passemard)
**** KILL Anomalies du cervelet : Joubert, NPH ... (L Burglen)
**** KILL Epilepsie et syndromes (C Mignot)
**** KILL Holoprosencéphalie (S Odent)
**** KILL Hydrocephalie (S Odent)
**** KILL Anomalies de migration (S Passemard)
**** KILL Chondrodysplasies (G Baujat)
**** KILL Anomalies de segmentation et scoliose (J Thévenon)
**** KILL Génétique du développement des membres et principaux syndromes (F Petit)
**** KILL Classification des malformations des membres (F Petit)
**** KILL Prise en charge des anomalies des membres (N Quintero)
**** KILL Syndromes avec anomalies uro-néphrologiques pré- et postnatal (G Morin)
**** KILL Syndromes avec anomalies génitales et DSD (B Leheup)
**** KILL Du coeur au syndrome (D Genevieve)
**** KILL Malformation cardiaque en anténatal (D Genevieve)
**** KILL Base génétique du déterminisme du sexe (C Colson)
**** KILL Surdités syndromiques (S Marlin)
**** KILL Malformations oculaires (N Chassaing)
**** KILL Dermatologie et développement (P Vabres)
**** KILL Dysmorphologie et métabolisme (M Barth)
**** KILL Maladies de surcharge (D Germain)
**** KILL Trisomie 21 (R Touraine)
**** KILL S. Williams - duplication 7q11.2 (M Rossi)
**** KILL Délétion 22q11.2 (L Perrin)
**** KILL Syndromes cytogénétiques (D Sanlaville)
**** KILL Gonosomes (J Leger)
**** KILL Parcours de soin des patients avec anomalies du développement (N Jean-Marçais)
**** KILL Prise en charge médicosociale du handicap (D Juzeau)
**** KILL Fanconi (T Leblanc)
**** KILL Ehlers-Danlos (D Germain)
**** KILL Chromatinopathies: TAD - Kabuki, Rubinstein-Taybi, Wiedemann-Steiner, SBYSS... (D Genevieve)
**** KILL Marfan et syndromes apparentés (G Jondeau)
**** KILL RASopathies (Y Capri)
**** KILL Syndromes de Pitt Hopkins, Angelman, Rett et Rett-like (N Bahi-Buisson)
**** KILL Filaminopathies A (C Goizet)
**** KILL Achondroplasie (G Baujat)
**** KILL OI (G Baujat)
**** KILL Ciliopathies: approche globale (T Attie-Bitach)
**** KILL Smith-Magenis (L Perrin)
**** KILL Cohésinopathies : Cornelia de Lange, Coffin-Siris/NB, CHOPS... (A Goldenberg)
**** KILL Albinisme et syndromes apparentés (B Arveiler)
**** KILL Beckwith Wiedemann Syndrome & Silver Russel Syndrome (F Brioude)
**** KILL Neurofibromatoses - STB (C Goizet)
**** KILL Cowden, Gorlin (P Goizet)
**** KILL Syndrome de Kleefstra (L Perrin)
**** KILL Téloméropathies (T Leblanc)

Deletion in projects.org at line 586 [5.123895]

B:BD[69.2233] → [2.14:195]

∅:D[2.195] → [56.137:197]

B:BD[70.858] → [56.137:197]

∅:D[56.197] → [70.887:1068]

B:BD[70.887] → [70.887:1068]

B:BD[70.1068] → [56.198:286]

∅:D[56.286] → [71.74:173]

B:BD[70.1125] → [71.74:173]

B:BD[71.173] → [56.287:334]

∅:D[56.334] → [71.174:238]

B:BD[70.1178] → [71.174:238]

∅:D[71.238] → [70.1211:1242]

B:BD[70.1211] → [70.1211:1242]

B:BD[70.1242] → [72.445:534]

∅:D[72.534] → [71.239:320]

B:BD[70.1300] → [71.239:320]

∅:D[71.320] → [73.53:89]

B:BD[73.53] → [73.53:89]

B:BD[73.89] → [42.32:137]

∅:D[42.137] → [72.609:678]

B:BD[72.609] → [72.609:678]

B:BD[72.678] → [42.138:146]

B:BD[42.146] → [56.335:416]

∅:D[56.416] → [42.146:273]

B:BD[42.146] → [42.146:273]

∅:D[42.273] → [74.262:321]

B:BD[74.262] → [74.262:321]

∅:D[74.321] → [60.375:424]

B:BD[60.375] → [60.375:424]

B:BD[60.424] → [74.322:397]

B:BD[74.397] → [75.265:347]

∅:D[75.347] → [76.674:719]

B:BD[76.674] → [76.674:719]

B:BD[76.719] → [77.14:66]

B:BD[77.66] → [70.1310:1365]

∅:D[77.66] → [78.30:47]

∅:D[70.1365] → [78.30:47]

∅:D[11.5259] → [78.30:47]

B:BD[76.719] → [78.30:47]

B:BD[78.47] → [79.463:524]

∅:D[79.524] → [80.622:647]

B:BD[78.77] → [80.622:647]

∅:D[80.647] → [78.77:124]

B:BD[78.77] → [78.77:124]

B:BD[78.124] → [80.648:708]

B:BD[80.708] → [42.274:362]

B:BD[42.390] → [43.216:227]

∅:D[43.227] → [79.608:1084]

∅:D[42.390] → [79.608:1084]

B:BD[79.608] → [79.608:1084]

B:BD[79.1084] → [6.413:505]

∅:D[43.289] → [78.153:169]

∅:D[6.505] → [78.153:169]

∅:D[80.708] → [78.153:169]

∅:D[79.1084] → [78.153:169]

B:BD[78.153] → [78.153:169]

B:BD[78.169] → [42.391:476]

∅:D[78.185] → [60.452:529]

∅:D[42.476] → [60.452:529]

B:BD[60.452] → [60.452:529]

B:BD[60.529] → [14.33326:33349]

∅:D[6.529] → [42.500:560]

∅:D[14.33349] → [42.500:560]

B:BD[42.500] → [42.500:560]

∅:D[17.156] → [60.590:626]

∅:D[42.560] → [60.590:626]

B:BD[60.590] → [60.590:626]

B:BD[60.626] → [6.530:744]

∅:D[6.744] → [81.3:121]

B:BD[42.626] → [81.3:121]

∅:D[81.121] → [82.207:255]

∅:D[42.713] → [82.207:255]

B:BD[82.207] → [82.207:255]

B:BD[82.255] → [42.714:740]

B:BD[42.740] → [81.122:168]

B:BD[81.168] → [83.3:166]

∅:D[83.166] → [42.872:888]

B:BD[42.872] → [42.872:888]

∅:D[42.888] → [82.305:350]

B:BD[82.305] → [82.305:350]

B:BD[82.350] → [81.273:319]

B:BD[81.319] → [6.745:874]

∅:D[6.874] → [42.987:1036]

B:BD[42.987] → [42.987:1036]

∅:D[42.1036] → [82.533:602]

B:BD[82.533] → [82.533:602]

B:BD[82.602] → [42.1037:1164]

∅:D[17.185] → [84.45:81]

∅:D[42.1164] → [84.45:81]

B:BD[84.45] → [84.45:81]

B:BD[84.81] → [75.398:445]

B:BD[75.445] → [42.1165:1236]

∅:D[42.1236] → [64.4485:4544]

B:BD[64.4485] → [64.4485:4544]

∅:D[64.4544] → [85.168:204]

B:BD[85.168] → [85.168:204]

B:BD[85.204] → [86.14:68]

B:BD[86.68] → [75.446:475]

∅:D[75.475] → [86.68:287]

B:BD[86.68] → [86.68:287]

B:BD[86.287] → [42.1237:1251]

∅:D[86.287] → [87.765:766]

∅:D[42.1251] → [87.765:766]

B:BD[85.204] → [87.765:766]

B:BD[87.766] → [88.3:81]

B:BD[42.1343] → [88.82:89]

∅:D[88.89] → [42.1343:1356]

B:BD[42.1343] → [42.1343:1356]

B:BD[42.1356] → [14.33350:33378]

∅:D[14.33378] → [43.290:312]

B:BD[42.1384] → [43.290:312]

∅:D[43.312] → [42.1398:1428]

B:BD[42.1398] → [42.1398:1428]

B:BD[42.1428] → [14.33379:33407]

∅:D[14.33407] → [42.1456:1484]

B:BD[42.1456] → [42.1456:1484]

B:BD[42.1484] → [14.33408:33436]

∅:D[86.397] → [80.726:727]

∅:D[42.1512] → [80.726:727]

∅:D[14.33436] → [80.726:727]

B:BD[80.726] → [80.726:727]

B:BD[80.727] → [89.14:85]

B:BD[89.85] → [43.313:314]

B:BD[43.314] → [6.875:1025]

** DONE Héberger arbre généaloqiue
CLOSED: [2023-06-24 Sat 15:42] SCHEDULED: <2023-04-14 Fri>
- Github : genealogy
- Sourcehut : genealogy
- Proton drive
- Mail <2023-06-24 Sat>
** DONE Enterrement Mme Karl
CLOSED: [2023-05-28 Sun 10:02]
*** DONE Commander bouquets
CLOSED: [2023-04-13 Thu 09:11] SCHEDULED: <2023-04-12 Wed>
*** DONE Message avec les bouquets
CLOSED: [2023-04-13 Thu 09:11] SCHEDULED: <2023-04-13 Thu>
*** DONE Remboursement [5/5]
CLOSED: [2023-05-28 Sun 10:02] SCHEDULED: <2023-04-20 Thu>
**** DONE Aurélien
CLOSED: [2023-04-22 Sat 15:27]
**** DONE Élise
CLOSED: [2023-04-22 Sat 15:27]
**** DONE Yvain
CLOSED: [2023-05-28 Sun 10:02]
**** DONE Papa
CLOSED: [2023-04-22 Sat 15:27]
**** DONE Thierry
CLOSED: [2023-04-13 Thu 09:12]
*** DONE Carte personnalisée
CLOSED: [2023-04-26 Wed 21:10] SCHEDULED: <2023-04-17 Mon>
** DONE Don index nzb
CLOSED: [2023-04-22 Sat 15:27] SCHEDULED: <2023-04-19 Wed>
/Entered on/ [2023-04-16 Sun 16:53]
** DONE Remboursement TER Grenoble du 9 avril
CLOSED: [2023-05-06 Sat 09:06] SCHEDULED: <2023-05-03 Wed>
/Entered on/ [2023-04-26 Wed 21:03]
Demande envoyé <2023-04-26 Wed>
Refusé
** TODO Demander accès déchetteries Saône
/Entered on/ [2023-05-28 Sun 10:02]
* Nixpkgs :nix:
** DONE GATK
CLOSED: [2023-05-06 Sat 08:51]
*** DONE [[https://github.com/NixOS/nixpkgs/pull/185819][Binaire]]
CLOSED: [2022-09-10 Sat 23:53] SCHEDULED: <2022-08-10 Wed>
/Entered on/ [2022-08-09 Tue 10:57]
PR submitted
*** KILL Corriger code pour utiliser source
CLOSED: [2022-09-11 Sun 22:05]
*** DONE Corriger PATH pour include java et python
CLOSED: [2022-10-11 Tue 11:46]
https://github.com/NixOS/nixpkgs/pull/191548
Review <2022-10-10 Mon> , corrigé dans la journée
*** DONE Update 4.3.0.0
CLOSED: [2023-04-13 Thu 09:01]
** TODO Nextflow
*** KILL version script seule
CLOSED: [2023-04-01 Sat 18:29]
Fix pour SGE et nextflow
https://github.com/NixOS/nixpkgs/issues/192396
*** KILL Version avec gradle
CLOSED: [2022-10-09 Sun 22:51]
*** TODO [[https://github.com/NixOS/nixpkgs/issues/192396][Bug report Version 22.10.6]]
**** Notes
Erreur :
ERROR: Cannot download nextflow required file -- make sure you can connect to the internet
Alternatively you can try to download this file:
    https://www.nextflow.io/releases/v22.10.6/nextflow-22.10.6-all.jar
and save it as:
    .//nix/store/md2b1ah4d7ivj82k8xxap30dmdci00pa-nextflow-22.10.6/bin/.nextflow-wrapped
Dans la mise à jour, il y a la création d'un environnement virtuel qui casse l'exécution de nextflow (besoin de télécharger)
Fix = désactiver
**** KILL Patch NXF_OFFLINE=true
CLOSED: [2023-07-02 Sun 11:02] SCHEDULED: <2023-06-11 Sun>
** TODO Multiqc
** TODO VEP :vep:
*** DONE [[https://github.com/NixOS/nixpkgs/pull/185691][BioPerl]]
SCHEDULED: <2022-08-10 Wed>
/Entered on/ [2022-08-09 Tue 10:57]
PR submitted
*** TODO BioDBBBigFile
SCHEDULED: <2023-05-07 Sun>
:PROPERTIES:
:ORDERED:  t
:END:
/Entered on/ [2022-08-10 Wed 14:28]
On utilise la dernière version de kent, donc plus de problème.
PRête à être mergé. Rebase faite<2023-07-02 Sun>
**** DONE Version de kent déjà packagée : forcer version  335
CLOSED: [2023-07-02 Sun 11:20]
***** KILL [[https://github.com/NixOS/nixpkgs/pull/206991][Restore building kent 404]]
CLOSED: [2023-05-06 Sat 17:40]
Review faite <2023-03-26 Sun> , atteinte merge]
Relancé <2023-05-06 Sat>
Kent 446 n'a pas ce problème donc PR inutile
***** DONE [[https://github.com/NixOS/nixpkgs/pull/223411][Ajouter les header to package]] (inc folder)
CLOSED: [2023-05-08 Mon 10:18] SCHEDULED: <2023-05-07 Sun>
Review à faire
https://github.com/NixOS/nixpkgs/pull/223411
Corrigé et plus besoin de la PR précédente
***** KILL [[https://github.com/NixOS/nixpkgs/pull/186462][BioDBBBigFile]] avec ces 2 changements
CLOSED: [2023-07-02 Sun 11:20]
**** KILL Version de kent déjà packagée : 404
CLOSED: [2023-03-27 Mon 16:43]
Compile mais les tests de passent pas
*** DONE [[https://github.com/NixOS/nixpkgs/pull/186459][BioDBHTS]]
CLOSED: [2023-05-06 Sat 08:49] SCHEDULED: <2023-04-15 Sat>
/Entered on/ [2022-08-10 Wed 14:28]
Correction pour review faites <2022-10-10 Mon>
*** DONE [[https://github.com/NixOS/nixpkgs/pull/186464][BioExtAlign]]
CLOSED: [2022-10-22 Sat 12:43] SCHEDULED: <2022-08-10 Wed>
/Entered on/ [2022-08-10 Wed 14:28]
Review <2022-10-10 Mon>, correction dans la journée.
Correction 2e passe, attente
Impossible de faire marcher les tests Car il ne trouve pas le module Bio::Tools::Align, qui est dans un dossier ailleurs dans le dépôt. Même en compilant tout le dépôt, cela ne fonctionne pas... On skip les tests.
*** TODO VEP
** WAIT [[https://github.com/NixOS/nixpkgs/pull/230394][rtg-tools]] :vcfeval:
Soumis
** TODO Spip
SCHEDULED: <2023-07-11 Tue>
** TODO Happy :happy:
*** TODO PR python 3 upstream
SCHEDULED: <2023-07-12 Wed>
*** TODO nixpkgs en l'état
SCHEDULED: <2023-07-12 Wed>
** TODO Bamsurgeon
/Entered on/ [2023-05-13 Sat 19:11]
*** TODO Velvet
** TODO PR Picard avec option pour gérer la mémoire
Similaire à
https://github.com/bioconda/bioconda-recipes/blob/master/recipes/picard/picard.sh

Deletion in projects.org at line 591 [5.123895]

∅:D[90.63] → [74.398:486]

B:BD[87.857] → [74.398:486]

∅:D[74.486] → [87.914:986]

B:BD[87.914] → [87.914:986]

B:BD[87.986] → [43.315:414]

∅:D[88.118] → [90.104:196]

∅:D[43.414] → [90.104:196]

∅:D[70.1394] → [90.104:196]

B:BD[90.104] → [90.104:196]

*** DONE Changer pneus avant
CLOSED: [2022-09-11 Sun 22:05] SCHEDULED: <2022-09-03 Sat>
Trop abimé pour prendre le risque, on changera les arrières plus tard
*** KILL Changer plaquettes +/- disques
CLOSED: [2023-06-11 Sun 18:39] SCHEDULED: <2023-05-09 Tue>
Écrou grippé ? Voir avec fils M. Chouffe à Gennes, chemin du vernois (voiture 607 bleue)

Deletion in projects.org at line 595 [5.123895]

B:BD[42.1535] → [3.728:815]

∅:D[3.815] → [56.485:565]

B:BD[56.485] → [56.485:565]

B:BD[56.565] → [6.1026:1294]

B:BD[6.1294] → [21.1386:1494]

B:BD[21.1494] → [3.816:898]

∅:D[3.898] → [43.590:680]

∅:D[6.1367] → [43.590:680]

∅:D[21.1545] → [43.590:680]

B:BD[43.590] → [43.590:680]

B:BD[43.680] → [6.1368:1492]

*** DONE Contrôle technique
CLOSED: [2023-07-27 Thu 23:32] DEADLINE: <2023-06-01 Thu>
**** DONE Prendre RV
CLOSED: [2023-05-28 Sun 10:03] SCHEDULED: <2023-05-07 Sun>
**** DONE Changer amortisseurs
CLOSED: [2023-07-02 Sun 10:52] SCHEDULED: <2023-06-13 Tue>
**** DONE Changer phare avant
CLOSED: [2023-07-02 Sun 10:52] SCHEDULED: <2023-06-13 Tue>
**** DONE Contrôle pollution
CLOSED: [2023-07-02 Sun 10:52] SCHEDULED: <2023-06-13 Tue>
**** DONE Prendre rendez-vous pour contre-visite
CLOSED: [2023-07-21 Fri 17:45] SCHEDULED: <2023-07-02 Sun>
**** DONE Contre-visite
CLOSED: [2023-07-27 Thu 23:31] DEADLINE: <2023-07-26 Wed>
*** TODO Amende
**** DONE Changement d'adresse carte grise
CLOSED: [2023-06-11 Sun 21:40]
**** DONE Envoyer photocopie carte grise pour éviter majoration
CLOSED: [2023-07-02 Sun 10:52] SCHEDULED: <2023-06-18 Sun>

Replacement in projects.org at line 600 [5.123895]

B:BD[6.1584] → [23.160:208]

** WAIT Saisie administrative taxe d'habitation

[87.1057]

[23.208]

:PROPERTIES:
:CATEGORY: maison
:END:
** DONE Saisie administrative taxe d'habitation
CLOSED: [2023-07-30 Sun 15:02]

Replacement in projects.org at line 622 [5.123895]
∅:D[23.320] → [11.5260:5277]
∅:D[4.537] → [11.5260:5277]
∅:D[43.864] → [11.5260:5277]
∅:D[9.1453] → [11.5260:5277]
∅:D[6.1584] → [11.5260:5277]
∅:D[64.4652] → [11.5260:5277]
B:BD[91.2423] → [11.5260:5277]
B:BD[9.1510] → [92.56:100]
```
* Gentoo
** GURU
*** DONE Ebuild pour adapteur wifi TBW-108B
```
[4.537]
[92.100]
```
#+category: maison
* Programmation :cs:
** Gentoo :gentoo:
*** GURU :guru:
**** DONE Ebuild pour adapteur wifi TBW-108B
```

Replacement in projects.org at line 629 [5.123895]

B:BD[93.129] → [6.1585:1651]

*** DONE net-wireless/rtl8723bu: migration to linux-mod-r1.eclass

[93.129]

[6.1651]

**** DONE net-wireless/rtl8723bu: migration to linux-mod-r1.eclass

Replacement in projects.org at line 631 [5.123895]
B:BD[6.1710] → [6.1710:1730]
```
*** DONE Ebuild hut
```
[6.1710]
[6.1730]
```
**** DONE Ebuild hut
```
Replacement in projects.org at line 634 [5.123895]
∅:D[93.129] → [32.2317:2349]
∅:D[92.160] → [32.2317:2349]
B:BD[11.5358] → [32.2317:2349]
```
** TODO Article nzbget sur wiki
```
[92.160]
[32.2377]
```
*** TODO Article nzbget sur wiki
```

Replacement in projects.org at line 636 [5.123895]

B:BD[32.2413] → [2.196:254]

** KILL Gentoo package diagrams-graphviz :gentoo:haskell:

[32.2413]

[2.254]

*** KILL Gentoo package diagrams-graphviz :gentoo:haskell:

Replacement in projects.org at line 640 [5.123895]

B:BD[94.478] → [94.478:488]

B:BD[94.488] → [14.33437:33516]

∅:D[14.33516] → [94.536:549]

B:BD[94.536] → [94.536:549]

* Haskell
** KILL Bug report diagrams-grapphviz :haskell:
CLOSED: [2023-07-07 Fri 18:48]
Pas de label

[94.478]

** Learning Haskell :haskell:
*** [#A] [[https://www.reddit.com/r/haskell/comments/npxfba/comment/h084wwa/?utm_source=share&utm_medium=web2x&context=3][Reddit suggestion]]]
**** Learn Foundational building blocks
- [X] [[https://mmhaskell.com/monads/functors][Functor]]
- [X] [[https://mmhaskell.com/monads/applicatives][Applicatives]]
- [X] [[https://mmhaskell.com/monads/tutorial][Monads]]
- [X] [[https://mmhaskell.com/monads/reader-writer][Reader, writer]]
- [X] [[https://mmhaskell.com/monads/state][State]]
- [X] [[https://mmhaskell.com/monads/transformers][Transformers]]
- [ ] [[https://mmhaskell.com/monads/laws][Laws]]
**** Real-world example
***** STRT Look at the example
- [X] Database
- [ ] API
***** Relax for a few days and watch how interactive programs are being composed
***** Get back to the real-world example and make it a complete Cabal project.
***** [[https://mmhaskell.com/testing/test-driven-development][Testing]]
**** [#A] Best resource : [[https://downloads.haskell.org/~ghc/8.10.4/docs/html/users_guide/glasgow_exts.html#language-options][Language Reference]]
whenever you see an unknown language extension or a compilation flag, look it up in Language Reference and try to understand it. You don't have to fully understand them though, just read about them and keep them on your mind. One day they will begin to automatically click into a sound set of concepts.
Language Reference is one of the most underappreciated sources of information (it's almost universally overlooked in language communities - it was the case for Python, and I find it to be true for Haskell as well). You mentioned that you don't like REPL examples, and neither do I. Luckily, the User Guide/Reference has introductory sections for people like us. Once I knew how to compile a single file and to run it, the rest was just a matter of getting to know things by their name in a new ecosystem.
**** DONE Learn to compose things
When you already know how to compile and run single-module interactive console programs, it takes about a day to understand basics of Cabal, and about a week to learn about input parsing and output formatting. Do you need CLI args? Use optparse-applicative. Env vars? Use envy. JSON? Use aeson and a cheatsheet. Don't think about performance and/or API conventions, that's not what you should be concerned of at this point, as you are just learning to compose things together from individual parts.
**** Experiment with various libraires, read haskell planetarium
At this point you have enough knowledge to begin experimenting with various libraries and APIs. Learn how to use Hoogle, and read as much as you can/want on Haskell Planetarium.
*** KILL Learn Haskell for your greater good
   :PROPERTIES:
   :CUSTOM_ID: kill-learn-haskell-for-your-greater-good
   :END:
50%
*** HOLD [[books.org::Haskell%20Programming%20From%20First%20Principles][Haskell programming from first principles]]
*** GHC
**** GHC commentary
Notamment Ollie Charles's 24 days of GHC Extensions,
**** Lire [[https://www.aosabook.org/en/ghc.html]]
*** Vidéos
**** STRT https://www.youtube.com/watch?v=re96UgMk6GQ
*** Articles historiques
1. [[https://watermark.silverchair.com/320098.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAsYwggLCBgkqhkiG9w0BBwagggKzMIICrwIBADCCAqgGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMHXfjdjwhGI2t4bLLAgEQgIICeQjZ-I8gmuaFqBktP4IOifHODtMAHcNF_LwRYyq7NswQ7vT6LJho9P_junCAORLGMV9dgq9JMePH2PFKNxXxrEP1VY7rIDG0gzoeObSkgMDn4MXalrIxD3ejY8vsGYy6vce8Kh70J_UJ8RamO1l3BNNUzy2W6VRaa_cMQr_ekdwcz0oihz0BVKn_bgm_8DjiiPhzj8uU9flVhi13t_oIFA6b3At2QMmPe7Z9OyfLkXivKkmKKNoHwSS7AnTIYAKCO383e4kG6NzZ_elai-XMAJs2Nk0vcgaltld1KeaW3269104DdIlFGevJUVNgwE_4LIheSYRZr9Gr0yRR6TROxdsyxrmgQ22Pzxxpnl8-KdjkW6aRSCKNk_yb5hYcPoRa3ldc5yPV15j8i4t9Mv4U_mBwmIRtMIKPdEHeMvcRx6c8_8uT4RV2esuOPfZlA05bzBgJhMS87M8myxisH-exkTMkm58o6nzHf1lGxzn_JS1VSHbhJCUl82ubzzOWjvl3QJM_vv805XTbn_G-fcRi0d9EQIRTqoObWVFyXW-pz16bWoZPZnBQ1gOmc3hPTGBMZjFR6p9VEAO7bKcK8o0yQDjVWEELNwfAAHc-oF_wLiEjXDNBoUttghgQzzvymKY_jSZhcU8TraVu2i551fpuDNEjSJd0qY5Rg3J6eWU550nJmnoWmX6o7KGiYp0vVMfOoFYXJ1trZWSGoRhDQP2LOLIOt3t2idlj6kV_MoCY3BRnkbxf4XIH7gLJf6Dky6hXFbTU8Fjsn8XHBeKSmaAYJ-sbmGB_BdZO8hHyvHvPv0lTtGcSuKywoJhMbblXRzyuacj_6mZQl5j3tAWhy][Why functional programming matters]]
   Très lisible
2. [[https://dl.acm.org/doi/pdf/10.1145/91556.91592][Comprehending monads]]
   Introduction du concept
3. [[https://dl.acm.org/doi/pdf/10.1145/158511.158524][Imperative functional programming]]
   Application des monads poru résoudre le problème IO

Replacement in projects/bisonex.org at line 1 [95.35]

B:BD[95.35] → [96.29:8221]

#+title: Bisonex
* Biblio
:PROPERTIES:
:CATEGORY: biblio
:END:
** Workflow
Comparaison WDL, Cromwell, nextflow
https://www.nature.com/articles/s41598-021-99288-8
Nextflow = bon compromis ?
Comparison alignement, variant caller (2021)
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04144-1
** Étapes du pipeline
*** Variant calling: Haplotype caller
https://gatk.broadinstitute.org/hc/en-us/articles/360035531412
Définis l'algorithme + image
*** Phred score
https://gatk.broadinstitute.org/hc/en-us/articles/360035531872-Phred-scaled-quality-scores
** VCF
*** GT genotype
encoded as alleles values separated by either of ”/” or “|”, e.g. The allele values are 0 for the reference allele (what is in the reference sequence), 1 for the first allele listed in ALT, 2 for the second allele list in ALT and so on. For diploid calls examples could be 0/1 or 1|0 etc. For haploid calls, e.g. on Y, male X, mitochondrion, only one allele value should be given. All samples must have GT call information; if a call cannot be made for a sample at a given locus, ”.” must be specified for each missing allele in the GT field (for example ./. for a diploid). The meanings of the separators are:
    / : genotype unphased
    | : genotype phased
** Validation
*** NA12878
**** KILL [[https://precision.fda.gov/challenges/truth/results][fdaPrecision challenge]]
Attention, génome et en hg19 donc comparaison non adaptée ...
**** TODO Best practices for the analytical validation of clinical whole-genome sequencing intended for the diagnosis of germline disease
https://www.nature.com/articles/s41525-020-00154-9
Recommandations générale pour genome, sans données brutes
**** TODO [#A] Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2928-9
1. variant calling seul
2. NA12878 + données simulées
3. exome
4. évalué via F-score
Code disponible ! https://github.com/bharani-lab/WES-Benchmarking-Pipeline_Manoj/tree/master/Script
Résultat: BWA/Novoalign_DeepVariant
Aligneurs
- BWA-MEM 0.7.16
- Bowtie2 2.2.6
- Novoalign 3.08.02
- SOAP 2.21
- MOSAIK 2.2.3
Variantcalling
- GATK HaplotypeCaller 4
- FreeBayes 1.1.0
- SAMtools mpileup 1.7
- DeepVariant r0.4
  SNV
| Exome | Pipeline |    TP |   FP |  FN | Sensitivity | Precision | F-Score |   FDR |
|     1 | BWA_GATK | 23689 | 1397 | 613 |       0.975 |     0.944 |   0.959 | 0.057 |
|     2 | BWA_GATK | 23946 |  865 | 356 |       0.985 |     0.965 |   0.975 | 0.036 |
indel
 |   TP | FP | FN | Sensitivity | Precision | F-Score |   FDR |   |
 | 1254 | 72 | 75 |       0.944 |     0.946 |   0.945 | 0.054 |   |
 | 1309 | 10 | 20 |       0.985 |     0.992 |   0.989 | 0.008 |   |
Valeur brutes :
https://static-content.springer.com/esm/art%3A10.1186%2Fs12859-019-2928-9/MediaObjects/12859_2019_2928_MOESM8_ESM.pdf
Autres articles avec même comparaison en exome sur NA12878
- Hwang et al., 2015 studyi
- Highnam et al, 2015
-  Cornish and Guda, 2015
Variant Type
|                       | SNVs & Indels | CNVs (>10Kb) | SVs | Mitochondrial variants | Pseudogenes | REs | Somatic/ mosaic | Literature/Data | Source   |
| NA12878               |         100%a |          40% |   0 |                      0 |           0 |   0 |               0 | Zook et  al18   | NIST     |
| Other NIST standard   |           71% |          40% | 50% |                      0 |           0 |   0 |               0 | Zook  et al18   |          |
| (e.g. AJ/Asian trios) |               |              |     |                        |             |     |                 |                 |          |
| Platinum              |           29% |            0 |   0 |                      0 |           0 |   0 |               0 | Eberle et  al8  | Platinum |
| Genomes               |               |              |     |                        |             |     |                 |                 |          |
| Venter/HuRef          |           14% |          40% |   0 |                      0 |           0 |   0 |               0 | Trost et al1    | HuRef    |
**** Systematic comparison of germline variant calling pipelines cross multiple next-generation sequencers
#+begin_src bibtex
@ARTICLE{Chen2019-fp,
  title     = "Systematic comparison of germline variant calling pipelines
               cross multiple next-generation sequencers",
  author    = "Chen, Jiayun and Li, Xingsong and Zhong, Hongbin and Meng,
               Yuhuan and Du, Hongli",
  abstract  = "The development and innovation of next generation sequencing
               (NGS) and the subsequent analysis tools have gain popularity in
               scientific researches and clinical diagnostic applications.
               Hence, a systematic comparison of the sequencing platforms and
               variant calling pipelines could provide significant guidance to
               NGS-based scientific and clinical genomics. In this study, we
               compared the performance, concordance and operating efficiency
               of 27 combinations of sequencing platforms and variant calling
               pipelines, testing three variant calling pipelines-Genome
               Analysis Tool Kit HaplotypeCaller, Strelka2 and
               Samtools-Varscan2 for nine data sets for the NA12878 genome
               sequenced by different platforms including BGISEQ500,
               MGISEQ2000, HiSeq4000, NovaSeq and HiSeq Xten. For the variants
               calling performance of 12 combinations in WES datasets, all
               combinations displayed good performance in calling SNPs, with
               their F-scores entirely higher than 0.96, and their performance
               in calling INDELs varies from 0.75 to 0.91. And all 15
               combinations in WGS datasets also manifested good performance,
               with F-scores in calling SNPs were entirely higher than 0.975
               and their performance in calling INDELs varies from 0.71 to
               0.93. All of these combinations manifested high concordance in
               variant identification, while the divergence of variants
               identification in WGS datasets were larger than that in WES
               datasets. We also down-sampled the original WES and WGS datasets
               at a series of gradient coverage across multiple platforms, then
               the variants calling period consumed by the three pipelines at
               each coverage were counted, respectively. For the GIAB datasets
               on both BGI and Illumina platforms, Strelka2 manifested its
               ultra-performance in detecting accuracy and processing
               efficiency compared with other two pipelines on each sequencing
               platform, which was recommended in the further promotion and
               application of next generation sequencing technology. The
               results of our researches will provide useful and comprehensive
               guidelines for personal or organizational researchers in
               reliable and consistent variants identification.",
  journal   = "Sci. Rep.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  9,
  number    =  1,
  pages     = "9345",
  month     =  jun,
  year      =  2019,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}
#+end_src
Comparaison de différents pipeline 2019
https://www.nature.com/articles/s41598-019-45835-3
Combinaison
- variant calling = GATK, Strelka2 and Samtools-Varscan2
- sur NA12878
- séquencé sur BGISEQ500, MGISEQ2000, HiSeq4000, NovaSeq and HiSeq Xten.
  Conclusion: strelka2 supérieur mais biais sur NA12878 ?
Illumina > BGI pour indel, probablement car reads plus grand
#+begin_quote
 For WES datasets, the BGI platforms displayed the superior performance in SNPs
 calling while Illumina platforms manifested the better variants calling
 performance

[95.35]

[96.8221]

#+title: Bisonex
#+category: bisonex
* Biblio
:PROPERTIES:
:CATEGORY: biblio
:END:
** Workflow
Comparaison WDL, Cromwell, nextflow
https://www.nature.com/articles/s41598-021-99288-8
Nextflow = bon compromis ?
Comparison alignement, variant caller (2021)
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04144-1
** Étapes du pipeline
*** Variant calling: Haplotype caller
https://gatk.broadinstitute.org/hc/en-us/articles/360035531412
Définis l'algorithme + image
*** Phred score
https://gatk.broadinstitute.org/hc/en-us/articles/360035531872-Phred-scaled-quality-scores
** VCF
*** GT genotype
encoded as alleles values separated by either of ”/” or “|”, e.g. The allele values are 0 for the reference allele (what is in the reference sequence), 1 for the first allele listed in ALT, 2 for the second allele list in ALT and so on. For diploid calls examples could be 0/1 or 1|0 etc. For haploid calls, e.g. on Y, male X, mitochondrion, only one allele value should be given. All samples must have GT call information; if a call cannot be made for a sample at a given locus, ”.” must be specified for each missing allele in the GT field (for example ./. for a diploid). The meanings of the separators are:
    / : genotype unphased
    | : genotype phased
** Validation
*** NA12878
**** KILL [[https://precision.fda.gov/challegnges/truth/results][fdaPrecision challenge]]
Attention, génome et en hg19 donc comparaison non adaptée ...
**** TODO Best practices for the analytical validation of clinical whole-genome sequencing intended for the diagnosis of germline disease
https://www.nature.com/articles/s41525-020-00154-9
Recommandations générale pour genome, sans données brutes
**** TODO [#A] Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2928-9
1. variant calling seul
2. NA12878 + données simulées
3. exome
4. évalué via F-score
Code disponible ! https://github.com/bharani-lab/WES-Benchmarking-Pipeline_Manoj/tree/master/Script
Résultat: BWA/Novoalign_DeepVariant
Aligneurs
- BWA-MEM 0.7.16
- Bowtie2 2.2.6
- Novoalign 3.08.02
- SOAP 2.21
- MOSAIK 2.2.3
Variantcalling
- GATK HaplotypeCaller 4
- FreeBayes 1.1.0
- SAMtools mpileup 1.7
- DeepVariant r0.4
  SNV
| Exome | Pipeline |    TP |   FP |  FN | Sensitivity | Precision | F-Score |   FDR |
|     1 | BWA_GATK | 2368g9 | 1397 | 613 |       0.975 |     0.944 |   0.959 | 0.057 |
|     2 | BWA_GATK | 23946 |  865 | 356 |       0.985 |     0.965 |   0.975 | 0.036 |
indel
 |   TP | FP | FN | Sensitivity | Precision | F-Score |   FDR |   |
 | 1254 | 72 | 75 |       0.944 |     0.946 |   0.945 | 0.054 |   |
 | 1309 | 10 | 20 |       0.985 |     0.992 |   0.989 | 0.008 |   |
Valeur brutes :
https://static-content.springer.com/esm/art%3A10.1186%2Fs12859-019-2928-9/MediaObjects/12859_2019_2928_MOESM8_ESM.pdf
Autres articles avec même comparaison en exome sur NA12878
- Hwang et al., 2015 studyi
- Highnam et al, 2015
-  Cornish and Guda, 2015
Variant Type
|                       | SNVs & Indels | CNVs (>10Kb) | SVs | Mitochondrial variants | Pseudogenes | REs | Somatic/ mosaic | Literature/Data | Source   |
| NA12878               |         100%a |          40% |   0 |                      0 |           0 |   0 |               0 | Zook et  al18   | NIST     |
| Other NIST standard   |           71% |          40% | 50% |                      0 |           0 |   0 |               0 | Zook  et al18   |          |
| (e.g. AJ/Asian trios) |               |              |     |                        |             |     |                 |                 |          |
| Platinum              |           29% |            0 |   0 |                      0 |           0 |   0 |               0 | Eberle et  al8  | Platinum |
| Genomes               |               |              |     |                        |             |     |                 |                 |          |
| Venter/HuRef          |           14% |          40% |   0 |                      0 |           0 |   0 |               0 | Trost et al1    | HuRef    |
**** Systematic comparison of germline variant calling pipelines cross multiple next-generation sequencers
#+begin_src bibtex
@ARTICLE{Chen2019-fp,
  title     = "Systematic comparison of germline variant calling pipelines
               cross multiple next-generation sequencers",
  author    = "Chen, Jiayun and Li, Xingsong and Zhong, Hongbin and Meng,
               Yuhuan and Du, Hongli",
  abstract  = "The development and innovation of next generation sequencing
               (NGS) and the subsequent analysis tools have gain popularity in
               scientific researches and clinical diagnostic applications.
               Hence, a systematic comparison of the sequencing platforms and
               variant calling pipelines could provide significant guidance to
               NGS-based scientific and clinical genomics. In this study, we
               compared the performance, concordance and operating efficiency
               of 27 combinations of sequencing platforms and variant calling
               pipelines, testing three variant calling pipelines-Genome
               Analysis Tool Kit HaplotypeCaller, Strelka2 and
               Samtools-Varscan2 for nine data sets for the NA12878 genome
               sequenced by different platforms including BGISEQ500,
               MGISEQ2000, HiSeq4000, NovaSeq and HiSeq Xten. For the variants
               calling performance of 12 combinations in WES datasets, all
               combinations displayed good performance in calling SNPs, with
               their F-scores entirely higher than 0.96, and their performance
               in calling INDELs varies from 0.75 to 0.91. And all 15
               combinations in WGS datasets also manifested good performance,
               with F-scores in calling SNPs were entirely higher than 0.975
               and their performance in calling INDELs varies from 0.71 to
               0.93. All of these combinations manifested high concordance in
               variant identification, while the divergence of variants
               identification in WGS datasets were larger than that in WES
               datasets. We also down-sampled the original WES and WGS datasets
               at a series of gradient coverage across multiple platforms, then
               the variants calling period consumed by the three pipelines at
               each coverage were counted, respectively. For the GIAB datasets
               on both BGI and Illumina platforms, Strelka2 manifested its
               ultra-performance in detecting accuracy and processing
               efficiency compared with other two pipelines on each sequencing
               platform, which was recommended in the further promotion and
               application of next generation sequencing technology. The
               results of our researches will provide useful and comprehensive
               guidelines for personal or organizational researchers in
               reliable and consistent variants identification.",
  journal   = "Sci. Rep.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  9,
  number    =  1,
  pages     = "9345",
  month     =  jun,
  year      =  2019,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}
#+end_src
Comparaison de différents pipeline 2019
https://www.nature.com/articles/s41598-019-45835-3
Combinaison
- variant calling = GATK, Strelka2 and Samtools-Varscan2
- sur NA12878
- séquencé sur BGISEQ500, MGISEQ2000, HiSeq4000, NovaSeq and HiSeq Xten.
  Conclusion: strelka2 supérieur mais biais sur NA12878 ?
Illumina > BGI pour indel, probablement car reads plus grand
#+begin_quote
 For WES datasets, the BGI platforms displayed the superior performance in SNPs
 calling while Illumina platforms manifested the better variants calling
 performance

Replacement in projects/bisonex.org at line 3 [95.35]

B:BD[96.16413] → [96.16413:32797]

séquences alternatives (utilisables)
- fix = patch (correction ou amélioration)
- random = séquence connue sur un chromosome mais non encore utilisée
** Pipelines prêt-à-l’emploi nextflow
Problème : nécessite singularity ou docker (ou conda)
Potentiellement utilisable avec nix...
** Validation : Quelles données de référence ?
Discussion avec Alexis
- Platinum genomes = génome seul
*** [[https://github.com/genome-in-a-bottle/giab_data_indexes][Genome in a bottle]]
  - NA12878 :
    - Illumina HiSeq Exome : fastq + capture en hg37
    - Illumina TruSeq Exome : bam, pas de capture
    - Exomes en hg37 https://zenodo.org/record/3597727 avec capture
      - HiSeq2000
      - NextSeq 500
      - HiSeq 2500
  - HG002,3,4
    - Illumina Whole Exome  : bam. le kit de capture est "Agilent SureSelect Human All Exon V5 kit" selon [[https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015/README.txt][README]]. On il faut les régions [[https://kb.10xgenomics.com/hc/en-us/articles/115004150923-Where-can-I-find-the-Agilent-Target-BED-files-][selon ce site]]
      Un autre fichier est disponible (capture ???)
    https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015/wex_Agilent_SureSelect_v05_b37.baits.slop50.merged.list
  "target region" +/- 50bp
    testé sur chr311780-312086 : ok
Autres technologies non adaptées au pipeline (vu avec Alexis)
*** [[https://www.illumina.com/platinumgenomes.html][Platinum genome
]] Que du génome « sequenced to 50x depth on a HiSeq 2000 system”
Genome possible
*** 1000 genomes
- intersection des capture + CCDS  [[id:b77e64fa-06a8-4ffa-8b5b-ab3fda684b61][Données brutes exome 1000 Genomes (fastq + capture)]]
- Broad instute : SureSelect human all exon v2 target capture kit : non disponible sur le site d'agilent (V6 ou plus)
*** Zone de capture
GIAB fourni le .bed pour l'exome . INfo : https://support.illumina.com/sequencing/sequencing_kits/nextera-rapid-capture-exome-kit/downloads.html
*** Valider la méthode
- 1000 genomes + SureSelect human all exon v2 target capture kit : non disponible sur le site d'agilent (V6 ou plus)
  https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2928-9
- GIAB + liftover du fichire de capture en hg38
Ce qui est aussi fait par
https://bcbio-nextgen.readthedocs.io/en/stable/contents/germline_variants.html
Mais avec UCSC liftover
** Centogène
https://www.twistbioscience.com/node/23906
Bed non fourni pour exactement cette capture
On prend https://www.twistbioscience.com/resources/data-files/twist-alliance-vcgs-exome-401mb-bed-files
qui content la majeure partie
* Données
:PROPERTIES:
:CATEGORY: data
:END:
** DONE Remplacer bam par fastq sur mesocentre
CLOSED: [2023-04-16 Sun 16:33]
Commande
*** DONE Supprimer les fastq non "paired"
CLOSED: [2023-04-16 Sun 16:33]
nushell
Liste des fastq avec "paired-end" manquant
#+begin_src nu
ls **/*.fastq.gz | get name | path basename | split column "_" | get column1 | uniq -u | save single.txt
#+end_src
#+RESULTS:
: 62907927
: 62907970
: 62899606
: 62911287
: 62913201
: 62914084
: 62915905
: 62921595
: 62923065
: 62925220
: 62926503
: 62926502
: 62926500
: 62926499
: 62926498
: 62931719
: 62943423
: 62943400
: 62948290
: 62949205
: 62949206
: 62949118
: 62951284
: 62960792
: 62960785
: 62960787
: 62960617
: 62962561
: 62962692
: 62967473
: 62972194
: 62979102
On vérifie
#+begin_src nu
open single.txt  | lines | each {|e| ls $"fastq/*_($in)/*" | get 0  }
open single.txt  | lines | each {|e| ls $"fastq/*_($in)/*" | get 0.name }  | path basename | split column "_" | get column1 | uniq -c
#+end_src
On met tous dans un dossier (pas de suppression )
#+begin_src
open single.txt  | lines | each {|e| ls $"fastq/*_($in)/*" | get 0  }  | each {|e| ^mv $e.name bad-fastq/}
#+end_src
On vérifie que les dossiier sont videsj
 open single.txt  | lines | each {|e| ls $"fastq/*_($in)" | get 0.name } | ^ls -l $in
 Puis on supprime
 open single.txt  | lines | each {|e| ls $"fastq/*_($in)" | get 0.name } | ^rm -r $in
*** DONE Supprimer bam qui ont des fastq
CLOSED: [2023-04-16 Sun 16:33]
On liste les identifiants des fastq et bam dans un tableau avec leur type :
#+begin_src
let fastq = (ls fastq/*/*.fastq.gz | get name | parse "{dir}/{full_id}/{id}_{R}_001.fastq.gz"  | select dir id | uniq )
let bam = (ls bam/*/*.bam | get name | parse "{dir}/{full_id}/{id}_{S}.bqrt.bam"  | select dir id)
#+end_src
On groupe les résultat par identifiant (résultats = liste de records qui doit être convertie en table)
et on trie ceux qui n'ont qu'un fastq ou un bam
#+begin_src
let single = ( $bam | append $fastq | group-by id | transpose id files | get files | where {|x| ($x | length) == 1})
#+end_src
On convertit en table et on récupère seulement les bam
#+begin_src
$single | reduce {|it, acc| $acc | append $it} | where dir == bam | get id | each {|e| ^ls $"bam/*_($e)/*.bam"}
#+end_src
#+RESULTS:
: bam/2100656174_62913201/62913201_S52.bqrt.bam
: bam/2100733271_62925220/62925220_S33.bqrt.bam
: bam/2100738763_62926502/62926502_S108.bqrt.bam
: bam/2100746726_62926498/62926498_S105.bqrt.bam
: bam/2100787936_62931955/62931955_S4.bqrt.bam
: bam/2200066374_62948290/62948290_S130.bqrt.bam
: bam/2200074722_62948298/62948298_S131.bqrt.bam
: bam/2200074990_62948306/62948306_S218.bqrt.bam
: bam/2200214581_62967331/62967331_S267.bqrt.bam
: bam/2200225399_62972187/62972187_S85.bqrt.bam
: bam/2200293962_62979117/62979117_S63.bqrt.bam
: bam/2200423985_62999352/62999352_S1.bqrt.bam
: bam/2200495073_63010427/63010427_S20.bqrt.bam
: bam/2200511274_63012586/63012586_S114.bqrt.bam
: bam/2200669188_63036688/63036688_S150.bqrt.bam
* Nouveau workflow
:PROPERTIES:
:CATEGORY: workflow
:END:
** TODO Bases de données
*** KILL Nix pour télécharger les données brutes
**** Conclusion
Non viable sur cluster car en dehors de /nix/store
On peut utiliser des symlink mais trop compliqué
**** KILL Axel au lieu de curl pour gérer les timeout?
CLOSED: [2022-08-19 Fri 15:18]
*** DONE Tester patch de @pennae pour gros fichiers
SCHEDULED: <2022-08-19 Fri>
*** KILL Télécharger les données avec nextflow: hg38
CLOSED: [2023-06-12 Mon 23:29]
**** DONE Genome de référence
**** DONE dbSNP
**** DONE VEP 20G
CLOSED: [2023-06-12 Mon 23:29]
Ajout vérification checksum -> à vérifier
**** DONE transcriptome (spip)
CLOSED: [2023-06-12 Mon 23:29]
Rajouter checksum manuel
**** KILL Refseq
**** KILL OMIM
CLOSED: [2023-06-12 Mon 23:29]
codé, à vérifier
**** KILL ACMG incidental
CLOSED: [2023-06-12 Mon 23:29]
*** TODO Données :T2T:
:PROPERTIES:
:ID:       5d915178-ca96-44ef-87f1-6702af114f2b
:END:
**** DONE fasta
CLOSED: [2023-06-12 Mon 23:30]
***** DONE compatibilité hg38
CLOSED: [2023-06-12 Mon 23:30]
**** DONE fasta index
CLOSED: [2023-06-13 Tue 00:07]
***** DONE compatibilité hg38
CLOSED: [2023-06-13 Tue 00:07]
**** DONE fasta dictionnaire
CLOSED: [2023-06-13 Tue 00:07]
**** DONE dbSNP
CLOSED: [2023-06-12 Mon 23:30]
***** DONE compatibilité hg38
CLOSED: [2023-06-12 Mon 23:30]
**** DONE commonSNP
CLOSED: [2023-06-14 Wed 22:32]
***** DONE compatibilité hg38
CLOSED: [2023-06-14 Wed 22:32]
cd /Work/Groups/bisonex/data/dbsnp/GRCh38.p14
❯ ga@mesointeractive GRCh38.p14]$ zgrep -c '^NC' dbSNP_common.vcf.gz
21340485
[apraga@mesointeractive GRCh38.p14]$ pwd
[apraga@mesointeractive GRCh38.p14]$ zgrep -c '^NC'
dbSNP_common.vcf.gz                     ID_of_common_snp_not_clinvar_patho.txt
dbSNP_common.vcf.gz.tbi                 ID_of_common_snp.txt
[apraga@mesointeractive dbsnp]$ cd chm13v2.0/
[apraga@mesointeractive chm13v2.0]$ ls
chm13v2.0_dbSNPv155.vcf.gz      dbSNP_common.vcf.gz.tbi                 versions.yml
chm13v2.0_dbSNPv155.vcf.gz.tbi  ID_of_common_snp_not_clinvar_patho.txt
dbSNP_common.vcf.gz             ID_of_common_snp.txt
[apraga@mesointeractive chm13v2.0]$ zgrep -c '^chr' dbSNP_common.vcf.gz
19433713
[apraga@mesointeractive chm13v2.0] $
❯ man tmux
**** DONE commonSNP non patho
CLOSED: [2023-06-14 Wed 22:35]
***** DONE compatibilité hg38
CLOSED: [2023-06-14 Wed 22:35]
**** TODO cache vep
SCHEDULED: <2023-07-25 Tue>
*** HOLD Processing bases de données
**** DONE dbSNP common
**** DONE Seulement les ID dans dbSNP common !
CLOSED: [2022-11-19 Sat 21:42]
172G au lieu de 253M...
**** HOLD common dbSNP not clinvar patho
***** DONE Conclusion partielle
CLOSED: [2022-12-12 Mon 22:25]
- vcfeval : prometteur mais n'arrive pas à traiter toutes les régions
- isec : trop de problèmes avec
- classif clinvar directement dans dbSNP: le plus simple
  Et ça permet de rattraper quelques erreurs dans le script d'Alexis
***** KILL Utiliser directement le numéro dbSNP dans clinvar ? Non
CLOSED: [2022-11-20 Sun 19:51]
Ex: chr20
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools query -f 'rs%INFO/RS \n' -i 'INFO/RS != "." & INFO/CLNSIG="Pathogenic"' clinvar_chr20.vcf.gz | sort > ID_clinvar_patho.txt
bcftools query -f '%ID\n' dbSNP_common_chr20.vcf.gz | sort > ID_of_common_snp.txt
comm -23 ID_of_common_snp.txt ID_clinvar_patho.txt > ID_of_common_snp_not_clinvar_patho.txt
wc -l ID_of_common_snp_not_clinvar_patho.txt
# sort ID
#+end_src
#+RESULTS:
: 518846 ID_of_common_snp_not_clinvar_patho.txt
Version d'alexis
#+begin_src sh :dir ~/code/bisonex/test_isec
snp=dbSNP_common_chr20.vcf.gz
clinvar=clinvar_chr20_notremapped.vcf.gz
python ../script/pythonScript/clinvar_sbSNP.py \
    --clinvar $clinvar \
    --chrm_name_table ../database/RefSeq/refseq_to_number_only_consensual.txt \
    --dbSNP $snp --output prod.txt
wc -l prod.txt
zgrep '^NC' dbSNP_common_chr20.vcf.gz | wc -l
#+end_src
#+RESULTS:
| 518832 | prod.txt |
| 518846 |          |
***** KILL classification clinvar codée dbSNP ?
CLOSED: [2022-12-04 Sun 14:38]
Sur le chromosome 20
*Attention* CLNSIG a plusieurs champs (séparé par une virgule)
On y accède avec INFO/CLNSIG[*]
Ensuite, chaque item peut avoir plusieurs haploïdie (séparé par un |). IL faut donc utiliser une regexp
NB: *ne pas mettre la condition* dans une variable !!
Pour avoir les clinvar patho, on veut 5 mais pas 255 (= autre) pour la classification !`
Il faut également les likely patho et conflicting
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools query -f '%INFO/CLNSIG\n' dbSNP_common_chr20.vcf.gz -i \
'INFO/CLNSIG[*]~"^5|" | INFO/CLNSIG[*]=="5" | INFO/CLNSIG[*]~"|5" | INFO/CLNSIG[*]~"^4|" | INFO/CLNSIG[*]=="4" | INFO/CLNSIG[*]~"|4" | INFO/CLNSIG[*]~"^12|" | INFO/CLNSIG[*]=="12" | INFO/CLNSIG[*]~"|12"' | sort
#+end_src
#+RESULTS:
| . |  . | 12 |    |   |   |   |   |   |   |   |
| . | 12 |  0 |  2 |   |   |   |   |   |   |   |
| 2 |  3 |  2 |  2 | 2 | 5 | . |   |   |   |   |
| . |  2 |  3 |  2 | 2 | 4 |   |   |   |   |   |
| . |  . |  3 | 12 | 3 |   |   |   |   |   |   |
| . |  5 |  2 |  . |   |   |   |   |   |   |   |
| . |  . |  . |  5 | 2 | 2 |   |   |   |   |   |
| . |  9 |  9 |  9 | 5 | 5 | 2 | 3 | 2 | 3 | 2 |
Si on les exclut :
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools query -f '%ID\n' dbSNP_common_chr20.vcf.gz -e \
'INFO/CLNSIG[*]~"^5|" | INFO/CLNSIG[*]=="5" | INFO/CLNSIG[*]~"|5" | INFO/CLNSIG[*]~"4" | INFO/CLNSIG[*]~"12"' | sort | uniq > common-notpatho.txt
#+end_src
#+RESULTS:
 #+begin_src sh :dir ~/code/bisonex/test_isec
snp=dbSNP_common_chr20.vcf.gz
clinvar=clinvar_chr20_notremapped.vcf.gz
python ../script/pythonScript/clinvar_sbSNP.py \
    --clinvar $clinvar \
    --chrm_name_table ../database/RefSeq/refseq_to_number_only_consensual.txt \
    --dbSNP $snp --output tmp.txt
sort tmp.txt | uniq > common-notpatho-alexis.txt
wc -l common-notpatho-alexis.txt
 #+end_src
 #+RESULTS:
 : 518832 common-notpatho-alexis.txt
On en a 6 de plus que la version d'Alexis mais quelques différences
Ceux d'Alexis qui manquent:
#+begin_src sh :dir ~/code/bisonex/test_isec
comm -23 common-notpatho-alexis.txt common-notpatho.txt > alexis-only.txt
cat alexis-only.txt
#+end_src
#+RESULTS:
| rs1064039  |
| rs3833341  |
| rs73598374 |
On les teste dans clinvar et dbSNP
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'ID=@alexis-only.txt' dbSNP_common_chr20.vcf.gz
bcftools query -f '%POS\n' -i 'ID=@alexis-only.txt' dbSNP_common_chr20.vcf.gz > alexis-only-pos.txt
while read  -r line; do
bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'POS='$line clinvar_chr20.vcf.gz
done < alexis-only-pos.txt
# bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'POS=23637790' clinvar_chr20.vcf.gz
#+end_src
#+RESULTS:
|   764018 | A | ACAGGTCAAT,ACAGGT | .,5     | 2,. |   |
| 23637790 | C | G,T               | .,.,12  |     |   |
| 44651586 | C | A,G,T             | .,.,.,5 |   2 | 2 |
|   764018 | A | ACAGGTCAAT        | Benign  |     |   |
| 23637790 | C | T                 | Benign  |     |   |
| 44651586 | C | T                 | Benign  |     |   |
On a donc une discordance entre clinvar et dbSNP.
On dirait qu'ils ont mal fait l'intersection avec clinvar.
Par exemple https://www.ncbi.nlm.nih.gov/snp/rs3833341#clinical_significance
Tu as l'impression qu'il y a un 1 clinvar bénin et 1 patho.
En cherchant par NM, tu vois qu'il est bénin sur clinvar car il y a d'autres soumissions ! https://www.ncbi.nlm.nih.gov/clinvar/variation/262235/
Confirmation sur nos bases de données :
$ bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'POS=764018' dbSNP_common_chr20.vcf.gz
764018 A ACAGGTCAAT,ACAGGT .,5|2,.
$ bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'POS=764018' clinvar_chr20.vcf.gz
764018 A ACAGGTCAAT Benign
***** KILL Corriger script alexi
CLOSED: [2022-12-04 Sun 13:03]
Gère clinvar patho, probablement patho ou conflicting !
***** HOLD Rtg tools
****** Test
1. Générer SDf file
   #+begin_src sh
rtg format genomeRef.fna  -o genomeRef.sdf
   #+end_src
2. Pour les bases de donnés, il faut l'option --sample ALT sinon on a
 #+begin_src
$ rtg vcfeval -b dbSNP_common.vcf.gz -c clinvar.vcf.gz -o test -t genomeRef.sdf/^C
VCF header does not contain a FORMAT field named GQ
Error: Record did not contain enough samples: NC_000001.11	10001	rs1570391677	A,C	.	PASS	RS=1570391677;dbSNPBuildID=154;SSR=0;PSEUDOGENEINFO=DDX11L1:100287102;VC=SNV;R5;GNO;FREQ=KOREAN:0.9891,0.0109,.|SGDP_PRJ:0,1,.|dbGaP_PopFreq:1,.,0;COMMON
 #+end_src
 Essai intersection clinvar (patho ou non) dbSNP
   - faux négatif = dbSNP common qui ne sont pas dans clinvar
   - faux positif = clinvar qui ne sont pas dbSNP common
   - vrai positif = clinvar qui sont dans dbSNP common
   - vrai positif baseline = dbSNP common qui sont dans clinvar
 On calcule le nombre de lignes
 #+begin_src ssh
zgrep '^[^#]' /Work/Groups/bisonex/data/clinvar/GRCh38/clinvar.vcf.gz | wc -l
for i in *.vcf.gz; do echo $i; zgrep '^[^#]' $i | wc -l; done
 #+end_src
 | clinvar            |  1493470 |
 | fn.vcf.gz          | 22330220 |
 | fp.vcf.gz          |  1222529 |
 | tp-baseline.vcf.gz |   131040 |
 | tp.vcf.gz          |   136638 |
À noter qu'on ne retrouve pas tout clinvar...
1222529 + 131040 = 1353569 < 1493470
certains régions ne sont pas traitées :
#+begin_quote
Evaluation too complex (50002 unresolved paths, 34891 iterations) at reference region NC_000001.11:790930-790970. Variants in this region will not be included in results
#+end_quote
#+begin_src sh
grep 'not be included' vcfeval.log | wc -l
56192
#+end_src
Le total est quand même inférieur
On veut les clinvar non patho dans dbSNP soit les faux négatif (dbSNP common not contenu dans clinvar patho)
#+begin_src sh
bcftools filter -i 'INFO/CLNSIG="Pathogenic"' /Work/Groups/bisonex/data/clinvar/GRCh38/clinvar.vcf.gz -o /Work/Groups/bisonex/data/clinvar/GRCh38/clinvar-patho.vcf.gz
tabix /Work/Groups/bisonex/data/clinvar/GRCh38/clinvar-patho.vcf.gz
#+end_src
On lance le script (dbSNP common et clinvar = 9h)
#+begin_src sh
#!/bin/bash
#SBATCH --nodes=1
#SBATCH -p smp
#SBATCH --time=12:00:00
#SBATCH --mem=12G
dir=/Work/Groups/bisonex/data
dbSNP=$dir/dbSNP/GRCh38.p13/dbSNP_common.

[96.16413]

[96.32797]

séquences alternatives (utilisables)
- fix = patch (correction ou amélioration)
- random = séquence connue sur un chromosome mais non encore utilisée
** Pipelines prêt-à-l’emploi nextflow
Problème : nécessite singularity ou docker (ou conda)
Potentiellement utilisable avec nix...
** Validation : Quelles données de référence ?
Discussion avec Alexis
- Platinum genomes = génome seul
*** [[https://github.com/genome-in-a-bottle/giab_data_indexes][Genome in a bottle]]
  - NA12878 :
    - Illumina HiSeq Exome : fastq + capture en hg37
    - Illumina TruSeq Exome : bam, pas de capture
    - Exomes en hg37 https://zenodo.org/record/3597727 avec capture
      - HiSeq2000
      - NextSeq 500
      - HiSeq 2500
  - HG002,3,4
    - Illumina Whole Exome  : bam. le kit de capture est "Agilent SureSelect Human All Exon V5 kit" selon [[https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015/README.txt][README]]. On il faut les régions [[https://kb.10xgenomics.com/hc/en-us/articles/115004150923-Where-can-I-find-the-Agilent-Target-BED-files-][selon ce site]]
      Un autre fichier est disponible (capture ???)
    https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015/wex_Agilent_SureSelect_v05_b37.baits.slop50.merged.list
  "target region" +/- 50bp
    testé sur chr311780-312086 : ok
Autres technologies non adaptées au pipeline (vu avec Alexis)
*** [[https://www.illumina.com/platinumgenomes.html][Platinum genome
]] Que du génome « sequenced to 50x depth on a HiSeq 2000 system”
Genome possible
*** 1000 genomes
- intersection des capture + CCDS  [[id:b77e64fa-06a8-4ffa-8b5b-ab3fda684b61][Données brutes exome 1000 Genomes (fastq + capture)]]
- Broad instute : SureSelect human all exon v2 target capture kit : non disponible sur le site d'agilent (V6 ou plus)
*** Zone de capture
GIAB fourni le .bed pour l'exome . INfo : https://support.illumina.com/sequencing/sequencing_kits/nextera-rapid-capture-exome-kit/downloads.html
*** Valider la méthode
- 1000 genomes + SureSelect human all exon v2 target capture kit : non disponible sur le site d'agilent (V6 ou plus)
  https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2928-9
- GIAB + liftover du fichire de capture en hg38
Ce qui est aussi fait par
https://bcbio-nextgen.readthedocs.io/en/stable/contents/germline_variants.html
Mais avec UCSC liftover
** Centogène
https://www.twistbioscience.com/node/23906
Bed non fourni pour exactement cette capture
On prend https://www.twistbioscience.com/resources/data-files/twist-alliance-vcgs-exome-401mb-bed-files
qui content la majeure partie
* Nixpkgs :nix:
** DONE GATK
CLOSED: [2023-05-06 Sat 08:51]
*** DONE [[https://github.com/NixOS/nixpkgs/pull/185819][Binaire]]
CLOSED: [2022-09-10 Sat 23:53] SCHEDULED: <2022-08-10 Wed>
/Entered on/ [2022-08-09 Tue 10:57]
PR submitted
*** KILL Corriger code pour utiliser source
CLOSED: [2022-09-11 Sun 22:05]
*** DONE Corriger PATH pour include java et python
CLOSED: [2022-10-11 Tue 11:46]
https://github.com/NixOS/nixpkgs/pull/191548
Review <2022-10-10 Mon> , corrigé dans la journée
*** DONE Update 4.3.0.0
CLOSED: [2023-04-13 Thu 09:01]
** TODO Nextflow
*** KILL version script seule
CLOSED: [2023-04-01 Sat 18:29]
Fix pour SGE et nextflow
https://github.com/NixOS/nixpkgs/issues/192396
*** KILL Version avec gradle
CLOSED: [2022-10-09 Sun 22:51]
*** TODO [[https://github.com/NixOS/nixpkgs/issues/192396][Bug report Version 22.10.6]]
**** Notes
Erreur :
ERROR: Cannot download nextflow required file -- make sure you can connect to the internet
Alternatively you can try to download this file:
    https://www.nextflow.io/releases/v22.10.6/nextflow-22.10.6-all.jar
and save it as:
    .//nix/store/md2b1ah4d7ivj82k8xxap30dmdci00pa-nextflow-22.10.6/bin/.nextflow-wrapped
Dans la mise à jour, il y a la création d'un environnement virtuel qui casse l'exécution de nextflow (besoin de télécharger)
Fix = désactiver
**** KILL Patch NXF_OFFLINE=true
CLOSED: [2023-07-02 Sun 11:02] SCHEDULED: <2023-06-11 Sun>
** TODO Multiqc
** TODO VEP :vep:
*** DONE [[https://github.com/NixOS/nixpkgs/pull/185691][BioPerl]]
SCHEDULED: <2022-08-10 Wed>
/Entered on/ [2022-08-09 Tue 10:57]
PR submitted
*** TODO BioDBBBigFile
:PROPERTIES:
:ORDERED:  t
:END:
/Entered on/ [2022-08-10 Wed 14:28]
On utilise la dernière version de kent, donc plus de problème.
PRête à être mergé. Rebase faite<2023-07-02 Sun>
**** DONE Version de kent déjà packagée : forcer version  335
CLOSED: [2023-07-02 Sun 11:20]
***** KILL [[https://github.com/NixOS/nixpkgs/pull/206991][Restore building kent 404]]
CLOSED: [2023-05-06 Sat 17:40]
Review faite <2023-03-26 Sun> , atteinte merge]
Relancé <2023-05-06 Sat>
Kent 446 n'a pas ce problème donc PR inutile
***** DONE [[https://github.com/NixOS/nixpkgs/pull/223411][Ajouter les header to package]] (inc folder)
CLOSED: [2023-05-08 Mon 10:18] SCHEDULED: <2023-05-07 Sun>
Review à faire
https://github.com/NixOS/nixpkgs/pull/223411
Corrigé et plus besoin de la PR précédente
***** KILL [[https://github.com/NixOS/nixpkgs/pull/186462][BioDBBBigFile]] avec ces 2 changements
CLOSED: [2023-07-02 Sun 11:20]
**** KILL Version de kent déjà packagée : 404
CLOSED: [2023-03-27 Mon 16:43]
Compile mais les tests de passent pas
**** TODO Modifier selon PR https://github.com/NixOS/nixpkgs/pull/186462
SCHEDULED: <2023-07-30 Sun>
*** DONE [[https://github.com/NixOS/nixpkgs/pull/186459][BioDBHTS]]
CLOSED: [2023-05-06 Sat 08:49] SCHEDULED: <2023-04-15 Sat>
/Entered on/ [2022-08-10 Wed 14:28]
Correction pour review faites <2022-10-10 Mon>
*** DONE [[https://github.com/NixOS/nixpkgs/pull/186464][BioExtAlign]]
CLOSED: [2022-10-22 Sat 12:43] SCHEDULED: <2022-08-10 Wed>
/Entered on/ [2022-08-10 Wed 14:28]
Review <2022-10-10 Mon>, correction dans la journée.
Correction 2e passe, attente
Impossible de faire marcher les tests Car il ne trouve pas le module Bio::Tools::Align, qui est dans un dossier ailleurs dans le dépôt. Même en compilant tout le dépôt, cela ne fonctionne pas... On skip les tests.
*** TODO VEP
** WAIT [[https://github.com/NixOS/nixpkgs/pull/230394][rtg-tools]] :vcfeval:
Soumis
** PROJ Spip
** TODO Happy :happy:
*** PROJ PR python 3 upstream
*** PROJ nixpkgs en l'état
** TODO Bamsurgeon
/Entered on/ [2023-05-13 Sat 19:11]
*** TODO Velvet
** TODO PR Picard avec option pour gérer la mémoire
Similaire à
https://github.com/bioconda/bioconda-recipes/blob/master/recipes/picard/picard.sh
** Julia :julia:
*** KILL XAM.jl: PR pour modification record :julia:
CLOSED: [2023-05-29 Mon 15:40] SCHEDULED: <2023-05-28 Sun>
/Entered on/ [2023-05-27 Sat 22:39]
*** TODO XAMscissors.jl :xamscissors:
Modification de la séquence dans BAM.
*Pas de mise à jour de CIGAR*
On convertit en fastq et on lance le pipeline pour "corriger"
#+begin_src sh
cd /home/alex/code/bisonex/out/63003856/preprocessing/mapped
samtools view 63003856_S135.bam NC_000022.11 -o 63003856_S135_chr22.bam
cd /home/alex/recherche/bisonex/code/BamScissors.jl
cp ~/code/bisonex/out/63003856/preprocessing/mapped/63003856_S135_chr22.bam .
samtools index 63003856_chr22.bam
#+end_src
Le script va modifier le bam, le trier et générer le fastq. !!!
Attention: ne pas oublier l'option -n !!!
#+begin_src sh
time julia --project=.. insertVariant.jl
scp 63003856_S135_chr22_{1,2}.fq.gz meso:/Work/Users/apraga/bisonex/tests/bamscissors/
#+end_src
**** WAIT Implémenter les SNV avec VAF :snv:
Stratégie :
1. calculer la profondeur sur les positions
2. créer un dictionnaire { nom du reads : position dataframe }
3. itérer sur tous les reads et changer ceux marqués
***** DONE VAF = 1
CLOSED: [2023-05-29 Mon 15:34]
***** DONE VAF selon loi normale
CLOSED: [2023-05-29 Mon 15:35]
Tronquée si > 1
***** WAIT Tests unitaires
****** DONE NA12878: 1 gène sur chromosome 22
CLOSED: [2023-05-30 Tue 23:55]
root = "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/NA12878/Garvan_NA12878_HG001_HiSeq_Exome/"
#+begin_src sh
samtools view project.NIST_NIST7035_H7AP8ADXX_NA12878.bwa.markDuplicates.bam  chr22 -o project.NIST_NIST7035_H7AP8ADXX_NA12878_chr22.bam
samtools view project.NIST_NIST7035_H7AP8ADXX_NA12878_chr22.bam chr22:19419700-19424000 -o NIST7035_H7AP8ADXX_NA12878_chr22_MRPL40_hg19.bam
#+end_src
****** WAIT Pull request formatspeciment
https://github.com/BioJulia/FormatSpecimens.jl/pull/8
****** DONE Formatspecimens
CLOSED: [2023-05-29 Mon 23:03]
******* DONE 1 read
CLOSED: [2023-05-29 Mon 23:02]
******* DONE VAF sur 1 exon
CLOSED: [2023-05-29 Mon 23:03]
**** TODO Implémenter les indel avec VAF :indel:
**** TODO Soumission paquet
* Données
:PROPERTIES:
:CATEGORY: data
:END:
** DONE Remplacer bam par fastq sur mesocentre
CLOSED: [2023-04-16 Sun 16:33]
Commande
*** DONE Supprimer les fastq non "paired"
CLOSED: [2023-04-16 Sun 16:33]
nushell
Liste des fastq avec "paired-end" manquant
#+begin_src nu
ls **/*.fastq.gz | get name | path basename | split column "_" | get column1 | uniq -u | save single.txt
#+end_src
#+RESULTS:
: 62907927
: 62907970
: 62899606
: 62911287
: 62913201
: 62914084
: 62915905
: 62921595
: 62923065
: 62925220
: 62926503
: 62926502
: 62926500
: 62926499
: 62926498
: 62931719
: 62943423
: 62943400
: 62948290
: 62949205
: 62949206
: 62949118
: 62951284
: 62960792
: 62960785
: 62960787
: 62960617
: 62962561
: 62962692
: 62967473
: 62972194
: 62979102
On vérifie
#+begin_src nu
open single.txt  | lines | each {|e| ls $"fastq/*_($in)/*" | get 0  }
open single.txt  | lines | each {|e| ls $"fastq/*_($in)/*" | get 0.name }  | path basename | split column "_" | get column1 | uniq -c
#+end_src
On met tous dans un dossier (pas de suppression )
#+begin_src
open single.txt  | lines | each {|e| ls $"fastq/*_($in)/*" | get 0  }  | each {|e| ^mv $e.name bad-fastq/}
#+end_src
On vérifie que les dossiier sont videsj
 open single.txt  | lines | each {|e| ls $"fastq/*_($in)" | get 0.name } | ^ls -l $in
 Puis on supprime
 open single.txt  | lines | each {|e| ls $"fastq/*_($in)" | get 0.name } | ^rm -r $in
*** DONE Supprimer bam qui ont des fastq
CLOSED: [2023-04-16 Sun 16:33]
On liste les identifiants des fastq et bam dans un tableau avec leur type :
#+begin_src
let fastq = (ls fastq/*/*.fastq.gz | get name | parse "{dir}/{full_id}/{id}_{R}_001.fastq.gz"  | select dir id | uniq )
let bam = (ls bam/*/*.bam | get name | parse "{dir}/{full_id}/{id}_{S}.bqrt.bam"  | select dir id)
#+end_src
On groupe les résultat par identifiant (résultats = liste de records qui doit être convertie en table)
et on trie ceux qui n'ont qu'un fastq ou un bam
#+begin_src
let single = ( $bam | append $fastq | group-by id | transpose id files | get files | where {|x| ($x | length) == 1})
#+end_src
On convertit en table et on récupère seulement les bam
#+begin_src
$single | reduce {|it, acc| $acc | append $it} | where dir == bam | get id | each {|e| ^ls $"bam/*_($e)/*.bam"}
#+end_src
#+RESULTS:
: bam/2100656174_62913201/62913201_S52.bqrt.bam
: bam/2100733271_62925220/62925220_S33.bqrt.bam
: bam/2100738763_62926502/62926502_S108.bqrt.bam
: bam/2100746726_62926498/62926498_S105.bqrt.bam
: bam/2100787936_62931955/62931955_S4.bqrt.bam
: bam/2200066374_62948290/62948290_S130.bqrt.bam
: bam/2200074722_62948298/62948298_S131.bqrt.bam
: bam/2200074990_62948306/62948306_S218.bqrt.bam
: bam/2200214581_62967331/62967331_S267.bqrt.bam
: bam/2200225399_62972187/62972187_S85.bqrt.bam
: bam/2200293962_62979117/62979117_S63.bqrt.bam
: bam/2200423985_62999352/62999352_S1.bqrt.bam
: bam/2200495073_63010427/63010427_S20.bqrt.bam
: bam/2200511274_63012586/63012586_S114.bqrt.bam
: bam/2200669188_63036688/63036688_S150.bqrt.bam
* Nouveau workflow :workflow:
** TODO Bases de données
*** KILL Nix pour télécharger les données brutes
**** Conclusion
Non viable sur cluster car en dehors de /nix/store
On peut utiliser des symlink mais trop compliqué
**** KILL Axel au lieu de curl pour gérer les timeout?
CLOSED: [2022-08-19 Fri 15:18]
*** DONE Tester patch de @pennae pour gros fichiers
SCHEDULED: <2022-08-19 Fri>
*** KILL Télécharger les données avec nextflow: hg38
CLOSED: [2023-06-12 Mon 23:29]
**** DONE Genome de référence
**** DONE dbSNP
**** DONE VEP 20G
CLOSED: [2023-06-12 Mon 23:29]
Ajout vérification checksum -> à vérifier
**** DONE transcriptome (spip)
CLOSED: [2023-06-12 Mon 23:29]
Rajouter checksum manuel
**** KILL Refseq
**** KILL OMIM
CLOSED: [2023-06-12 Mon 23:29]
codé, à vérifier
**** KILL ACMG incidental
CLOSED: [2023-06-12 Mon 23:29]
*** TODO Données :T2T:
:PROPERTIES:
:ID:       5d915178-ca96-44ef-87f1-6702af114f2b
:END:
**** DONE fasta
CLOSED: [2023-06-12 Mon 23:30]
***** DONE compatibilité hg38
CLOSED: [2023-06-12 Mon 23:30]
**** DONE fasta index
CLOSED: [2023-06-13 Tue 00:07]
***** DONE compatibilité hg38
CLOSED: [2023-06-13 Tue 00:07]
**** DONE fasta dictionnaire
CLOSED: [2023-06-13 Tue 00:07]
**** DONE dbSNP
CLOSED: [2023-06-12 Mon 23:30]
***** DONE compatibilité hg38
CLOSED: [2023-06-12 Mon 23:30]
**** DONE commonSNP
CLOSED: [2023-06-14 Wed 22:32]
***** DONE compatibilité hg38
CLOSED: [2023-06-14 Wed 22:32]
cd /Work/Groups/bisonex/data/dbsnp/GRCh38.p14
❯ ga@mesointeractive GRCh38.p14]$ zgrep -c '^NC' dbSNP_common.vcf.gz
21340485
[apraga@mesointeractive GRCh38.p14]$ pwd
[apraga@mesointeractive GRCh38.p14]$ zgrep -c '^NC'
dbSNP_common.vcf.gz                     ID_of_common_snp_not_clinvar_patho.txt
dbSNP_common.vcf.gz.tbi                 ID_of_common_snp.txt
[apraga@mesointeractive dbsnp]$ cd chm13v2.0/
[apraga@mesointeractive chm13v2.0]$ ls
chm13v2.0_dbSNPv155.vcf.gz      dbSNP_common.vcf.gz.tbi                 versions.yml
chm13v2.0_dbSNPv155.vcf.gz.tbi  ID_of_common_snp_not_clinvar_patho.txt
dbSNP_common.vcf.gz             ID_of_common_snp.txt
[apraga@mesointeractive chm13v2.0]$ zgrep -c '^chr' dbSNP_common.vcf.gz
19433713
[apraga@mesointeractive chm13v2.0] $
❯ man tmux
**** DONE commonSNP non patho
CLOSED: [2023-06-14 Wed 22:35]
***** DONE compatibilité hg38
CLOSED: [2023-06-14 Wed 22:35]
**** DONE cache vep
CLOSED: [2023-06-30 Sun 14:20] SCHEDULED: <2023-07-25 Tue>
*** HOLD Processing bases de données
**** DONE dbSNP common
**** DONE Seulement les ID dans dbSNP common !
CLOSED: [2022-11-19 Sat 21:42]
172G au lieu de 253M...
**** HOLD common dbSNP not clinvar patho
***** DONE Conclusion partielle
CLOSED: [2022-12-12 Mon 22:25]
- vcfeval : prometteur mais n'arrive pas à traiter toutes les régions
- isec : trop de problèmes avec
- classif clinvar directement dans dbSNP: le plus simple
  Et ça permet de rattraper quelques erreurs dans le script d'Alexis
***** KILL Utiliser directement le numéro dbSNP dans clinvar ? Non
CLOSED: [2022-11-20 Sun 19:51]
Ex: chr20
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools query -f 'rs%INFO/RS \n' -i 'INFO/RS != "." & INFO/CLNSIG="Pathogenic"' clinvar_chr20.vcf.gz | sort > ID_clinvar_patho.txt
bcftools query -f '%ID\n' dbSNP_common_chr20.vcf.gz | sort > ID_of_common_snp.txt
comm -23 ID_of_common_snp.txt ID_clinvar_patho.txt > ID_of_common_snp_not_clinvar_patho.txt
wc -l ID_of_common_snp_not_clinvar_patho.txt
# sort ID
#+end_src
#+RESULTS:
: 518846 ID_of_common_snp_not_clinvar_patho.txt
Version d'alexis
#+begin_src sh :dir ~/code/bisonex/test_isec
snp=dbSNP_common_chr20.vcf.gz
clinvar=clinvar_chr20_notremapped.vcf.gz
python ../script/pythonScript/clinvar_sbSNP.py \
    --clinvar $clinvar \
    --chrm_name_table ../database/RefSeq/refseq_to_number_only_consensual.txt \
    --dbSNP $snp --output prod.txt
wc -l prod.txt
zgrep '^NC' dbSNP_common_chr20.vcf.gz | wc -l
#+end_src
#+RESULTS:
| 518832 | prod.txt |
| 518846 |          |
***** KILL classification clinvar codée dbSNP ?
CLOSED: [2022-12-04 Sun 14:38]
Sur le chromosome 20
*Attention* CLNSIG a plusieurs champs (séparé par une virgule)
On y accède avec INFO/CLNSIG[*]
Ensuite, chaque item peut avoir plusieurs haploïdie (séparé par un |). IL faut donc utiliser une regexp
NB: *ne pas mettre la condition* dans une variable !!
Pour avoir les clinvar patho, on veut 5 mais pas 255 (= autre) pour la classification !`
Il faut également les likely patho et conflicting
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools query -f '%INFO/CLNSIG\n' dbSNP_common_chr20.vcf.gz -i \
'INFO/CLNSIG[*]~"^5|" | INFO/CLNSIG[*]=="5" | INFO/CLNSIG[*]~"|5" | INFO/CLNSIG[*]~"^4|" | INFO/CLNSIG[*]=="4" | INFO/CLNSIG[*]~"|4" | INFO/CLNSIG[*]~"^12|" | INFO/CLNSIG[*]=="12" | INFO/CLNSIG[*]~"|12"' | sort
#+end_src
#+RESULTS:
| . |  . | 12 |    |   |   |   |   |   |   |   |
| . | 12 |  0 |  2 |   |   |   |   |   |   |   |
| 2 |  3 |  2 |  2 | 2 | 5 | . |   |   |   |   |
| . |  2 |  3 |  2 | 2 | 4 |   |   |   |   |   |
| . |  . |  3 | 12 | 3 |   |   |   |   |   |   |
| . |  5 |  2 |  . |   |   |   |   |   |   |   |
| . |  . |  . |  5 | 2 | 2 |   |   |   |   |   |
| . |  9 |  9 |  9 | 5 | 5 | 2 | 3 | 2 | 3 | 2 |
Si on les exclut :
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools query -f '%ID\n' dbSNP_common_chr20.vcf.gz -e \
'INFO/CLNSIG[*]~"^5|" | INFO/CLNSIG[*]=="5" | INFO/CLNSIG[*]~"|5" | INFO/CLNSIG[*]~"4" | INFO/CLNSIG[*]~"12"' | sort | uniq > common-notpatho.txt
#+end_src
#+RESULTS:
 #+begin_src sh :dir ~/code/bisonex/test_isec
snp=dbSNP_common_chr20.vcf.gz
clinvar=clinvar_chr20_notremapped.vcf.gz
python ../script/pythonScript/clinvar_sbSNP.py \
    --clinvar $clinvar \
    --chrm_name_table ../database/RefSeq/refseq_to_number_only_consensual.txt \
    --dbSNP $snp --output tmp.txt
sort tmp.txt | uniq > common-notpatho-alexis.txt
wc -l common-notpatho-alexis.txt
 #+end_src
 #+RESULTS:
 : 518832 common-notpatho-alexis.txt
On en a 6 de plus que la version d'Alexis mais quelques différences
Ceux d'Alexis qui manquent:
#+begin_src sh :dir ~/code/bisonex/test_isec
comm -23 common-notpatho-alexis.txt common-notpatho.txt > alexis-only.txt
cat alexis-only.txt
#+end_src
#+RESULTS:
| rs1064039  |
| rs3833341  |
| rs73598374 |
On les teste dans clinvar et dbSNP
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'ID=@alexis-only.txt' dbSNP_common_chr20.vcf.gz
bcftools query -f '%POS\n' -i 'ID=@alexis-only.txt' dbSNP_common_chr20.vcf.gz > alexis-only-pos.txt
while read  -r line; do
bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'POS='$line clinvar_chr20.vcf.gz
done < alexis-only-pos.txt
# bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'POS=23637790' clinvar_chr20.vcf.gz
#+end_src
#+RESULTS:
|   764018 | A | ACAGGTCAAT,ACAGGT | .,5     | 2,. |   |
| 23637790 | C | G,T               | .,.,12  |     |   |
| 44651586 | C | A,G,T             | .,.,.,5 |   2 | 2 |
|   764018 | A | ACAGGTCAAT        | Benign  |     |   |
| 23637790 | C | T                 | Benign  |     |   |
| 44651586 | C | T                 | Benign  |     |   |
On a donc une discordance entre clinvar et dbSNP.
On dirait qu'ils ont mal fait l'intersection avec clinvar.
Par exemple https://www.ncbi.nlm.nih.gov/snp/rs3833341#clinical_significance
Tu as l'impression qu'il y a un 1 clinvar bénin et 1 patho.
En cherchant par NM, tu vois qu'il est bénin sur clinvar car il y a d'autres soumissions ! https://www.ncbi.nlm.nih.gov/clinvar/variation/262235/
Confirmation sur nos bases de données :
$ bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'POS=764018' dbSNP_common_chr20.vcf.gz
764018 A ACAGGTCAAT,ACAGGT .,5|2,.
$ bcftools query -f '%POS %REF %ALT %INFO/CLNSIG\n' -i 'POS=764018' clinvar_chr20.vcf.gz
764018 A ACAGGTCAAT Benign
***** KILL Corriger script alexi
CLOSED: [2022-12-04 Sun 13:03]
Gère clinvar patho, probablement patho ou conflicting !
***** HOLD Rtg tools
****** Test
1. Générer SDf file
   #+begin_src sh
rtg format genomeRef.fna  -o genomeRef.sdf
   #+end_src
2. Pour les bases de donnés, il faut l'option --sample ALT sinon on a
 #+begin_src
$ rtg vcfeval -b dbSNP_common.vcf.gz -c clinvar.vcf.gz -o test -t genomeRef.sdf/^C
VCF header does not contain a FORMAT field named GQ
Error: Record did not contain enough samples: NC_000001.11	10001	rs1570391677	A,C	.	PASS	RS=1570391677;dbSNPBuildID=154;SSR=0;PSEUDOGENEINFO=DDX11L1:100287102;VC=SNV;R5;GNO;FREQ=KOREAN:0.9891,0.0109,.|SGDP_PRJ:0,1,.|dbGaP_PopFreq:1,.,0;COMMON
 #+end_src
 Essai intersection clinvar (patho ou non) dbSNP
   - faux négatif = dbSNP common qui ne sont pas dans clinvar
   - faux positif = clinvar qui ne sont pas dbSNP common
   - vrai positif = clinvar qui sont dans dbSNP common
   - vrai positif baseline = dbSNP common qui sont dans clinvar
 On calcule le nombre de lignes
 #+begin_src ssh
zgrep '^[^#]' /Work/Groups/bisonex/data/clinvar/GRCh38/clinvar.vcf.gz | wc -l
for i in *.vcf.gz; do echo $i; zgrep '^[^#]' $i | wc -l; done
 #+end_src
 | clinvar            |  1493470 |
 | fn.vcf.gz          | 22330220 |
 | fp.vcf.gz          |  1222529 |
 | tp-baseline.vcf.gz |   131040 |
 | tp.vcf.gz          |   136638 |
À noter qu'on ne retrouve pas tout clinvar...
1222529 + 131040 = 1353569 < 1493470
certains régions ne sont pas traitées :
#+begin_quote
Evaluation too complex (50002 unresolved paths, 34891 iterations) at reference region NC_000001.11:790930-790970. Variants in this region will not be included in results
#+end_quote
#+begin_src sh
grep 'not be included' vcfeval.log | wc -l
56192
#+end_src
Le total est quand même inférieur
On veut les clinvar non patho dans dbSNP soit les faux négatif (dbSNP common not contenu dans clinvar patho)
#+begin_src sh
bcftools filter -i 'INFO/CLNSIG="Pathogenic"' /Work/Groups/bisonex/data/clinvar/GRCh38/clinvar.vcf.gz -o /Work/Groups/bisonex/data/clinvar/GRCh38/clinvar-patho.vcf.gz
tabix /Work/Groups/bisonex/data/clinvar/GRCh38/clinvar-patho.vcf.gz
#+end_src
On lance le script (dbSNP common et clinvar = 9h)
#+begin_src sh
#!/bin/bash
#SBATCH --nodes=1
#SBATCH -p smp
#SBATCH --time=12:00:00
#SBATCH --mem=12G
dir=/Work/Groups/bisonex/data
dbSNP=$dir/dbSNP/GRCh38.p13/dbSNP_common.

Replacement in projects/bisonex.org at line 6 [95.35]

B:BD[97.12706] → [97.12706:12744]

B:BD[97.12744] → [96.32836:57374]

bien vérifier que tous ne sont pas pa
tho.
   La version d'Alexis le fait bien
| NC_000020.11 | 3234173 | rs3827075 | T         | A,C,G     |                                              |
| NC_000020.11 | 3234173 |    262001 | T         | G         | Conflicting_interpretations_of_pathogenicity |
| NC_000020.11 | 3234173 |   1072511 | T         | TGGCGAAGC | Pathogenic                                   |
| NC_000020.11 | 3234173 |    208613 | TGGCGAAGC | G         | Pathogenic                                   |
| NC_000020.11 | 3234173 |      1312 | TGGCGAAGC | T         | Pathogenic                                   |
****** DONE Voir si isec gère les multiallélique (chr20) : non, impossible de faire marcher
CLOSED: [2022-11-27 Sun 00:37]
******* DONE chr20 en prenant un patho clinvar aussi dans dbSNP
CLOSED: [2022-11-27 Sun 00:37]
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools filter dbSNP_common_chr20.vcf.gz -i 'POS=10652589' -o test_dbsnp.vcf.gz
bcftools filter clinvar_chr20.vcf.gz -i 'POS=10652589' -o test_clinvar.vcf.gz
bcftools index test_dbsnp.vcf.gz
bcftools index test_clinvar.vcf.gz
#+end_src
#+RESULTS:
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools isec test_dbsnp.vcf.gz test_clinvar.vcf.gz -p tmp
grep '^[^#]' tmp/0002.vcf
grep '^[^#]' tmp/0003.vcf
#+end_src
#+RESULTS:
Même en biallélique, ne fonctionne pas.
Testé en modifiant test_dbsnp !
Fonctionne avec un variant par ligne
****** DONE isec en coupant les sites multialléliques: non
CLOSED: [2022-11-27 Sun 00:37]
******* DONE Exemple simple ok
CLOSED: [2022-11-27 Sun 00:34]
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools filter -i 'POS=10652589' dbSNP_common_chr20.vcf.gz -o dbsnp_mwi.vcf.gz
bcftools filter -i 'POS=10652589' clinvar_chr20.vcf.gz -o clinvar_mwi.vcf.gz
bcftools index -f dbsnp_mwi.vcf.gz
bcftools index -f clinvar_mwi.vcf.gz
bcftools isec dbsnp_mwi.vcf.gz clinvar_mwi.vcf.gz -n=2
#+end_src
#+RESULTS:
Même en biallélique, ne fonctionne pas.
Chr 20
Avec les fichiers du teste précédent
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools norm -m -any dbsnp_mwi.vcf.gz -o dbsnp_mwi_norm.vcf.gz
bcftools index dbsnp_mwi_norm.vcf.gz
bcftools isec dbsnp_mwi_norm.vcf.gz clinvar_mwi.vcf.gz -n=2
#+end_src
#+RESULTS:
| NC_000020.11 | 10652589 | G | A | 11 |
| NC_000020.11 | 10652589 | G | C | 11 |
******* TODO Sur dbSNP chr20 non
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools norm -m -any dbSNP_common_chr20 -o dbSNP_common_chr20_norm.vcf.gz
#+end_src
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools isec -i 'INFO/CLNSIG="Pathogenic"' dbSNP_common_chr20_norm.vcf.gz clinvar_chr20.vcf.gz -p tmp
#+end_src
#+RESULTS:
***** DONE Essai bedtools intersect
#+begin_src sh
bedtools intersect -a  dbSNP_common.vcf.gz -b clinvar.vcf.gz
#+end_src
$ wc -l intersect.vcf
220206 intersect.vcf
** TODO Dépendences avec Nix
*** DONE GATK
CLOSED: [2022-10-21 Fri 21:59]
*** WAIT BioDBHTS
Contribuer pull request
*** DONE BioExtAlign
CLOSED: [2022-10-22 Sat 00:38]
*** WAIT BioBigFile
Revoir si on peut utliser kent dernière version
Contribuer pull request
*** HOLD rtg-tools
Convertir clinvar NC
*** DONE simuscop
CLOSED: [2022-12-30 Fri 22:31]
*** DONE Spip
CLOSED: [2022-12-04 Sun 12:49]
Pas de pull request
*** DONE R + packages
CLOSED: [2022-11-19 Sat 21:05]
*** TODO hap.py
https://github.com/Illumina/hap.py
**** DONE Version sans rtgtools avec python 3
CLOSED: [2023-02-02 Thu 22:15]
Procédure pour tester
#+begin_src
nix develop .#hap-py
$ genericBuild
#+end_src
1. Supprimer l’appel à make_dependencies dans cmakelist.txt : on peut tout installer avec nix
2. Patch Roc.cpp pour avoir numeric_limits ( error: 'numeric_limits' is not a member of 'std')
3. ajout de flags de link (essai, error)
set(ZLIB_LIBRARIES -lz -lbz2 -lcurl -lcrypto -llzma)
4. Changer les appels à print en print() dans le code python et suppression de quelques import
[nix-shell:~/source]$ sed -i.orig 's/print \"\(.*\)"/print(\1)/' src/python/*.py
**** DONE Sérialiser json pour écrire données de sorties
CLOSED: [2023-02-17 Fri 19:25]
**** DONE Tester sur example
CLOSED: [2023-02-04 Sat 00:25]
#+begin_src sh
$ cd hap.py
$ ../result/bin/hap.py example/happy/PG_NA12878_chr21.vcf.gz       example/happy/NA12878_chr21.vcf.gz       -f example/happy/PG_Conf_chr21.bed.gz       -o test -r example/chr21.fa
#+end_src
#+RESULTS:
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score |
| INDEL | ALL    |        8937 |     7839 |     1098 |       11812 |      343 |      3520 |    45 |   283 |      0.877140 |         0.958635 |       0.298002 |        0.916079 |
| INDEL | PASS   |        8937 |     7550 |     1387 |        9971 |      283 |      1964 |    30 |   242 |      0.844803 |         0.964656 |       0.196971 |        0.900760 |
| SNP   | ALL    |       52494 |    52125 |      369 |       90092 |      582 |     37348 |   107 |   354 |      0.992971 |         0.988966 |       0.414554 |        0.990964 |
| SNP   | PASS   |       52494 |    46920 |     5574 |       48078 |      143 |       992 |     8 |    97 |      0.893816 |         0.996963 |       0.020633 |        0.942576 |
**** TODO Version avec rtg-tools
**** TODO Faire fonctionner Tests
***** TODO Essai 2 : depuis nix develop:
SCHEDULED: <2023-07-25 Tue>
#+begin_src
nix develop .#hap-py
genericBuild
#+end_src
Lancé initialement à la main, mais on peut maintenant utiliser run_tests
#+begin_src
HCDIR=bin/ ../src/sh/run_tests.sha
#+end_src
- [X] test boost
- [X] multimerge
- [X] hapenum
- [X] fp accuracy
- [X] faulty variant
- leftshift fails
- [X] other vcf
- [X] chr prefix
- [X] gvcf
- [X] decomp
- [X] contig lengt
- [X]  integration test
- [ ] scmp fails sur le type
- [X] giab
- [X] performance
- [ ] quantify fails sur le type
- [ ] stratified échec sur les résultats !
- [X] pg counting
- [ ] sompy: ne trouve pas Strelka dans somatic
phases="buildPhase checkPhase installPhase fixupPhase" genericBuild
#+end_src
**** KILL Reproduire les performances precisionchallenge : attention à HG002 et HG001!
CLOSED: [2023-04-01 Sat 19:43]
https://www.nist.gov/programs-projects/genome-bottle
***** KILL 0GOOR
CLOSED: [2023-04-01 Sat 19:40]
Le problème venait 1. de l'ADN et 2. du renommage des chromosomes qui était faux
****** DONE HG002
CLOSED: [2023-02-17 Fri 19:31]
 Type Filter  TRUTH.TOTAL  TRUTH.TP  TRUTH.FN  QUERY.TOTAL  QUERY.FP  QUERY.UNK  FP.gt  FP.al  METRIC.Recall  METRIC.Precision  METRIC.Frac_NA  METRIC.F1_Score
INDEL    ALL       525466    491355     34111      1156702     57724     605307   9384  25027       0.935084          0.895313        0.523304         0.914766
INDEL   PASS       525466    491355     34111      1156702     57724     605307   9384  25027       0.935084          0.895313        0.523304         0.914766
  SNP    ALL      3365115   3358399      6716      5666020     21995    2284364   4194   1125       0.998004          0.993496        0.403169         0.995745
  SNP   PASS      3365115   3358399      6716      5666020     21995    2284364   4194   1125       0.998004          0.993496        0.403169         0.995745
 TRUTH.TOTAL.TiTv_ratio  QUERY.TOTAL.TiTv_ratio  TRUTH.TOTAL.het_hom_ratio  QUERY.TOTAL.het_hom_ratio
                    NaN                     NaN                   1.528276                   2.752637
                    NaN                     NaN                   1.528276                   2.752637
               2.100129                1.473519                   1.581196                   1.795603
               2.100129                1.473519                   1.581196                   1.795603
***** KILL Avec python2
CLOSED: [2023-02-17 Fri 19:25]
****** KILL avec nix
CLOSED: [2023-02-17 Fri 19:25]
conda create -n python2 python=2.7 anaconda
****** KILL avec conda
CLOSED: [2023-02-17 Fri 19:25]
******* Gentoo: regex_error sur test...
Ok avec bash !
#+begin_src
anaconda3/bin/conda create --name py2 python=2.7
conda activate py2
conda install -c bioconda hap.py
#+end_src
******** Faire tourner les tests.
Il faut remplace bin/test_haplotypes par test_haplotypes dans src/sh/run_tests.sh
#+begin_src sh
 HGREF=../genome/GRCh38/GCA_000001405.15_GRCh38_no_alt_analysis_set.fasta HCDIR=~/anaconda3/envs/py2/bin bash src/sh/run_tests.sh
#+end_src
Echec:
test_haplotypes: /opt/conda/conda-bld/work/hap.py-0.3.7/src/c++/lib/tools/Fasta.cpp:81: MMappedFastaFile::MMappedFastaFile(const string&): Assertion `fd != -1' failed.
unknown location(0): fatal error in "testVariantPrimitiveSplitter": signal: SIGABRT (application abort requested)
/opt/conda/conda-bld/work/hap.py-0.3.7/src/c++/test/test_align.cpp(298): last checkpoint
******** Chr21
HGREF=../genome/GRCh38/GCA_000001405.15_GRCh38_no_alt_analysis_set.fasta hap.py        example/happy/PG_NA12878_chr21.vcf.gz       example/happy/NA12878_chr21.vcf.gz       -f example/happy/PG_Conf_chr21.bed.gz       -o test
******* Helios
échec
** TODO T2T :T2T:
Toutes les ressourcs sont décrites ici
https://github.com/marbl/CHM13
Détails sur le pipeline
https://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hub_3267197_GCA_009914755.4&c=CP068277.2&g=hub_3267197_hgLiftOver
*** DONE Alignement
CLOSED: [2023-06-26 Mon 19:42]
NXF_OPTS=-D"user.name=${USER}" nextflow run main.nf -profile standard,helios  --input="/Work/Groups/bisonex/data/giab/*_R{1,2}_001.fastq.gz" --id=NA12878-T2T -bg
SCHEDULED: <2023-06-14 Wed>
*** DONE Haplotypecaller
CLOSED: [2023-06-26 Mon 19:42] SCHEDULED: <2023-06-15 Thu>
*** TODO Filtres
SCHEDULED: <2023-07-27 Thu>
*** Liftover pipelines
:PROPERTIES:
:ID:       d2280207-3f65-4a31-a291-41fa9a9658c2
:END:
Contient les chain files
** TODO Indicateurs qualité
SCHEDULED: <2023-07-26 Wed>
*** Idée
Raredisease:
- FastQC : nombreuses statistiques. Non disponible Nix
- Mosdepth : calcule la profondeur (2x plus rapide que samtools depth). Nix
- MultiQC : fusionne juste les résultats des analyses. Non disponible nix
- Picard's CollectMutipleMetrics, CollectHsMetrics, and CollectWgsMetrics
- Qualimap : alternative fastqc ? Non disponible nix
- Sentieon's WgsMetricsAlgo : propriétaire
- TIDDIT's cov : TIDIT = remaninement chromosomique
Sarek:
- alignment statistics : samtools stats, mosdepth
- QC : MultiQC
MultiQC : non disponible Nix
** TODO vérifier si normalisation
SCHEDULED: <2023-07-26 Wed>
** TODO Rajouter vérification hgvs
SCHEDULED: <2023-07-26 Wed>
** DONE Exécution
CLOSED: [2022-09-13 Tue 21:37]
*** KILL test Bionix
*** KILL Implémenter execution avec Nix ?
Voir https://academic.oup.com/gigascience/article/9/11/giaa121/5987272?login=false
pour un exemple.
Probablement plus simple d’utiliser Nix pour gestion de l’environnement et snakemake pour l’exécution
Pas d’accès internet depuis le cluster
*** DONE nextflow
CLOSED: [2022-09-13 Tue 21:37]
**** TODO Bug scheduler SGE
Le job se fait tuer car l'utilisateur n'est pas passé correctement à nextflow
***** DONE Forcer l'utilisateur à l'exécution
CLOSED: [2023-04-01 Sat 17:57]
NXF_OPTS=-D"user.name=alex"
***** DONE Vérifier si le problème persiste avec 22.10.6
CLOSED: [2023-04-01 Sat 18:38] SCHEDULED: <2023-04-01 Sat>
oui
***** KILL Packager l'utilisateur dans le programme ?
Mauvaise idée..
** TODO Preprocessing avec nextflow
*** TODO Map to reference
**** TODO Sample ID dans header
/Work/Users/apraga/bisonex/out/63003856_S135/preprocessing/baserecalibrator
*** DONE Mark duplicate
CLOSED: [2022-10-09 Sun 22:30]
*** DONE Recalibrate base quality score
CLOSED: [2022-10-09 Sun 22:30]
** DONE Variant calling avec Nextflow
CLOSED: [2022-11-19 Sat 21:34]
*** DONE Haplotype caller
CLOSED: [2022-10-09 Sun 22:40]
*** DONE Filter variants
CLOSED: [2022-10-09 Sun 22:40]
*** DONE Filter common snp not clinvar path
CLOSED: [2022-11-07 Mon 23:00]
Voir [[*common dbSNP not clinvar patho][common dbSNP not clinvar patho]]
*** DONE Filter variant only in consensual sequence
CLOSED: [2022-11-08 Tue 22:23]
*** DONE Filter technical variants
CLOSED: [2022-11-19 Sat 21:34]
*** DONE Utilise AVX pour accélerer l'exécution
CLOSED: [2023-04-29 Sat 15:46]
Sans cela, on a l'avertissement
#+begin_quote
17:28:00.720 INFO  PairHMM - OpenMP multi-threaded AVX-accelerated native PairHMM implementation is not supported
17:28:00.721 INFO  NativeLibraryLoader - Loading libgkl_utils.so from jar:file:/nix/store/cy9ckxqwrkifx7wf02hm4ww1p6lnbxg9-gatk-4.2.4.1/bin/gatk-package-4.2.4.1-local.jar!/com/intel/gkl/native/libgkl_utils.so
17:28:00.733 WARN  NativeLibraryLoader - Unable to load libgkl_utils.so from native/libgkl_utils.so (/Work/Users/apraga/bisonex/out/NA12878_NIST7035/preprocessing/applybqsr/libgkl_utils821485189051585397.so: libgomp.so.1: cannot open shared object file: No such file or directory)
17:28:00.733 WARN  IntelPairHmm - Intel GKL Utils not loaded
17:28:00.733 WARN  PairHMM - ***WARNING: Machine does not have the AVX instruction set support needed for the accelerated AVX PairHmm. Falling back to the MUCH slower LOGLESS_CACHING implementation!
17:28:00.763 INFO  ProgressMeter - Starting traversal
#+end_quote
libgomp.so est fourni par gcc donc il faut charger le module
 module load gcc@11.3.0/gcc-12.1.0
** KILL Utiliser subworkflow
CLOSED: [2023-04-02 Sun 18:08]
Notre version permet d'être plus souple
*** KILL Alignement
CLOSED: [2023-04-02 Sun 18:08] SCHEDULED: <2023-04-05 Wed>
*** KILL Vep
CLOSED: [2023-04-02 Sun 18:08] SCHEDULED: <2023-04-05 Wed>
vcf_annotate_ensemblvep
** TODO Annotation avec nextflow :annotation:
*** KILL VEP : --gene-phenotype ?
CLOSED: [2023-04-18 mar. 18:32]
Vu avec alexis : bases de données non à jour
https://www.ensembl.org/info/genome/variation/phenotype/sources_phenotype_documentation.html
*** DONE plugin VEP
CLOSED: [2023-04-18 mar. 18:32]
Cloner dépôt git avec plugin
Puis utiliser --dir_plugins
*** HOLD Utiliser code d’Alexis
*** TODO Nouvelle version avec VEP
Example avec --custom
https://www.ensembl.org/info/docs/tools/vep/script/vep_custom.html
**** DONE Ajout spliceAI
CLOSED: [2023-05-18 Thu 11:02] SCHEDULED: <2023-04-30 Sun>
plugin VEP
***** DONE Télécharger les données
CLOSED: [2023-05-11 Thu 19:01]
Difficile d'automatiser, le lien est temporaire...
***** DONE PLugin
CLOSED: [2023-05-11 Thu 20:16]
***** DONE Séparer score en plusieurs colonnes
CLOSED: [2023-05-11 Thu 20:16]
Test avec ce fichier pour avoir une ligne avec annotation et une ligne sans
#CHROM	POS	ID	REF	ALT
1	9091	.	A	C
1	69091	.	A	C
et
#+begin_src sh
rm -f postvep.tsv* && vep -i testspliceai.vcf.gz -o postvep.tsv --tab  --dir 109 --merged --pick --use_given_ref   --offline  --plugin SpliceAI,snv=spliceai_scores.raw.snv.hg38.vcf.gz,indel=spliceai_scores.raw.indel.hg38.vcf.gz
#+end_src
#+begin_src
$ bgzip postvep.tsv
$ python spliceai.py
$ cat postvep2.tsv
,variation,Location,Allele,Gene,Feature,Feature_type,Consequence,cDNA_position,CDS_position,Protein_position,Amino_acids,Codons,Existing_variation,IMPACT,DISTANCE,STRAND,FLAGS,REFSEQ_MATCH,SOURCE,REFSEQ_OFFSET,SpliceAI_AG,SpliceAI_AL,SpliceAI_DG,SpliceAI_DL
0,1_9091_A/C,1:9091,C,ENSG00000290825,ENST00000456328,Transcript,upstream_gene_variant,-,-,-,-,-,-,MODIFIER,2778,1,-,-,Ensembl,-,,,,
1,1_69091_A/C,1:69091,C,ENSG00000186092,ENST00000641515,Transcript,missense_variant,124,64,22,M/L,Atg/Ctg,-,MODERATE,-,1,-,-,Ensembl,-,0.01,0.00,0.00,0.01
#+end_src
Test
cp work/bf/437ae511958509e43072f032f4d495/small.tab.gz tests/vep-spip.tab.gz
cp work/d5/3b1244b5ae83d54409ee0d456e8c55/small_cadd.tab.gz tests/vep-cadd-splice.tab.gz
**** TODO Package Nix spliceAI ?
nix profile install nixpkgs#python3Packages.tensorflow
+ ajouter dépendencs ("grep import" ou cnad)
**** TODO Ajout LOEUF et pli
plugin VEP
**** TODO NMD
**** KILL Ajout LOEUF
CLOSED: [2023-04-19 mer. 16:32]
plugin VEP
**** DONE Spip
CLOSED: [2023-05-01 Mon 23:07] SCHEDULED: <2023-04-30 Sun>
BED ne semble pas bien marcher (il faut définir une zone)
VCF : trop d’information
Attention, plusieurs transcripts mais résultats identiques. On supprimer les doublons
***** DONE interpretation + score + intervalle de confiance séparé
CLOSED: [2023-05-01 Mon 23:07] SCHEDULED: <2023-04-30 Sun>
Tests :
dans tests/
vep -i 63004925-small.vcf -o postvep.vcf --vcf --fasta genomeRef.fna --dir 109 --merged --pick  --offline --custom ../script/spip_annotation.vcf.gz,SPIP,vcf,exact,0,spipInterp,spipScore,spipConfidence
***** DONE Score
CLOSED: [2023-04-22 Sat 15:30]
**** DONE CADD: remplacer par plugin VEP
CLOSED: [2023-05-07 Sun 14:45] SCHEDULED: <2023-05-07 Sun>
***** Test
#+begin_src
vep  -i test.vcf  -o lol.vcf --offline --dir  /Work/Projects/bisonex/data/vep/GRCh38/ --merged --vcf --fasta /Work/Projects/bisonex/data/genome/GRCh38.p13/genomeRef.fna --plugin CADD,/Work/Users/apraga/bisonex/work/13/9287a7fef17ab9365f5696f20710cd/gnomad.genomes.r3.0.snv.tsv.gz,/Work/Users/apraga/bisonex/work/13/9287a7fef17ab9365f5696f20710cd/gnomad.genomes.r3.0.indel.tsv.gz  --dir_plugins ../VEP_plugins/ -v
#+end_src
Test
#+begin_src sh
vep --id "1  230710048 230710048 A/G 1"   --offline --dir  /Work/Projects/bisonex/data/vep/GRCh38/ --merged --vcf --fasta /Work/Projects/bisonex/data/genome/GRCh38.p13/genomeRef.fna --plugin CADD,/Work/Users/apraga/bisonex/work/13/9287a7fef17ab9365f5696f20710cd/gnomad.genomes.r3.0.snv.tsv.gz,/Work/Users/apraga/bisonex/work/13/9287a7fef17ab9365f5696f20710cd/gnomad.genomes.r3.0.indel.tsv.gz  --hgvsg --plugin pLI --plugin LOEUF -o lol
#+end_src
CSQ=G|missense_variant|MODERATE|AGT|ENSG00000135744|Transcript|ENST00000366667|protein_coding|2/5||||843|776|259|M/T|aTg/aCg|||-1||HGNC|HGNC:333||Ensembl||A|A||1:g.230710048A>G|0.347|-0.277922|
Correspond bien à https://www.ensembl.org/Homo_sapiens/Tools/VEP/Results?tl=I7ZsIbrj14P6lD43-9115494
***** DONE Utiliser whole genome
CLOSED: [2023-04-29 Sat 15:46]
***** KILL Renommer les chromosome avant ...
CLOSED: [2023-05-01 Mon 09:14] SCHEDULED: <2023-04-30 Sun>
Trop long !
- Téléchargement de CADD: 4h20
- renommer les chromosome pour SNV : 6h20
- tabix sur les SNV : job tué au bout de 21h....
***** DONE annoter séparément et fusionner les tableaux
CLOSED: [2023-05-07 Sun 14:45] SCHEDULED: <2023-05-01 Mon>
NB: on pourrait filtrer CADD avec tabix pour se restreindre à nos variants
**** DONE clinvar
CLOSED: [2023-04-22 Sat 15:31]
**** KILL Vérifier résultats HGVS avec mutalyzer
CLOSED: [2023-05-01 Mon 09:26]
**** TODO Parallélisation
***** HOLD par chromosome avec workflow VEP
https://github.com/Ensembl/ensembl-vep/blob/release/109/nextflow/workflows/run_vep.nf
***** HOLD Avec option --fork
**** DONE Utiliser la version de nf-core de VEP
CLOSED: [2023-05-13 Sat 18:27] SCHEDULED: <2023-05-07 Sun>
**** DONE OMIM
CLOSED: [2023-05-08 Mon 15:02] SCHEDULED: <2023-05-01 Mon>
**** TODO Grantham
**** TODO ACMG incidental
**** TODO Gnomad ?
**** TODO ACMG incidental
**** TODO Gnomad ?
**** DONE Filtrer après VEP avec filter_vep
CLOSED: [2023-04-29 Sat 15:47]
nNon testé
*** TODO Comparer les annotations sur 63003856
**** Relancer le nouveau pipeline
*** HOLD Ancienne version
**** TODO HGVS
**** TODO Filtrer après VEP
**** TODO OMIM
**** TODO clinvar
**** TODO ACMG incidental
**** TODO Grantham
**** KILL LRG
CLOSED: [2023-04-18 mar. 17:22] SCHEDULED: <2023-04-18 Tue>
Vu avec alexis, n’est plus à jour
**** TODO Gnomad
** DONE Porter exactement la version d'Alexis sur Helios
CLOSED: [2023-01-14 Sat 17:56]
Branche "prod"
** KILL Tester version d'alexis avec Nix
CLOSED: [2023-06-14 Wed 22:37]
*** DONE Ajouter clinvar
CLOSED: [2022-11-13 Sun 19:37]
*** DONE Alignement
CLOSED: [2022-11-13 Sun 12:52]
*** DONE Haplotype caller
CLOSED: [2022-11-13 Sun 13:00]
*** KILL Filter
CLOSED: [2023-06-14 Wed 22:37]
- [X] depth
- [ ] comon snp not path
Problème avec liste des ID
**** KILL variant annotation
CLOSED: [2023-06-14 Wed 22:37]
Besoin de vep
*** KILL Variant calling
CLOSED: [2023-06-14 Wed 22:37]
** STRT Tester sarek
#+begin_src sh
 module load apptainer/1.1.8
 nextflow run nf-core/sarek -profile test,singularity --outdir test-sarek
#+end_src
Les dépendences ne se téléchargent pas correctement, on les extrait à la main
#+begin_src sh
 rg -IN galaxyproject modules  | sed 's/ //g;s/:$//' | sort | uniq > deps.txt
#+end_src
 Nettoyage à la main
 Puis
 #+begin_src sh
 cat deps.txt | xargs -L1 singularity pull
 #+end_src
* Amélioration :amelioration:
* Documentation
:PROPERTIES:
:CATEGORY: doc
:END:
** DONE Procédure d'installation nix + dependences pour VM CHU
CLOSED: [2023-04-22 Sat 15:27] SCHEDULED: <2023-04-13 Thu>
* Manuscript
:PROPERTIES:
:CATEGORY: manuscript
:END:
* Tests
:PROPERTIES:
:CATEGORY: tests
:END:
** KILL Non régression : version prod
CLOSED: [2023-05-23 Tue 08:46]
*** DONE ID common snp
CLOSED: [2022-11-19 Sat 21:36]
#+begin_src
$ wc -l ID_of_common_snp.txt
23194290 ID_of_common_snp.txt
$ wc -l /Work/Users/apraga/bisonex/database/dbSNP/ID_of_common_snp.txt
23194290 /Work/Users/apraga/bisonex/database/dbSNP/ID_of_common_snp.txt
#+end_src
*** DONE ID common snp not clinvar patho
CLOSED: [2022-12-11 Sun 20:11]
**** DONE Vérification du problème
CLOSED: [2022-12-11 Sun 16:30]
Sur le J:
21155134 /Work/Groups/bisonex/data/dbSNP/GRCh38.p13/ID_of_common_snp_not_clinvar_patho.txt.ref
Version de "non-régression"
21155076 database/dbSNP/ID_of_common_snp_not_clinvar_patho.txt
Nouvelle version
23193391 /Work/Groups/bisonex/data/dbSNP/GRCh38.p13/ID_of_common_snp_not_clinvar_patho.txt
Si on enlève les doublons
$ sort database/dbSNP/ID_of_common_snp_not_clinvar_patho.txt | uniq > old.txt
$ wc -l old.txt
21107097 old.txt
$ sort /Work/Groups/bisonex/data/dbSNP/GRCh38.p13/ID_of_common_snp_not_clinvar_patho.txt | uniq > new.txt
$ wc -l new.txt
21174578 new.txt
$ sort /Work/Groups/bisonex/data/dbSNP/GRCh38.p13/ID_of_common_snp_not_clinvar_patho.txt.ref | uniq > ref.txt
$ wc -l ref.txt
21107155 ref.txt
Si on regarde la différence
 comm -23 ref.txt old.txt
rs1052692
rs1057518973
rs1057518973
rs11074121
rs112848754
rs12573787
rs145033890
rs147889095
rs1553904159
rs1560294695
rs1560296615
rs1560310926
rs1560325547
rs1560342418
rs1560356225
rs1578287542
...
On cherche le premier
bcftools query -i 'ID="rs1052692"' database/dbSNP/dbSNP_common.vcf.gz -f '%CHROM %POS %REF %ALT\n'
NC_000019.10 1619351 C A,T
Il est bien patho...
$ bcftools query -i 'POS=1619351' database/clinvar/clinvar.vcf.gz -f '%CHROM %POS %REF %ALT %INFO/CLNSIG\n'
19 1619351 C T Conflicting_interpretations_of_pathogenicity
On vérifie pour tous les autres
$ comm -23 ref.txt old.txt > tocheck.txt
On génère les régions à vérifier (chromosome number:position)
$ bcftools query -i 'ID=@tocheck.txt' database/dbSNP/dbSNP_common.vcf.gz -f '%CHROM\t%POS\n' > tocheck.pos
On génère le mapping inverse (chromosome number -> NC)
$ awk ' { t = $1; $1 = $2; $2 = t; print; } ' database/RefSeq/refseq_to_number_only_consensual.txt  > mapping.txt
On remap clinvar
$ bcftools annotate --rename-chrs mapping.txt database/clinvar/clinvar.vcf.gz -o clinvar_remapped.vcf.gz
$ tabix clinvar_remapped.vcf.gz
Enfin, on cherche dans clinvar la classification
$ bcftools query -R tocheck.pos clinvar_remapped.vcf.gz -f '%CHROM %POS %INFO/CLNSIG\n'
$ bcftools query -R tocheck.pos database/dbSNP/dbSNP_common.vcf.gz -f '%CHROM %POS %ID \n' | grep '^NC'
#+RESULTS:
**** DONE Comprendre pourquoi la nouvelle version donne un résultat différent
CLOSED: [2022-12-11 Sun 20:11]
***** DONE Même version dbsnp et clinvar ?
CLOSED: [2022-12-10 Sat 23:02]
Clinvar différent !
  $ bcftools stats clinvar.gz
  clinvar (Alexis)
SN	0	number of samples:	0
SN	0	number of records:	1492828
SN	0	number of no-ALTs:	965
SN	0	number of SNPs:	1338007
SN	0	number of MNPs:	5562
SN	0	number of indels:	144580
SN	0	number of others:	3714
SN	0	number of multiallelic sites:	0
SN	0	number of multiallelic SNP sites:	0
clinvar (new)
SN	0	number of samples:	0
SN	0	number of records:	1493470
SN	0	number of no-ALTs:	965
SN	0	number of SNPs:	1338561
SN	0	number of MNPs:	5565
SN	0	number of indels:	144663
SN	0

[97.12706]

[96.57374]

bien vérifier que tous ne sont pas patho.
   La version d'Alexis le fait bien
| NC_000020.11 | 3234173 | rs3827075 | T         | A,C,G     |                                              |
| NC_000020.11 | 3234173 |    262001 | T         | G         | Conflicting_interpretations_of_pathogenicity |
| NC_000020.11 | 3234173 |   1072511 | T         | TGGCGAAGC | Pathogenic                                   |
| NC_000020.11 | 3234173 |    208613 | TGGCGAAGC | G         | Pathogenic                                   |
| NC_000020.11 | 3234173 |      1312 | TGGCGAAGC | T         | Pathogenic                                   |
****** DONE Voir si isec gère les multiallélique (chr20) : non, impossible de faire marcher
CLOSED: [2022-11-27 Sun 00:37]
******* DONE chr20 en prenant un patho clinvar aussi dans dbSNP
CLOSED: [2022-11-27 Sun 00:37]
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools filter dbSNP_common_chr20.vcf.gz -i 'POS=10652589' -o test_dbsnp.vcf.gz
bcftools filter clinvar_chr20.vcf.gz -i 'POS=10652589' -o test_clinvar.vcf.gz
bcftools index test_dbsnp.vcf.gz
bcftools index test_clinvar.vcf.gz
#+end_src
#+RESULTS:
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools isec test_dbsnp.vcf.gz test_clinvar.vcf.gz -p tmp
grep '^[^#]' tmp/0002.vcf
grep '^[^#]' tmp/0003.vcf
#+end_src
#+RESULTS:
Même en biallélique, ne fonctionne pas.
Testé en modifiant test_dbsnp !
Fonctionne avec un variant par ligne
****** DONE isec en coupant les sites multialléliques: non
CLOSED: [2022-11-27 Sun 00:37]
******* DONE Exemple simple ok
CLOSED: [2022-11-27 Sun 00:34]
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools filter -i 'POS=10652589' dbSNP_common_chr20.vcf.gz -o dbsnp_mwi.vcf.gz
bcftools filter -i 'POS=10652589' clinvar_chr20.vcf.gz -o clinvar_mwi.vcf.gz
bcftools index -f dbsnp_mwi.vcf.gz
bcftools index -f clinvar_mwi.vcf.gz
bcftools isec dbsnp_mwi.vcf.gz clinvar_mwi.vcf.gz -n=2
#+end_src
#+RESULTS:
Même en biallélique, ne fonctionne pas.
Chr 20
Avec les fichiers du teste précédent
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools norm -m -any dbsnp_mwi.vcf.gz -o dbsnp_mwi_norm.vcf.gz
bcftools index dbsnp_mwi_norm.vcf.gz
bcftools isec dbsnp_mwi_norm.vcf.gz clinvar_mwi.vcf.gz -n=2
#+end_src
#+RESULTS:
| NC_000020.11 | 10652589 | G | A | 11 |
| NC_000020.11 | 10652589 | G | C | 11 |
******* TODO Sur dbSNP chr20 non
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools norm -m -any dbSNP_common_chr20 -o dbSNP_common_chr20_norm.vcf.gz
#+end_src
#+begin_src sh :dir ~/code/bisonex/test_isec
bcftools isec -i 'INFO/CLNSIG="Pathogenic"' dbSNP_common_chr20_norm.vcf.gz clinvar_chr20.vcf.gz -p tmp
#+end_src
#+RESULTS:
***** DONE Essai bedtools intersect
#+begin_src sh
bedtools intersect -a  dbSNP_common.vcf.gz -b clinvar.vcf.gz
#+end_src
$ wc -l intersect.vcf
220206 intersect.vcf
** TODO Dépendences avec Nix
*** DONE GATK
CLOSED: [2022-10-21 Fri 21:59]
*** WAIT BioDBHTS
Contribuer pull request
*** DONE BioExtAlign
CLOSED: [2022-10-22 Sat 00:38]
*** WAIT BioBigFile
Revoir si on peut utliser kent dernière version
Contribuer pull request
*** HOLD rtg-tools
Convertir clinvar NC
*** DONE simuscop
CLOSED: [2022-12-30 Fri 22:31]
*** DONE Spip
CLOSED: [2022-12-04 Sun 12:49]
Pas de pull request
*** DONE R + packages
CLOSED: [2022-11-19 Sat 21:05]
*** TODO hap.py
https://github.com/Illumina/hap.py
**** DONE Version sans rtgtools avec python 3
CLOSED: [2023-02-02 Thu 22:15]
Procédure pour tester
#+begin_src
nix develop .#hap-py
$ genericBuild
#+end_src
1. Supprimer l’appel à make_dependencies dans cmakelist.txt : on peut tout installer avec nix
2. Patch Roc.cpp pour avoir numeric_limits ( error: 'numeric_limits' is not a member of 'std')
3. ajout de flags de link (essai, error)
set(ZLIB_LIBRARIES -lz -lbz2 -lcurl -lcrypto -llzma)
4. Changer les appels à print en print() dans le code python et suppression de quelques import
[nix-shell:~/source]$ sed -i.orig 's/print \"\(.*\)"/print(\1)/' src/python/*.py
**** DONE Sérialiser json pour écrire données de sorties
CLOSED: [2023-02-17 Fri 19:25]
**** DONE Tester sur example
CLOSED: [2023-02-04 Sat 00:25]
#+begin_src sh
$ cd hap.py
$ ../result/bin/hap.py example/happy/PG_NA12878_chr21.vcf.gz       example/happy/NA12878_chr21.vcf.gz       -f example/happy/PG_Conf_chr21.bed.gz       -o test -r example/chr21.fa
#+end_src
#+RESULTS:
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score |
| INDEL | ALL    |        8937 |     7839 |     1098 |       11812 |      343 |      3520 |    45 |   283 |      0.877140 |         0.958635 |       0.298002 |        0.916079 |
| INDEL | PASS   |        8937 |     7550 |     1387 |        9971 |      283 |      1964 |    30 |   242 |      0.844803 |         0.964656 |       0.196971 |        0.900760 |
| SNP   | ALL    |       52494 |    52125 |      369 |       90092 |      582 |     37348 |   107 |   354 |      0.992971 |         0.988966 |       0.414554 |        0.990964 |
| SNP   | PASS   |       52494 |    46920 |     5574 |       48078 |      143 |       992 |     8 |    97 |      0.893816 |         0.996963 |       0.020633 |        0.942576 |
**** KILL Version avec rtg-tools
CLOSED: [2023-07-30 Sun 14:38]
**** HOLD Faire fonctionner Tests
***** HOLD Essai 2 : depuis nix develop:
#+begin_src
nix develop .#hap-py
genericBuild
#+end_src
Lancé initialement à la main, mais on peut maintenant utiliser run_tests
#+begin_src
HCDIR=bin/ ../src/sh/run_tests.sha
#+end_src
- [X] test boost
- [X] multimerge
- [X] hapenum
- [X] fp accuracy
- [X] faulty variant
- leftshift fails
- [X] other vcf
- [X] chr prefix
- [X] gvcf
- [X] decomp
- [X] contig lengt
- [X]  integration test
- [ ] scmp fails sur le type
- [X] giab
- [X] performance
- [ ] quantify fails sur le type
- [ ] stratified échec sur les résultats !
- [X] pg counting
- [ ] sompy: ne trouve pas Strelka dans somatic
phases="buildPhase checkPhase installPhase fixupPhase" genericBuild
#+end_src
**** KILL Reproduire les performances precisionchallenge : attention à HG002 et HG001!
CLOSED: [2023-04-01 Sat 19:43]
https://www.nist.gov/programs-projects/genome-bottle
***** KILL 0GOOR
CLOSED: [2023-04-01 Sat 19:40]
Le problème venait 1. de l'ADN et 2. du renommage des chromosomes qui était faux
****** DONE HG002
CLOSED: [2023-02-17 Fri 19:31]
 Type Filter  TRUTH.TOTAL  TRUTH.TP  TRUTH.FN  QUERY.TOTAL  QUERY.FP  QUERY.UNK  FP.gt  FP.al  METRIC.Recall  METRIC.Precision  METRIC.Frac_NA  METRIC.F1_Score
INDEL    ALL       525466    491355     34111      1156702     57724     605307   9384  25027       0.935084          0.895313        0.523304         0.914766
INDEL   PASS       525466    491355     34111      1156702     57724     605307   9384  25027       0.935084          0.895313        0.523304         0.914766
  SNP    ALL      3365115   3358399      6716      5666020     21995    2284364   4194   1125       0.998004          0.993496        0.403169         0.995745
  SNP   PASS      3365115   3358399      6716      5666020     21995    2284364   4194   1125       0.998004          0.993496        0.403169         0.995745
 TRUTH.TOTAL.TiTv_ratio  QUERY.TOTAL.TiTv_ratio  TRUTH.TOTAL.het_hom_ratio  QUERY.TOTAL.het_hom_ratio
                    NaN                     NaN                   1.528276                   2.752637
                    NaN                     NaN                   1.528276                   2.752637
               2.100129                1.473519                   1.581196                   1.795603
               2.100129                1.473519                   1.581196                   1.795603
***** KILL Avec python2
CLOSED: [2023-02-17 Fri 19:25]
****** KILL avec nix
CLOSED: [2023-02-17 Fri 19:25]
conda create -n python2 python=2.7 anaconda
****** KILL avec conda
CLOSED: [2023-02-17 Fri 19:25]
******* Gentoo: regex_error sur test...
Ok avec bash !
#+begin_src
anaconda3/bin/conda create --name py2 python=2.7
conda activate py2
conda install -c bioconda hap.py
#+end_src
******** Faire tourner les tests.
Il faut remplace bin/test_haplotypes par test_haplotypes dans src/sh/run_tests.sh
#+begin_src sh
 HGREF=../genome/GRCh38/GCA_000001405.15_GRCh38_no_alt_analysis_set.fasta HCDIR=~/anaconda3/envs/py2/bin bash src/sh/run_tests.sh
#+end_src
Echec:
test_haplotypes: /opt/conda/conda-bld/work/hap.py-0.3.7/src/c++/lib/tools/Fasta.cpp:81: MMappedFastaFile::MMappedFastaFile(const string&): Assertion `fd != -1' failed.
unknown location(0): fatal error in "testVariantPrimitiveSplitter": signal: SIGABRT (application abort requested)
/opt/conda/conda-bld/work/hap.py-0.3.7/src/c++/test/test_align.cpp(298): last checkpoint
******** Chr21
HGREF=../genome/GRCh38/GCA_000001405.15_GRCh38_no_alt_analysis_set.fasta hap.py        example/happy/PG_NA12878_chr21.vcf.gz       example/happy/NA12878_chr21.vcf.gz       -f example/happy/PG_Conf_chr21.bed.gz       -o test
******* Helios
échec
** TODO T2T :T2T:
Toutes les ressourcs sont décrites ici
https://github.com/marbl/CHM13
Détails sur le pipeline
https://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hub_3267197_GCA_009914755.4&c=CP068277.2&g=hub_3267197_hgLiftOver
*** DONE Alignement
CLOSED: [2023-06-26 Mon 19:42]
NXF_OPTS=-D"user.name=${USER}" nextflow run main.nf -profile standard,helios  --input="/Work/Groups/bisonex/data/giab/*_R{1,2}_001.fastq.gz" --id=NA12878-T2T -bg
SCHEDULED: <2023-06-14 Wed>
*** DONE Haplotypecaller
CLOSED: [2023-06-26 Mon 19:42] SCHEDULED: <2023-06-15 Thu>
*** TODO Filtres
SCHEDULED: <2023-07-27 Thu>
*** Liftover pipelines
:PROPERTIES:
:ID:       d2280207-3f65-4a31-a291-41fa9a9658c2
:END:
Contient les chain files
** PROJ Indicateurs qualité
*** Idée
Raredisease:
- FastQC : nombreuses statistiques. Non disponible Nix
- Mosdepth : calcule la profondeur (2x plus rapide que samtools depth). Nix
- MultiQC : fusionne juste les résultats des analyses. Non disponible nix
- Picard's CollectMutipleMetrics, CollectHsMetrics, and CollectWgsMetrics
- Qualimap : alternative fastqc ? Non disponible nix
- Sentieon's WgsMetricsAlgo : propriétaire
- TIDDIT's cov : TIDIT = remaninement chromosomique
Sarek:
- alignment statistics : samtools stats, mosdepth
- QC : MultiQC
MultiQC : non disponible Nix
** PROJ vérifier si normalisation
** PROJ Rajouter vérification hgvs
** DONE Exécution
CLOSED: [2022-09-13 Tue 21:37]
*** KILL test Bionix
*** KILL Implémenter execution avec Nix ?
Voir https://academic.oup.com/gigascience/article/9/11/giaa121/5987272?login=false
pour un exemple.
Probablement plus simple d’utiliser Nix pour gestion de l’environnement et snakemake pour l’exécution
Pas d’accès internet depuis le cluster
*** DONE nextflow
CLOSED: [2022-09-13 Tue 21:37]
**** TODO Bug scheduler SGE
Le job se fait tuer car l'utilisateur n'est pas passé correctement à nextflow
***** DONE Forcer l'utilisateur à l'exécution
CLOSED: [2023-04-01 Sat 17:57]
NXF_OPTS=-D"user.name=alex"
***** DONE Vérifier si le problème persiste avec 22.10.6
CLOSED: [2023-04-01 Sat 18:38] SCHEDULED: <2023-04-01 Sat>
oui
***** KILL Packager l'utilisateur dans le programme ?
Mauvaise idée..
** TODO Preprocessing avec nextflow
*** TODO Map to reference
**** TODO Sample ID dans header
/Work/Users/apraga/bisonex/out/63003856_S135/preprocessing/baserecalibrator
*** DONE Mark duplicate
CLOSED: [2022-10-09 Sun 22:30]
*** DONE Recalibrate base quality score
CLOSED: [2022-10-09 Sun 22:30]
** DONE Variant calling avec Nextflow
CLOSED: [2022-11-19 Sat 21:34]
*** DONE Haplotype caller
CLOSED: [2022-10-09 Sun 22:40]
*** DONE Filter variants
CLOSED: [2022-10-09 Sun 22:40]
*** DONE Filter common snp not clinvar path
CLOSED: [2022-11-07 Mon 23:00]
Voir [[*common dbSNP not clinvar patho][common dbSNP not clinvar patho]]
*** DONE Filter variant only in consensual sequence
CLOSED: [2022-11-08 Tue 22:23]
*** DONE Filter technical variants
CLOSED: [2022-11-19 Sat 21:34]
*** DONE Utilise AVX pour accélerer l'exécution
CLOSED: [2023-04-29 Sat 15:46]
Sans cela, on a l'avertissement
#+begin_quote
17:28:00.720 INFO  PairHMM - OpenMP multi-threaded AVX-accelerated native PairHMM implementation is not supported
17:28:00.721 INFO  NativeLibraryLoader - Loading libgkl_utils.so from jar:file:/nix/store/cy9ckxqwrkifx7wf02hm4ww1p6lnbxg9-gatk-4.2.4.1/bin/gatk-package-4.2.4.1-local.jar!/com/intel/gkl/native/libgkl_utils.so
17:28:00.733 WARN  NativeLibraryLoader - Unable to load libgkl_utils.so from native/libgkl_utils.so (/Work/Users/apraga/bisonex/out/NA12878_NIST7035/preprocessing/applybqsr/libgkl_utils821485189051585397.so: libgomp.so.1: cannot open shared object file: No such file or directory)
17:28:00.733 WARN  IntelPairHmm - Intel GKL Utils not loaded
17:28:00.733 WARN  PairHMM - ***WARNING: Machine does not have the AVX instruction set support needed for the accelerated AVX PairHmm. Falling back to the MUCH slower LOGLESS_CACHING implementation!
17:28:00.763 INFO  ProgressMeter - Starting traversal
#+end_quote
libgomp.so est fourni par gcc donc il faut charger le module
 module load gcc@11.3.0/gcc-12.1.0
** KILL Utiliser subworkflow
CLOSED: [2023-04-02 Sun 18:08]
Notre version permet d'être plus souple
*** KILL Alignement
CLOSED: [2023-04-02 Sun 18:08] SCHEDULED: <2023-04-05 Wed>
*** KILL Vep
CLOSED: [2023-04-02 Sun 18:08] SCHEDULED: <2023-04-05 Wed>
vcf_annotate_ensemblvep
** TODO Annotation avec nextflow :annotation:
*** KILL VEP : --gene-phenotype ?
CLOSED: [2023-04-18 mar. 18:32]
Vu avec alexis : bases de données non à jour
https://www.ensembl.org/info/genome/variation/phenotype/sources_phenotype_documentation.html
*** DONE plugin VEP
CLOSED: [2023-04-18 mar. 18:32]
Cloner dépôt git avec plugin
Puis utiliser --dir_plugins
*** HOLD Utiliser code d’Alexis
*** TODO Nouvelle version avec VEP
Example avec --custom
https://www.ensembl.org/info/docs/tools/vep/script/vep_custom.html
**** DONE Ajout spliceAI
CLOSED: [2023-05-18 Thu 11:02] SCHEDULED: <2023-04-30 Sun>
plugin VEP
***** DONE Télécharger les données
CLOSED: [2023-05-11 Thu 19:01]
Difficile d'automatiser, le lien est temporaire...
***** DONE PLugin
CLOSED: [2023-05-11 Thu 20:16]
***** DONE Séparer score en plusieurs colonnes
CLOSED: [2023-05-11 Thu 20:16]
Test avec ce fichier pour avoir une ligne avec annotation et une ligne sans
#CHROM	POS	ID	REF	ALT
1	9091	.	A	C
1	69091	.	A	C
et
#+begin_src sh
rm -f postvep.tsv* && vep -i testspliceai.vcf.gz -o postvep.tsv --tab  --dir 109 --merged --pick --use_given_ref   --offline  --plugin SpliceAI,snv=spliceai_scores.raw.snv.hg38.vcf.gz,indel=spliceai_scores.raw.indel.hg38.vcf.gz
#+end_src
#+begin_src
$ bgzip postvep.tsv
$ python spliceai.py
$ cat postvep2.tsv
,variation,Location,Allele,Gene,Feature,Feature_type,Consequence,cDNA_position,CDS_position,Protein_position,Amino_acids,Codons,Existing_variation,IMPACT,DISTANCE,STRAND,FLAGS,REFSEQ_MATCH,SOURCE,REFSEQ_OFFSET,SpliceAI_AG,SpliceAI_AL,SpliceAI_DG,SpliceAI_DL
0,1_9091_A/C,1:9091,C,ENSG00000290825,ENST00000456328,Transcript,upstream_gene_variant,-,-,-,-,-,-,MODIFIER,2778,1,-,-,Ensembl,-,,,,
1,1_69091_A/C,1:69091,C,ENSG00000186092,ENST00000641515,Transcript,missense_variant,124,64,22,M/L,Atg/Ctg,-,MODERATE,-,1,-,-,Ensembl,-,0.01,0.00,0.00,0.01
#+end_src
Test
cp work/bf/437ae511958509e43072f032f4d495/small.tab.gz tests/vep-spip.tab.gz
cp work/d5/3b1244b5ae83d54409ee0d456e8c55/small_cadd.tab.gz tests/vep-cadd-splice.tab.gz
**** TODO Package Nix spliceAI ?
nix profile install nixpkgs#python3Packages.tensorflow
+ ajouter dépendencs ("grep import" ou cnad)
**** TODO Ajout LOEUF et pli
plugin VEP
**** TODO NMD
**** KILL Ajout LOEUF
CLOSED: [2023-04-19 mer. 16:32]
plugin VEP
**** DONE Spip
CLOSED: [2023-05-01 Mon 23:07] SCHEDULED: <2023-04-30 Sun>
BED ne semble pas bien marcher (il faut définir une zone)
VCF : trop d’information
Attention, plusieurs transcripts mais résultats identiques. On supprimer les doublons
***** DONE interpretation + score + intervalle de confiance séparé
CLOSED: [2023-05-01 Mon 23:07] SCHEDULED: <2023-04-30 Sun>
Tests :
dans tests/
vep -i 63004925-small.vcf -o postvep.vcf --vcf --fasta genomeRef.fna --dir 109 --merged --pick  --offline --custom ../script/spip_annotation.vcf.gz,SPIP,vcf,exact,0,spipInterp,spipScore,spipConfidence
***** DONE Score
CLOSED: [2023-04-22 Sat 15:30]
**** DONE CADD: remplacer par plugin VEP
CLOSED: [2023-05-07 Sun 14:45] SCHEDULED: <2023-05-07 Sun>
***** Test
#+begin_src
vep  -i test.vcf  -o lol.vcf --offline --dir  /Work/Projects/bisonex/data/vep/GRCh38/ --merged --vcf --fasta /Work/Projects/bisonex/data/genome/GRCh38.p13/genomeRef.fna --plugin CADD,/Work/Users/apraga/bisonex/work/13/9287a7fef17ab9365f5696f20710cd/gnomad.genomes.r3.0.snv.tsv.gz,/Work/Users/apraga/bisonex/work/13/9287a7fef17ab9365f5696f20710cd/gnomad.genomes.r3.0.indel.tsv.gz  --dir_plugins ../VEP_plugins/ -v
#+end_src
Test
#+begin_src sh
vep --id "1  230710048 230710048 A/G 1"   --offline --dir  /Work/Projects/bisonex/data/vep/GRCh38/ --merged --vcf --fasta /Work/Projects/bisonex/data/genome/GRCh38.p13/genomeRef.fna --plugin CADD,/Work/Users/apraga/bisonex/work/13/9287a7fef17ab9365f5696f20710cd/gnomad.genomes.r3.0.snv.tsv.gz,/Work/Users/apraga/bisonex/work/13/9287a7fef17ab9365f5696f20710cd/gnomad.genomes.r3.0.indel.tsv.gz  --hgvsg --plugin pLI --plugin LOEUF -o lol
#+end_src
CSQ=G|missense_variant|MODERATE|AGT|ENSG00000135744|Transcript|ENST00000366667|protein_coding|2/5||||843|776|259|M/T|aTg/aCg|||-1||HGNC|HGNC:333||Ensembl||A|A||1:g.230710048A>G|0.347|-0.277922|
Correspond bien à https://www.ensembl.org/Homo_sapiens/Tools/VEP/Results?tl=I7ZsIbrj14P6lD43-9115494
***** DONE Utiliser whole genome
CLOSED: [2023-04-29 Sat 15:46]
***** KILL Renommer les chromosome avant ...
CLOSED: [2023-05-01 Mon 09:14] SCHEDULED: <2023-04-30 Sun>
Trop long !
- Téléchargement de CADD: 4h20
- renommer les chromosome pour SNV : 6h20
- tabix sur les SNV : job tué au bout de 21h....
***** DONE annoter séparément et fusionner les tableaux
CLOSED: [2023-05-07 Sun 14:45] SCHEDULED: <2023-05-01 Mon>
NB: on pourrait filtrer CADD avec tabix pour se restreindre à nos variants
**** DONE clinvar
CLOSED: [2023-04-22 Sat 15:31]
**** KILL Vérifier résultats HGVS avec mutalyzer
CLOSED: [2023-05-01 Mon 09:26]
**** TODO Parallélisation
***** HOLD par chromosome avec workflow VEP
https://github.com/Ensembl/ensembl-vep/blob/release/109/nextflow/workflows/run_vep.nf
***** HOLD Avec option --fork
**** DONE Utiliser la version de nf-core de VEP
CLOSED: [2023-05-13 Sat 18:27] SCHEDULED: <2023-05-07 Sun>
**** DONE OMIM
CLOSED: [2023-05-08 Mon 15:02] SCHEDULED: <2023-05-01 Mon>
**** TODO Grantham
**** TODO ACMG incidental
**** TODO Gnomad ?
**** TODO ACMG incidental
**** TODO Gnomad ?
**** DONE Filtrer après VEP avec filter_vep
CLOSED: [2023-04-29 Sat 15:47]
nNon testé
*** TODO Comparer les annotations sur 63003856
**** Relancer le nouveau pipeline
*** HOLD Ancienne version
**** TODO HGVS
**** TODO Filtrer après VEP
**** TODO OMIM
**** TODO clinvar
**** TODO ACMG incidental
**** TODO Grantham
**** KILL LRG
CLOSED: [2023-04-18 mar. 17:22] SCHEDULED: <2023-04-18 Tue>
Vu avec alexis, n’est plus à jour
**** TODO Gnomad
** DONE Porter exactement la version d'Alexis sur Helios
CLOSED: [2023-01-14 Sat 17:56]
Branche "prod"
** KILL Tester version d'alexis avec Nix
CLOSED: [2023-06-14 Wed 22:37]
*** DONE Ajouter clinvar
CLOSED: [2022-11-13 Sun 19:37]
*** DONE Alignement
CLOSED: [2022-11-13 Sun 12:52]
*** DONE Haplotype caller
CLOSED: [2022-11-13 Sun 13:00]
*** KILL Filter
CLOSED: [2023-06-14 Wed 22:37]
- [X] depth
- [ ] comon snp not path
Problème avec liste des ID
**** KILL variant annotation
CLOSED: [2023-06-14 Wed 22:37]
Besoin de vep
*** KILL Variant calling
CLOSED: [2023-06-14 Wed 22:37]
** STRT Tester sarek
#+begin_src sh
 module load apptainer/1.1.8
 nextflow run nf-core/sarek -profile test,singularity --outdir test-sarek
#+end_src
Les dépendences ne se téléchargent pas correctement, on les extrait à la main
#+begin_src sh
 rg -IN galaxyproject modules  | sed 's/ //g;s/:$//' | sort | uniq > deps.txt
#+end_src
 Nettoyage à la main
 Puis
 #+begin_src sh
 cat deps.txt | xargs -L1 singularity pull
 #+end_src
* Amélioration :amelioration:
* Documentation
:PROPERTIES:
:CATEGORY: doc
:END:
** DONE Procédure d'installation nix + dependences pour VM CHU
CLOSED: [2023-04-22 Sat 15:27] SCHEDULED: <2023-04-13 Thu>
* Manuscript
:PROPERTIES:
:CATEGORY: manuscript
:END:
* Tests :tests:
** KILL Non régression : version prod
CLOSED: [2023-05-23 Tue 08:46]
*** DONE ID common snp
CLOSED: [2022-11-19 Sat 21:36]
#+begin_src
$ wc -l ID_of_common_snp.txt
23194290 ID_of_common_snp.txt
$ wc -l /Work/Users/apraga/bisonex/database/dbSNP/ID_of_common_snp.txt
23194290 /Work/Users/apraga/bisonex/database/dbSNP/ID_of_common_snp.txt
#+end_src
*** DONE ID common snp not clinvar patho
CLOSED: [2022-12-11 Sun 20:11]
**** DONE Vérification du problème
CLOSED: [2022-12-11 Sun 16:30]
Sur le J:
21155134 /Work/Groups/bisonex/data/dbSNP/GRCh38.p13/ID_of_common_snp_not_clinvar_patho.txt.ref
Version de "non-régression"
21155076 database/dbSNP/ID_of_common_snp_not_clinvar_patho.txt
Nouvelle version
23193391 /Work/Groups/bisonex/data/dbSNP/GRCh38.p13/ID_of_common_snp_not_clinvar_patho.txt
Si on enlève les doublons
$ sort database/dbSNP/ID_of_common_snp_not_clinvar_patho.txt | uniq > old.txt
$ wc -l old.txt
21107097 old.txt
$ sort /Work/Groups/bisonex/data/dbSNP/GRCh38.p13/ID_of_common_snp_not_clinvar_patho.txt | uniq > new.txt
$ wc -l new.txt
21174578 new.txt
$ sort /Work/Groups/bisonex/data/dbSNP/GRCh38.p13/ID_of_common_snp_not_clinvar_patho.txt.ref | uniq > ref.txt
$ wc -l ref.txt
21107155 ref.txt
Si on regarde la différence
 comm -23 ref.txt old.txt
rs1052692
rs1057518973
rs1057518973
rs11074121
rs112848754
rs12573787
rs145033890
rs147889095
rs1553904159
rs1560294695
rs1560296615
rs1560310926
rs1560325547
rs1560342418
rs1560356225
rs1578287542
...
On cherche le premier
bcftools query -i 'ID="rs1052692"' database/dbSNP/dbSNP_common.vcf.gz -f '%CHROM %POS %REF %ALT\n'
NC_000019.10 1619351 C A,T
Il est bien patho...
$ bcftools query -i 'POS=1619351' database/clinvar/clinvar.vcf.gz -f '%CHROM %POS %REF %ALT %INFO/CLNSIG\n'
19 1619351 C T Conflicting_interpretations_of_pathogenicity
On vérifie pour tous les autres
$ comm -23 ref.txt old.txt > tocheck.txt
On génère les régions à vérifier (chromosome number:position)
$ bcftools query -i 'ID=@tocheck.txt' database/dbSNP/dbSNP_common.vcf.gz -f '%CHROM\t%POS\n' > tocheck.pos
On génère le mapping inverse (chromosome number -> NC)
$ awk ' { t = $1; $1 = $2; $2 = t; print; } ' database/RefSeq/refseq_to_number_only_consensual.txt  > mapping.txt
On remap clinvar
$ bcftools annotate --rename-chrs mapping.txt database/clinvar/clinvar.vcf.gz -o clinvar_remapped.vcf.gz
$ tabix clinvar_remapped.vcf.gz
Enfin, on cherche dans clinvar la classification
$ bcftools query -R tocheck.pos clinvar_remapped.vcf.gz -f '%CHROM %POS %INFO/CLNSIG\n'
$ bcftools query -R tocheck.pos database/dbSNP/dbSNP_common.vcf.gz -f '%CHROM %POS %ID \n' | grep '^NC'
#+RESULTS:
**** DONE Comprendre pourquoi la nouvelle version donne un résultat différent
CLOSED: [2022-12-11 Sun 20:11]
***** DONE Même version dbsnp et clinvar ?
CLOSED: [2022-12-10 Sat 23:02]
Clinvar différent !
  $ bcftools stats clinvar.gz
  clinvar (Alexis)
SN	0	number of samples:	0
SN	0	number of records:	1492828
SN	0	number of no-ALTs:	965
SN	0	number of SNPs:	1338007
SN	0	number of MNPs:	5562
SN	0	number of indels:	144580
SN	0	number of others:	3714
SN	0	number of multiallelic sites:	0
SN	0	number of multiallelic SNP sites:	0
clinvar (new)
SN	0	number of samples:	0
SN	0	number of records:	1493470
SN	0	number of no-ALTs:	965
SN	0	number of SNPs:	1338561
SN	0	number of MNPs:	5565
SN	0	number of indels:	144663
SN	0

Replacement in projects/bisonex.org at line 30 [95.35]

B:BD[14.57842] → [14.57842:58110]

B:BD[14.58110] → [98.79:8003]

B:BD[98.8003] → [99.62:8254]

c -l work/e4/9981dc539a2373c2beeaa0affc3497/Agilent_SureSelect_All_Exons_v7_hg38_Regions_hg38.interval_list
On a donc perdu 1000 zones
***** DONE Liftovervcf avec variant échangé référence/alternative ?
CLOSED: [2023-07-02 Sun 23:09]
***** TODO Comprendre ma
uvais résultat
SCHEDULED: <2023-07-03 Mon>
****** DONE 2x moins de variants, beaucoup trop de FP : erreur de FILTER
CLOSED: [2023-07-08 Sat 11:17] SCHEDULED: <2023-07-07 Fri>
******* DONE [#A] Certains FP sont des vrais FP: erreur de flag et non d'happy
CLOSED: [2023-07-08 Sat 10:43] SCHEDULED: <2023-07-07 Fri>
******** Ex: chr1:408793
G	C	600.64	.	BS=408793;Regions=CONF,TS_contained	GT:BD:BK:BI:BVT:BLT:QQ	./.:.:.:.:NOCALL:nocall:.	0/1:FP:.:tv:SNP:het:600.64
CLOSED: [2023-07-07 Fri 20:03]
********* DONE Vérifier que l'on est bien dans la zone "high confidence" définie dans le bed
oui:
chr1	408514	414397	.	500	+
********* DONE Vérifier que le variant est bien dans le truth et query
CLOSED: [2023-07-07 Fri 20:14]
truth
chr1    408793  .       G       C       50      MismatchedRefAllele     AttemptedAlleles=C*->G;AttemptedLocus=chr1:408793-408793;SwappedAlleles;callable=CS_HiSeqPE300xGATK_callable,CS_10XLRGATK_callable,CS_CCS15kb_20kbGATK4_callable,CS_CGnormal_callable,CS_HiSeqPE300xfreebayes_callable;callsetnames=HiSeqPE300xGATK,CCS15kb_20kbGATK4,CGnormal,HiSeqPE300xfreebayes,CCS15kb_20kbDV,10XLRGATK;callsets=6;datasetnames=HiSeqPE300x,CCS15kb_20kb,CGnormal,10XChromiumLR;datasets=4;datasetsmissingcall=IonExome,SolidSE75bp;filt=CS_CCS15kb_20kbDV_filt,CS_CCS15kb_20kbGATK4_filt;platformnames=Illumina,PacBio,CG,10X;platforms=4       GT:AD:ADALL:DP:GQ       1/0:153,159:141,132:689:99
query
chr1    408793  .       G       C       600.64  .       AC=1;AF=0.500;AN=2;BaseQRankSum=-1.390;DP=64;ExcessHet=0.0000;FS=0.961;MLEAC=1;MLEAF=0.500;MQ=60.00;MQRankSum=0.000;QD=9.53;ReadPosRankSum=-3.146;SOR=0.906     GT:AD:DP:GQ:PL  0/1:33,30:63:99:608,0,798
********* DONE Tester happy sur ce variant: non retrouvé
CLOSED: [2023-07-08 Sat 10:42]
Il faut au moins 2 variant + les index (sinon "no chromosome in common")
Donc on augmente la taille de la région
#+begin_src sh :dir ~/code/bisonex/out
bcftools view HG001_GRCh38_1_22_v4_lifted_merged.vcf.gz  -o lifted_test.vcf.gz chr1:408793-408900
bcftools view HG001-SRX11061486_SRR14724513-T2T.vcf.gz -o run_test.vcf.gz chr1:408793-408900
bcftools index lifted_test.vcf.gz
bcftools index run_test.vcf.gz
#+end_src
 #+begin_src sh
 hap.py lifted_test.vcf.gz run_test.vcf.gz --reference ../data/chm13v2.0.fa -o test
 #+end_src
On trouve bien les 2 variants. L'indel est retrouvé mais pas le SNP
chr1    408793  .       G       C       600.64  .       BS=408793       GT:BD:BK:BI:BVT:BLT:QQ  ./.:.:.:.:NOCALL:nocall:.       0/1:FP:.:tv:SNP:het:600.64
chr1    408874  .       CGAA    C       1476.6  .       BS=408874       GT:BD:BK:BI:BVT:BLT:QQ  0/1:TP:gm:d1_5:INDEL:het:1476.6 0/1:TP:gm:d1_5:INDEL:het:1476.6
********* DONE Changer flag : mismatch -> PASS: OUI
CLOSED: [2023-07-08 Sat 10:42]
********* DONE Changer haplotype 1/0 -> 0/1 truth: idem
CLOSED: [2023-07-08 Sat 10:35]
********* DONE Tester avec vcfeval : idemnt
CLOSED: [2023-07-08 Sat 10:43]
******* DONE Comparer hg38 et T2T: 2x moinsr de variants, trop de FP et FN
CLOSED: [2023-07-07 Fri 18:38]
T2T
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |         413 |      246 |      167 |         751 |      289 |       215 |     2 |    93 |      0.595642 |         0.460821 |       0.286285 |        0.519629 |                    NaN |                    NaN |                  2.428571 |                  2.465116 |
| INDEL | PASS   |         413 |      246 |      167 |         751 |      289 |       215 |     2 |    93 |      0.595642 |         0.460821 |       0.286285 |        0.519629 |                    NaN |                    NaN |                  2.428571 |                  2.465116 |
| SNP   | ALL    |       11236 |    10985 |      251 |       23597 |     9771 |      2841 |    26 |    58 |      0.977661 |         0.529245 |       0.120397 |        0.686734 |                3.11461 |                2.85705 |                  3.640644 |                  2.114633 |
| SNP   | PASS   |       11236 |    10985 |      251 |       23597 |     9771 |      2841 |    26 |    58 |      0.977661 |         0.529245 |       0.120397 |        0.686734 |                3.11461 |                2.85705 |                  3.640644 |                  2.114633 |
Hg38
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |      0.890710 |         0.885510 |       0.378198 |        0.888102 |                    NaN |                    NaN |                  1.860963 |                  2.247273 |
| INDEL | PASS   |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |      0.890710 |         0.885510 |       0.378198 |        0.888102 |                    NaN |                    NaN |                  1.860963 |                  2.247273 |
| SNP   | ALL    |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |       0.162184 |        0.975588 |                3.00711 |               2.784686 |                  1.591810 |                  1.816145 |
| SNP   | PASS   |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |       0.162184 |        0.975588 |                3.00711 |               2.784686 |                  1.591810 |                  1.816145 |
******** Résumé
T2T
| Type  | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision |
| INDEL |         413 |      246 |      167 |         751 |      289 |       215 |     2 |    93 |      0.595642 |         0.460821 |
| SNP   |       11236 |    10985 |      251 |       23597 |     9771 |      2841 |    26 |    58 |      0.977661 |         0.529245 |
Hg38
| Type  | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision |
| INDEL |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |      0.890710 |         0.885510 |
| SNP   |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |
****** DONE Interesection des bed: similaire
CLOSED: [2023-07-04 Tue 23:11]
HG38
 #+begin_src sh
 bedtools intersect -a capture/Agilent_SureSelect_All_Exons_v7_hg38_Regions.bed -b /Work/Groups/bisonex/data/giab/GRCh38/HG001_GRCh38_1_22_v4.2.1_benchmark.bed  | wc -l
 #+end_src
 204280
 T2T
 #+begin_src sh
 bedtools intersect -a /Work/Groups/bisonex/data/giab/T2T/Agilent_SureSelect_All_Exons_v7_hg38_Regions_hg38_T2T.bed -b /Work/Groups/bisonex/data/giab/T2T/HG001_GRCh38_1_22_v4.2.1_benchmark_hg38_T2T.bed  | wc -l
 #+end_src
 204021
****** DONE Vérifier la ligne de commande
CLOSED: [2023-07-04 Tue 23:38]
#+begin_src sh
hap.py \
    HG001_GRCh38_1_22_v4_lifted_merged.vcf.gz \
    HG001-SRX11061486_SRR14724513-T2T.vcf.gz \
     \
    --reference chm13v2.0.fa \
    --threads 6 \
     \
    -T Agilent_SureSelect_All_Exons_v7_hg38_Regions_hg38_T2T.bed \
    --false-positives HG001_GRCh38_1_22_v4.2.1_benchmark_hg38_T2T.bed \
     \
    -o HG001
#+end_src
****** DONE Corriger FILTER : mieux mais toujours trop de négatifs. 3/4 SNP retrouvés
CLOSED: [2023-07-08 Sat 15:19] SCHEDULED: <2023-07-08 Sat>
 Type Fi
lter  TRUTH.TOTAL  TRUTH.TP  TRUTH.FN  QUERY.TOTAL  QUERY.FP  QUERY.UNK  FP.gt  FP.al  METRIC.Recall  METRIC.Precision  METRIC.Frac_NA  METRIC.F1_Score  TRUTH.TOTAL.TiTv_ratio  QUERY.TOTAL.TiTv_ratio  TRUTH.TOTAL.het_hom_ratio  QUERY.TOTAL.het_hom_ratio
INDEL    ALL          413       246       167          751       289        215      2     98       0.595642          0.460821        0.286285         0.519629                     NaN                     NaN                   2.428571                   2.465116
INDEL   PASS          413       246       167          751       289        215      2     98       0.595642          0.460821        0.286285         0.519629                     NaN                     NaN                   2.428571                   2.465116
  SNP    ALL        15883     15479       404        23597      5277       2841     46     44       0.974564          0.745760        0.120397         0.844947                3.017198                 2.85705                   5.560099                   2.114633
  SNP   PASS        15883     15479       404        23597      5277       2841     46     44       0.974564          0.745760        0.120397         0.844947                3.017198                 2.85705                   5.560099                   2.114633
******* DONE Vérifier qu'il ne reste plus de filtre autre que PASS
CLOSED: [2023-07-08 Sat 15:19]
#+begin_src
$ zgrep -c 'PASS' HG001_GRCh38_1_22_v4_lifted_merged.vcf.gz
3730505
$ zgrep -c '^chr' HG001_GRCh38_1_22_v4_lifted_merged.vcf.gz
3730506
#+end_src
****** TODO 1/4 SNP manquant ?
SCHEDULED: <2023-07-08 Sat>
******* DONE Regarder avec Julia si ce sont vraiment des FP: 61/5277 qui ne le sont pas
CLOSED: [2023-07-09 Sun 12:09]
******* HOLD Examiner les FP
******* HOLD Tester un FP
  2 │ chr1        608765  A           G           ./.:.:.:.:NOCALL:nocall:.  1/1:FP:.:ti:SNP:homalt:188
  liftDown UCSC: rien en GIAB : vrai FP
 3 │ chr1        762943  A           G           ./.:.:.:.:NOCALL:nocall:.  1/1:FP:.:ti:SNP:homalt:287
 4 │ chr1        762945  A           T           ./.:.:.:.:NOCALL:nocall:.  1/1:FP:.:tv:SNP:homalt:287
 Remaniements complexes ? Pas dans le gène en HG38
******* DONE La plupart des FP (4705/5566) sont homozygotes: erreur de référence ?
CLOSED: [2023-07-12 Wed 21:10] SCHEDULED: <2023-07-09 Sun>
Sur les 2 premiers variants, ils montrent en fait la différence entre T2T et GRCh38
Erreur à l'alignement ?
******** KILL relancer l'alignement
CLOSED: [2023-07-09 Sun 17:36]
******** DONE vérifier reads identiques hg38 et T2T: oui
CLOSED: [2023-07-09 Sun 16:36]
T2T CHR1608765
38   	chr1:1180168-1180168 (
SRR14724513.24448214
SRR14724513.24448214
******* TODO Enlever les FP qui correspondent à un changement dans le génome
SCHEDULED: <2023-07-09 Sun>
******** Condition:
- pas de variation à la position en GRCh38
- variantion homozygote
- la varation en T2T correspond au changement de pair de base GRC38 -> T2T
  pour les SNP:
  alt_T2T[i] = DNA_GRC38[j]
  avec i la position en T2T et j la position en GRCh38
  Note: définir un ID n'est pas correct car les variants peuvent être modifié par happy !
******** Idée
 - Pour chaque FP, c'est un "faux" FP si
     - REF en hg38 == ALT en T2T
     - et REF en hg38 != REF en T2T
     - et variant homozygote
Comment obtenir les séquences de réferences ?
1. liftover
2. blat sur la séquence autour du variant
3. identifier quelques reads contenant le variant et regarder leur aligneement en hg38
Après discussion avec Alexis: solution 3
******** Algorithme
1. Extraire les coordonnées en T2T des faux positifs *homozygote*
2. Pour chaque faux positif
   1. lister 10 reads contenant le variant
   2. pour chacun de ces reads, récupérer la séquence en T2T et GRCh38 via le nom du read dans le bam
   3. si la séquence en T2T modifiée par le variant est "identique" à celle en GRCh38, alors on ignore ce faux positif
Note: on ignore les reads qui ont changé de chromosome entre les version
******** DONE Résultat préliminaire
CLOSED: [2023-07-23 Sun 14:30]
cf [[file:~/roam/research/bisonex/code/giab/giab-corrected.csv][script julia]]
3498 faux positifs en moins, soit 0.89 sensibilité
julia> tp=15479
julia> fp=5277
julia> tp/(tp+fp)
0.7457602620928888
julia> tp/(tp+(fp-3498))
0.8969173716537258
On est toujours en dessous des 97%
******** TODO Corriger proprement VCF ou résultats Happy
SCHEDULED: <2023-07-23 Sun>
******* DONE Vérifier quelques variants sur IGV
CLOSED: [2023-07-09 Sun 17:36]
******* KILL Répartition des FP : cluster ?
CLOSED: [2023-07-09 Sun 17:36]
******* TODO Méthodologie du pangenome
***** KILL Mail Yannis
CLOSED: [2023-07-08 Sat 10:44]
***** DONE Mail GIAB pour version T2T
CLOSED: [2023-07-07 Fri 18:37]
**** DONE NA12878 :na12878:hg38:
CLOSED: [2023-06-30 Fri 22:30]
***** DONE Discussion alexis : Mail
CLOSED: [2023-03-29 Wed 22:40]
Avec le patient NA12878 et comparaison avec hap.py du VCF de Genome In A Bottle ("gold" standard), on avait pour rappel
- sensibilité (=recall) 71% pour indel, 85% SNP
- précision  (= VPP) 69 et 97% respectivement
| Type  | TRUTH |    TP |   FN | QUERY |   FP |  UNK | FP.gt | FP.al |   Recall | Precision |
| INDEL |  4871 |  3461 | 1410 |  7048 | 1554 | 1987 |   193 |   346 | 0.710532 |  0.692946 |
| SNP   | 46032 | 39369 | 6663 | 44600 | 1186 | 4041 |   304 |    30 | 0.855253 |  0.970759 |
Les statistiques sur les génomes sont bien meilleurs (cf precisionFDA challenge).
Pour les exome, un article [1] a fait a des meilleures stats sur ce patient avec BWA et GATK mais ils ont moins de variant (on a presque un facteur 2 !).
Je soupçonne qu'on ne travaille pas sur les mêmes zones de capture (pas réussi à récupérer leur .bed)
| Exome | Type  |    TP |   FP |  FN | Sensitivity | Precision | F-Score |   FDR |
|     1 | SNV   | 23689 | 1397 | 613 |       0.975 |     0.944 |   0.959 | 0.057 |
|     2 | SNV   | 23946 |  865 | 356 |       0.985 |     0.965 |   0.975 | 0.036 |
|     1 | indel |  1254 |   72 |  75 |       0.944 |     0.946 |   0.945 | 0.054 |
|     2 | indel |  1309 |   10 |  20 |       0.985 |     0.992 |   0.989 | 0.008 |
Pour essayer d'améliorer les statistiques :
- La version du génome GRC38 vs GRCh38.p13 ne change quasiment rien
- Désactiver dbSNP ne change strictement rien pour le variant calling
J'ai exploré les faux négatifs :
- la grande majorité n'est juste pas vue (ce n'est pas un problème d'haploïde/génotype)
- la répartition par chromosome est relativement homogène, sauf sur le 6 ()
- la majorité est en 5' et 3'UTR (selon Best refseq)
Conclusion: je pense m'arrêter là pour la validation du variant calling par manque de temps. Il faudrait creuser pour savoir pourquoi certains variants ne sont pas vus par GATK mais ce n'est pas la majorité. En tout cas, je peux justifier d'une première analyse pour la thèse.
Ça te va ?
[1]
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2928-9
Résultats ici https://static-content.springer.com/esm/art%3A10.1186%2Fs12859-019-2928-9/MediaObjects/12859_2019_2928_MOESM8_ESM.pdf
***** DONE Comparaison
CLOSED: [2023-03-04 Sat 11:14]
HGREF=/Work/Groups/bisonex/data-alexis-reference/genome/GRCh38_latest_genomic.fna ./result/bin/hap.py /Work/Groups/bisonex/NA12878/HG001_GRCh38_1_22_v4.2.1
_benchmark_renamed.vcf.gz script/files/vcf/NA12878_NIST7035_vep_annot.vcf -f /Work/Groups/bison
ex/NA12878/HG001_GRCh38_1_22_v4.2.1_benchmark.bed -o test
na1878.slurm
#+begin_src slurm
#!/bin/bash
#SBATCH -c 4
#SBATCH -p smp
#SBATCH --time=01:00:00
#SBATCH --mem=32G
module load nix/2.11.0
export HGREF=/Work/Groups/bisonex/data-alexis-reference/genome/GRCh38_latest_genomic.fna
dir=/Work/Groups/bisonex/data/NA12878/GRCh38
hap.py ${dir}/HG001_GRCh38_1_22_v4.2.1_benchmark.vcf.gz script/files/vcf/NA12878_NIST7035.vcf -f ${dir}/HG001_GRCh38_1_22_v4.2.1_benchmark.bed -o test
#+end_src
****** KILL beaucoup trop de faux négatifs
CLOSED: [2023-02-17 Fri 19:37]
******* DONE Test 1 : vep annot : beaucoup tro

[14.57842]

[99.8254]

c -l work/e4/9981dc539a2373c2beeaa0affc3497/Agilent_SureSelect_All_Exons_v7_hg38_Regions_hg38.interval_list
On a donc perdu 1000 zones
***** DONE Liftovervcf avec variant échangé référence/alternative ?
CLOSED: [2023-07-02 Sun 23:09]
**** DONE NA12878 :na12878:hg38:
CLOSED: [2023-06-30 Fri 22:30]
***** DONE Discussion alexis : Mail
CLOSED: [2023-03-29 Wed 22:40]
Avec le patient NA12878 et comparaison avec hap.py du VCF de Genome In A Bottle ("gold" standard), on avait pour rappel
- sensibilité (=recall) 71% pour indel, 85% SNP
- précision  (= VPP) 69 et 97% respectivement
| Type  | TRUTH |    TP |   FN | QUERY |   FP |  UNK | FP.gt | FP.al |   Recall | Precision |
| INDEL |  4871 |  3461 | 1410 |  7048 | 1554 | 1987 |   193 |   346 | 0.710532 |  0.692946 |
| SNP   | 46032 | 39369 | 6663 | 44600 | 1186 | 4041 |   304 |    30 | 0.855253 |  0.970759 |
Les statistiques sur les génomes sont bien meilleurs (cf precisionFDA challenge).
Pour les exome, un article [1] a fait a des meilleures stats sur ce patient avec BWA et GATK mais ils ont moins de variant (on a presque un facteur 2 !).
Je soupçonne qu'on ne travaille pas sur les mêmes zones de capture (pas réussi à récupérer leur .bed)
| Exome | Type  |    TP |   FP |  FN | Sensitivity | Precision | F-Score |   FDR |
|     1 | SNV   | 23689 | 1397 | 613 |       0.975 |     0.944 |   0.959 | 0.057 |
|     2 | SNV   | 23946 |  865 | 356 |       0.985 |     0.965 |   0.975 | 0.036 |
|     1 | indel |  1254 |   72 |  75 |       0.944 |     0.946 |   0.945 | 0.054 |
|     2 | indel |  1309 |   10 |  20 |       0.985 |     0.992 |   0.989 | 0.008 |
Pour essayer d'améliorer les statistiques :
- La version du génome GRC38 vs GRCh38.p13 ne change quasiment rien
- Désactiver dbSNP ne change strictement rien pour le variant calling
J'ai exploré les faux négatifs :
- la grande majorité n'est juste pas vue (ce n'est pas un problème d'haploïde/génotype)
- la répartition par chromosome est relativement homogène, sauf sur le 6 ()
- la majorité est en 5' et 3'UTR (selon Best refseq)
Conclusion: je pense m'arrêter là pour la validation du variant calling par manque de temps. Il faudrait creuser pour savoir pourquoi certains variants ne sont pas vus par GATK mais ce n'est pas la majorité. En tout cas, je peux justifier d'une première analyse pour la thèse.
Ça te va ?
[1]
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2928-9
Résultats ici https://static-content.springer.com/esm/art%3A10.1186%2Fs12859-019-2928-9/MediaObjects/12859_2019_2928_MOESM8_ESM.pdf
***** DONE Comparaison
CLOSED: [2023-03-04 Sat 11:14]
HGREF=/Work/Groups/bisonex/data-alexis-reference/genome/GRCh38_latest_genomic.fna ./result/bin/hap.py /Work/Groups/bisonex/NA12878/HG001_GRCh38_1_22_v4.2.1
_benchmark_renamed.vcf.gz script/files/vcf/NA12878_NIST7035_vep_annot.vcf -f /Work/Groups/bison
ex/NA12878/HG001_GRCh38_1_22_v4.2.1_benchmark.bed -o test
na1878.slurm
#+begin_src slurm
#!/bin/bash
#SBATCH -c 4
#SBATCH -p smp
#SBATCH --time=01:00:00
#SBATCH --mem=32G
module load nix/2.11.0
export HGREF=/Work/Groups/bisonex/data-alexis-reference/genome/GRCh38_latest_genomic.fna
dir=/Work/Groups/bisonex/data/NA12878/GRCh38
hap.py ${dir}/HG001_GRCh38_1_22_v4.2.1_benchmark.vcf.gz script/files/vcf/NA12878_NIST7035.vcf -f ${dir}/HG001_GRCh38_1_22_v4.2.1_benchmark.bed -o test
#+end_src
****** KILL beaucoup trop de faux négatifs
CLOSED: [2023-02-17 Fri 19:37]
******* DONE Test 1 : vep annot : beaucoup tro

Replacement in projects/bisonex.org at line 36 [95.35]

B:BD[100.1239] → [100.1239:1249]

B:BD[100.1249] → [14.66715:68004]

B:BD[14.68004] → [96.65699:81698]

B:BD[96.81698] → [101.340:7618]


| Thresho
ld | True-pos-baseline | True-pos-call | False-pos | False-neg | Precision | Sensitivity | F-measure |
|-----------+-------------------+---------------+-----------+-----------+-----------+-------------+-----------|
| 3.000     |             42789 |         42416 |      2598 |      8080 |    0.9423 |      0.8412 |    0.8889 |
| None      |             42798 |         42425 |      2616 |      8071 |    0.9419 |      0.8413 |    0.8888 |
Indel avec le plus petit seuil : zcat NA12878.non_snp_roc.tsv.gz
Attention à inverser precision et recall !
 zcat NA12878.non_snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $6}'
0.71390.7136
SNP avec le plus petit seuil : zcat NA12878.non_snp_roc.tsv.gz
Attention à inverser precision et recall !
$ zcat NA12878.snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $6}'
0.85470.9727
compareNA12878-giab/vcfeval/NA12878.summary.txt
| Threshold | True-pos-baseline | True-pos-call | False-pos | False-neg | Precision | Sensitivity | F-measure |
| 1.000     |             44812 |         44812 |      2878 |      6057 |    0.9397 |      0.8809 |    0.9093 |
| None      |             44813 |         44813 |      2882 |      6056 |    0.9396 |      0.8809 |    0.9093 |
SNP:
$ zcat NA12878.snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $
6}'
0.89370.9621
indel
$ zcat NA12878.non_snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $6}'
0.75980.7445
compareNA12878-giab/happy/NA12878.summary.csv
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
|-------+--------+-------------+----------+----------+-------------+----------+-----------+-------+-------+---------------+------------------+----------------+-----------------+------------------------+------------------------+---------------------------+---------------------------|
| INDEL | ALL    |        4871 |     3678 |     1193 |        7036 |     1299 |      2011 |   208 |   217 |      0.755081 |         0.741493 |       0.285816 |        0.748225 |                        |                        |        1.6174985978687606 |        2.5240506329113925 |
| INDEL | PASS   |        4871 |     3678 |     1193 |        7036 |     1299 |      2011 |   208 |   217 |      0.755081 |         0.741493 |       0.285816 |        0.748225 |                        |                        |        1.6174985978687606 |        2.5240506329113925 |
| SNP   | ALL    |       46032 |    41138 |     4894 |       47694 |     1622 |      4930 |   362 |    31 |      0.893683 |         0.962071 |       0.103367 |        0.926617 |      2.529551552318896 |     2.4124463519313304 |        1.6206857273037931 |        1.6888675840288743 |
| SNP   | PASS   |       46032 |    41138 |     4894 |       47694 |     1622 |      4930 |   362 |    31 |      0.893683 |         0.962071 |       0.103367 |        0.926617 |      2.529551552318896 |     2.4124463519313304 |        1.6206857273037931 |         1.688867584028874 |
***** KILL Résultats sans trimming
CLOSED: [2023-06-25 Sun 15:53] SCHEDULED: <2023-06-26 Mon>
***** DONE Refaire : HiSeq4000 + agilent sureselect + génome "prêt à l'emploi"
CLOSED: [2023-06-30 Fri 22:08] SCHEDULED: <2023-06-25 Sun>
#+begin_src
nextflow run workflows/compareVCF.nf -profile standard,helios --outdir=out/HG001-SRX11061486_SRR14724513-GRCh38 --query=out/HG001-SRX11061486_SRR14724513-GRCh38/callVariant/haplotypecaller/HG001-SRX11061486_SRR14724513-GRCh38.vcf.gz --compare=vcfeval,happy -lib lib --capture=capture/Agilent_SureSelect_All_Exons_v7_hg38_Regions.bed  --id=HG001
#+end_src
Meilleurs résultats !
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |       0.89071 |          0.88551 |       0.378198 |        0.888102 |                        |                        |          1.86096256684492 |         2.247272727272727 |
| INDEL | PASS   |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |       0.89071 |          0.88551 |       0.378198 |        0.888102 |                        |                        |          1.86096256684492 |         2.247272727272727 |
| SNP   | ALL    |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |       0.162184 |        0.975588 |      3.007110300820419 |       2.78468624064479 |        1.5918102430965306 |        1.8161449399656946 |
| SNP   | PASS   |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |       0.162184 |        0.975588 |      3.007110300820419 |       2.78468624064479 |        1.5918102430965306 |        1.8161449399656946 |
***** KILL Utiliser d'autres données brutes ?
CLOSED: [2023-06-25 Sun 15:58]
https://zenodo.org/record/3597727
Capture en hg37 également. Serait intéressant mais pas le temps..
***** KILL Comparer avec UCSCS liftover
CLOSED: [2023-06-26 Mon 19:02] SCHEDULED: <2023-06-25 Sun>
Picard liftoverinterval est basé sur UCSCS
Mais on n'aurait pas la différence pour NA12878 qu'on voit...
**** TODO HG002 :hg002:hg38:
SCHEDULED: <2023-07-25 Tue>
#+begin_src
    NXF_OPTS=-D"user.name=${USER}" nextflow run workflows/giabFastq.nf -profile standard,helios
    NXF_OPTS=-D"user.name=${USER}" nextflow run main.nf -profile standard,helios -resume --input="/Work/Groups/bisonex/data/giab/GRCh38/HG002_{1,2}.fq.gz --test.id=HG002
Only the capture file differs. Results are better using the capture file given by Agilent, stored in data/
    NXF_OPTS=-D"user.name=${USER}" nextflow run workflows/compareVCF.nf -profile standard,helios -resume --outdir=compareHG002 --test.id=HG002 --test.query=out/HG002_1/variantCalling/haplotypecaller/HG002_1.vcf.gz  --test.compare=vcfeval,happy --test.capture=data/AgilentSureSelectv05_hg38.bed
#
#+end_src
***** DONE Mauvais résultats
CLOSED: [2023-04-14 Fri 09:42]
avec vcfeval
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    0.000              24585          24390      10060      39415     0.7080       0.3841     0.4980
     None              24585          24390      10060      39415     0.7080       0.3841     0.4980
La sortie du variantCalling est celle d'happy ???
On relance...
***** DONE Vérifier vcf en hg38
CLOSED: [2023-04-12 Wed 10:33] SCHEDULED: <2023-04-12 Wed>
***** KILL Capture en hg19 ?
CLOSED: [2023-04-13 Thu 09:46] SCHEDULED: <2023-04-12 Wed>
***** KILL Vraiment fichier de capture ou zone d'intérêt ?
CLOSED: [2023-04-13 Thu 09:45] SCHEDULED: <2023-04-12 Wed>
"target region" +/- 50bp
[[https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015/README.txt][README]]
 list file describing the variant calling regions (target regions extended with 50 bp on each end)
***** DONE .bed fourni par AGilent: sensbilité très mauvaise
CLOSED: [2023-04-13 Thu 09:46] SCHEDULED: <2023-04-13 Thu>
Agilent SureSelect Human All Exon V5 kit
Disponible en hg38
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    0.000              19653          19501       6410      21657     0.7526       0.4757     0.5830
     None              19653          19501       6410      21657     0.7526       0.4757     0.5830
***** DONE Trier par nom avec samtools sort : bons résultats
CLOSED: [2023-04-14 Fri 09:25] SCHEDULED: <2023-04-13 Thu>
Avec capture fourni par GIAB
vcf eval
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    5.000              57443          57032        984       6557     0.9830       0.8975     0.9383
     None              57457          57046       1009       6543     0.9826       0.8978     0.9383
Happy
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
|-------+--------+-------------+----------+----------+-------------+----------+-----------+-------+-------+---------------+------------------+----------------+-----------------+------------------------+------------------------+---------------------------+---------------------------|
| INDEL | ALL    |        6150 |     5007 |     1143 |        6978 |      556 |      1346 |   151 |   168 |      0.814146 |         0.901278 |       0.192892 |          0.8555 |                        |                        |        1.5434221840068787 |        1.9467178175618074 |
| INDEL | PASS   |        6150 |     5007 |     1143 |        6978 |      556 |      1346 |   151 |   168 |      0.814146 |         0.901278 |       0.192892 |          0.8555 |                        |                        |        1.5434221840068787 |        1.9467178175618074 |
| SNP   | ALL    |       57818 |    52464 |     5354 |       56016 |      500 |      3046 |    90 |    30 |      0.907399 |         0.990561 |       0.054377 |        0.947158 |     2.4892012548262548 |      2.426824047458871 |        1.5904527117884357 |        1.6107795598657217 |
| SNP   | PASS   |       57818 |    52464 |     5354 |       56016 |      500 |      3046 |    90 |    30 |      0.907399 |         0.990561 |       0.054377 |        0.947158 |     2.4892012548262548 |      2.426824047458871 |        1.5904527117884357 |        1.6107795598657217 |
***** DONE Capture agilent légment meilleur que celui fourni par GIAB (padding ?)
CLOSED: [2023-04-14 Fri 09:48]
GIAB:
vcf eval
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    5.000              57443          57032        984       6557     0.9830       0.8975     0.9383
     None              57457          57046       1009       6543     0.9826       0.8978     0.9383
Happy
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
|-------+--------+-------------+----------+----------+-------------+----------+-----------+-------+-------+---------------+------------------+----------------+-----------------+------------------------+------------------------+---------------------------+---------------------------|
| INDEL | ALL    |        6150 |     5007 |     1143 |        6978 |      556 |      1346 |   151 |   168 |      0.814146 |         0.901278 |       0.192892 |          0.8555 |                        |                        |        1.5434221840068787 |        1.9467178175618074 |
| INDEL | PASS   |        6150 |     5007 |     1143 |        6978 |      556 |      1346 |   151 |   168 |      0.814146 |         0.901278 |       0.192892 |          0.8555 |                        |                        |        1.5434221840068787 |        1.9467178175618074 |
| SNP   | ALL    |       57818 |    52464 |     5354 |       56016 |      500 |      3046 |    90 |    30 |      0.907399 |         0.990561 |       0.054377 |        0.947158 |     2.4892012548262548 |      2.426824047458871 |        1.5904527117884357 |        1.6107795598657217 |
| SNP   | PASS   |       57818 |    52464 |     5354 |       56016 |      500 |      3046 |    90 |    30 |      0.907399 |         0.990561 |       0.054377 |        0.947158 |     2.4892012548262548 |      2.426824047458871 |        1.5904527117884357 |        1.6107795598657217 |
Agilent
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    6.000              37241          36965        449       4069     0.9880       0.9015     0.9428
     None              37248          36972        461       4062     0.9877       0.9017     0.9427
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |        2909 |     2477 |      432 |        3229 |      207 |       519 |    52 |    50 |      0.851495 |         0.923616 |       0.160731 |        0.886091 |                        |                        |        1.4964850615114236 |        1.8339222614840989 |
| INDEL | PASS   |        2909 |     2477 |      432 |        3229 |      207 |       519 |    52 |    50 |      0.851495 |         0.923616 |       0.160731 |        0.886091 |                        |                        |        1.4964850615114236 |        1.8339222614840989 |
| SNP   | ALL    |       38406 |    34793 |     3613 |       36935 |      275 |      1868 |    37 |    15 |      0.905926 |         0.992158 |       0.050575 |        0.947083 |     2.6247759222568168 |     2.5752854654538417 |         1.588953331534934 |        1.6192536889897844 |
| SNP   | PASS   |       38406 |    34793 |     3613 |       36935 |      275 |      1868 |    37 |    15 |      0.905926 |         0.992158 |       0.050575 |        0.947083 |     2.6247759222568168 |     2.5752854654538417 |         1.588953331534934 |        1.6192536889897844 |
***** TODO Refaire : HiSeq4000 + agilent sureselect + génome "prêt à l'emploi"
SCHEDULED: <2023-07-23 Sun>
**** TODO HG003 :hg003:hg38:
***** Notes
#+begin_src sh
NXF_OPTS=-D"user.name=${USER}" nextflow run main.nf -profile standard,helios  --input /Work/Groups/bisonex/data/giab/GRCh38/HG003_{1,2}.fq.gz -bg
#+end_src
#+begin_src  sh
NXF_OPTS=-D"user.name=${USER}" nextflow run workflows/compareVCF.nf -profile standard,helios -resume --outdir=compareHG003  --test.id=HG003 --test.query=out/HG003_1/variantCalling/haplotypecaller/HG003_1.vcf.gz  --test.compare=vcfeval,happy --test.capture=data/AgilentSureSelectv05_hg38.bed
#+end_src
vcfeval
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    5.000              36745          36473        486       3988     0.9869       0.9021     0.9426
     None              36748          36476        495       3985     0.9866       0.9022     0.9425
$ zcat NA12878.snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $6}'
happy
Type Filter  TRUTH.TOTAL  TRUTH.TP  TRUTH.FN  QUERY.TOTAL  QUERY.FP  QUERY.UNK  FP.gt  FP.al  METRIC.Recall  METRIC.Precision  METRIC.Frac_NA  METRIC.F1_Score  TRUTH.TOTAL.TiTv_ratio  QUERY.TOTAL.TiTv_ratio  TRUTH.TOTAL.het_hom_ratio  QUERY.TOTAL.het_hom_ratio
INDEL    ALL         2731      2290       441         3092       208        577     62     53       0.838521          0.917296        0.186611         0.876141                     NaN                     NaN                   1.505145                   1.888993
INDEL   PASS         2731      2290       441         3092       208        577     62     53       0.838521          0.917296        0.186611         0.876141                     NaN                     NaN                   1.505145                   1.888993
  SNP    ALL        37997     34481      3516        36861       306       2074     33     13       0.907466          0.991204        0.056265         0.947488                2.611269                2.565915                   1.555780                   1.621727
  SNP   PASS        37997     34481      3516        36861       306       2074     33     13       0.907466          0.991204        0.056265         0.947488                2.611269                2.5659
***** TODO Refaire : HiSeq4000 + agilent sureselect + génome "prêt à l'emploi"
SCHEDULED: <2023-07-23 Sun>
**** TODO HG004 :hg38:hg004:
#+begin_src sh
NXF_OPTS=-D"user.name=${USER}" nextflow run main.nf -profile standard,helios  --input /Work/Groups/bisonex/data/giab/GRCh38/HG004_{1,2}.fq.gz -bg
#+end_src
vcfeval
Threshold  True-pos-baseline  True-pos
-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    6.000              36938          36678        421       4040     0.9887       0.9014     0.9430
     None              36942          36682        432       4036     0.9884       0.9015     0.9429
happy
 Type Filter  TRUTH.TOTAL  TRUTH.TP  TRUTH.FN  QUERY.TOTAL  QUERY.FP  QUERY.UNK  FP.gt  FP.al  METRIC.Recall  METRIC.Precision  METRIC.Frac_NA  METRIC.F1_Score  TRUTH.TOTAL.TiTv_ratio  QUERY.TOTAL.TiTv_ratio  TRUTH.TOTAL.het_hom_ratio  QUERY.TOTAL.het_hom_ratio
INDEL    ALL         2787      2388       399         3183       195        580     53     38       0.856835          0.925086        0.182218         0.889654                     NaN                     NaN                   1.507834                   1.848649
INDEL   PASS         2787      2388       399         3183       195        580     53     38       0.856835          0.925086        0.182218         0.889654                     NaN                     NaN                   1.507834                   1.848649
  SNP    ALL        38185     34560      3625        36921       254       2107     46      7       0.905067          0.992704        0.057068         0.946862                2.589175                2.553546                   1.632595                   1.653534
  SNP   PASS        38185     34560      3625        36921       254       2107     46      7       0.905067          0.992704        0.057068         0.946862                2.589175                2.553546                   1.632595                   1.653534
***** TODO Refaire : HiSeq4000 + agilent sureselect + génome "prêt à l'emploi"
SCHEDULED: <2023-07-23 Sun>
**** STRT HG001 :hg001:T2T:
SCHEDULED: <2023-07-03 Mon>
Avec liftover : 10x moins de variants...
Type,Filter,TRUTH.TOTAL,TRUTH.TP,TRUTH.FN,QUERY.TOTAL,QUERY.FP,QUERY.UNK,FP.gt,FP.al,METRIC.Recall,METRIC.Precision,METRIC.Frac_NA,METRIC.F1_Score,TRUTH.TOTAL.TiTv_ratio,QUERY.TOTAL.TiTv_ratio,TRUTH.TOTAL.het_hom_ratio,QUERY.TOTAL.het_hom_ratio
INDEL,ALL,413,246,167,751,289,215,2,93,0.595642,0.460821,0.286285,0.519629,,,2.4285714285714284,2.4651162790697674
INDEL,PASS,413,246,167,751,289,215,2,93,0.595642,0.460821,0.286285,0.519629,,,2.4285714285714284,2.4651162790697674
SNP,ALL,11236,10985,251,23597,9771,2841,26,58,0.977661,0.529245,0.120397,0.686734,3.1146100329549617,2.857049501715406,3.640644361833953,2.1146328578975173
SNP,PASS,11236,10985,251,23597,9771,2841,26,58,0.977661,0.529245,0.120397,0.686734,3.1146100329549617,2.857049501715406,3.640644361833953,2.1146328578975173
**** TODO HG002 :hg002:T2T:
**** TODO HG003 :hg003:T2T:
**** TODO HG004 :hg004:T2T:
**** TODO Résumer résultats pour Paul + article :resultats:hg38:
SCHEDULED: <2023-07-29 Sat>
Refaire résultats
**** TODO Plot : ashkenazim trio :hg38:
SCHEDULED: <2023-07-29 Sat>
/Entered on/ [2023-04-16 Sun 17:29]
Refaire résultats
*** KILL Platinum genome
CLOSED: [2023-06-14 Wed 22:37]
https://emea.illumina.com/platinumgenomes.html
*** TODO Séquencer NA12878 :cento:hg001:
Discussion avec Paul : sous-traitant ne nous donnera pas les données, il faut commander l'ADN
**** DONE ADN commandé
CLOSED: [2023-06-30 Fri 22:29]
**** TODO Sauvegarder les données brutes
SCHEDULED: <2023-07-19 Wed>
K, scality, S
**** TODO Récupérer le fichier de capture
SCHEDULED: <2023-07-23 Sun>
Par défaut, on utilisera https://www.twistbioscience.com/products/ngs/alliance-panels#tab-3
ANnonce récente pour nouveau panel Twist : https://www.centogene.com/news-events/news/newsdetails/twist-bioscience-and-centogene-launch-three-panels-to-advance-rare-disease-and-hereditary-cancer-research-and-support-diagnostics
Masi pas de fichier BED
***** TODO Mail centogène
DEADLINE: <2023-07-23 Sun>
**** STRT Comparer à GIAB
SCHEDULED: <2023-07-18 Tue>
#+begin_src sh
nextflow run main.nf -profile standard,helios --input="/Work/Groups/bisonex/centogene/2300346867_63118093_NA12878/63118093_S260_R{1,2}_001.fastq.gz"  --id=2300346867_63118093_NA12878-GRCh38 --genome=GRCh38 -bg
#+end_src
** TODO Insilico :cento:
*** TODO tous les variants centogène
**** DONE Extraire liste des SNVs
CLOSED: [2023-04-22 Sat 17:32] SCHEDULED: <2023-04-17 Mon>
***** DONE Corriger manquant à la main
CLOSED: [2023-04-22 Sat 17:31]
La sortie est sauvegardé dans git-annex : variants_success.csv
***** DONE Automatique
CLOSED: [2023-04-22 Sat 17:31]
**** DONE Convert SNVs : transcript -> génomique
CLOSED: [2023-06-03 Sat 17:16]
***** DONE Variant_recoder
CLOSED: [2023-04-26 Wed 21:21] SCHEDULED: <2023-04-22 Sat>
****** KILL Haskell: 160 manquant : recoded-success.csv
CLOSED: [2023-04-25 Tue 18:32]
La liste des variants a été générée en Haskel   l et nettoyée à la main.
On générer une liste de variant pour variant_rec            oder et on soumet tout d'un coup.
[[file:~/recherche/bisonex/parsevariants/app/Main.hs][parsevariant]]
#+begin_src haskell
recodeVariant = do
  prepareVariantRecod   er "variant_success.csv" "renamed.csv"
  runVariantRecoder "renamed.csv" "recoded.json"
#+end_src
#+RESULTS:
: <interactive>:4:3-19: error:
:     Variable not in scope: runVariantRecoder :: String -> String -> t
: gh
Problème : 160 n'ont pas pu être lu sur 820, probablement à cause du numéro mineur de transcrit
La sortie est sauvegardé dans git-annex : variants-recoded-raw.json.
****** KILL Julia
CLOSED: [2023-04-25 Tue 18:32]
On regénère la liste de variant et on passe à Julia pour préparer l'appel en parallèle à variant recoder
[[file:~/recherche/bisonex/parsevariants/variantRecoder.jl][variantRecoder.jl]]
#+begin_src julia
setupVariantRecoder(unique(init), n)
#+end_src
Puis
#+begin_src sh
parallel -a parallel-recoder.sh --jobs 10
#+end_src
On récupère les résultats
#+begin_src julia
(fails, success) = mergeVariantRecoder(n)
CSV.write(fSuccess, success)
CSV.write(fFailures, fails)
#+end_src
Certains variants ne sont pas trouvé, donc on prépare un nouveau job en enlevant les versionrs mineures des transcrits
#+begin_src julia
# Cleanup json and txt
if isfile(fSuccess) && isfile(fFailures)
    foreach(rm, variantRecoderInput())
    foreach(rm, variantRecoderOutput())
end
redoFails(fFailures)
#+end_src
Puis
#+begin_src sh
parallel -a parallel-recoder.sh --jobs 3
#+end_src
Il manque encore 70 transcrits
***** DONE Julia avec mobidetails: recode-failures-mobidetails.csv
CLOSED: [2023-04-25 Tue 18:58]
Nouvelle stratégie : on essaie une fois variant recoder.
Pour tous les échecs, on utilise mobidetails (~170).
Si l'ID n'est pas trouvé, on incrémente le numéro de version 2 fois
***** DONE Reste une dizaine à corriger à la main
CLOSED: [2023-04-26 Wed 21:21]
- [X] certains transcrits ont juste été supprimé
- [X] Erreur de parsing, manque souvent un -
#+begin_src julia
lastTryMobidetails("recoded-failures-mobidetails.csv")
#+end_src
***** DONE Fusionner données
CLOSED: [2023-04-26 Wed 22:35]
#+begin_src julia
function mergeAllGenomic()
    dNew = mergeAll("recoded-success.csv",
                    "recoded-failures-mobideta

[100.1239]

[101.7618]


| Threshold | True-pos-baseline | True-pos-call | False-pos | False-neg | Precision | Sensitivity | F-measure |
|-----------+-------------------+---------------+-----------+-----------+-----------+-------------+-----------|
| 3.000     |             42789 |         42416 |      2598 |      8080 |    0.9423 |      0.8412 |    0.8889 |
| None      |             42798 |         42425 |      2616 |      8071 |    0.9419 |      0.8413 |    0.8888 |
Indel avec le plus petit seuil : zcat NA12878.non_snp_roc.tsv.gz
Attention à inverser precision et recall !
 zcat NA12878.non_snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $6}'
0.71390.7136
SNP avec le plus petit seuil : zcat NA12878.non_snp_roc.tsv.gz
Attention à inverser precision et recall !
$ zcat NA12878.snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $6}'
0.85470.9727
compareNA12878-giab/vcfeval/NA12878.summary.txt
| Threshold | True-pos-baseline | True-pos-call | False-pos | False-neg | Precision | Sensitivity | F-measure |
| 1.000     |             44812 |         44812 |      2878 |      6057 |    0.9397 |      0.8809 |    0.9093 |
| None      |             44813 |         44813 |      2882 |      6056 |    0.9396 |      0.8809 |    0.9093 |
SNP:
$ zcat NA12878.snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $6}'
0.89370.9621
indel
$ zcat NA12878.non_snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $6}'
0.75980.7445
compareNA12878-giab/happy/NA12878.summary.csv
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
|-------+--------+-------------+----------+----------+-------------+----------+-----------+-------+-------+---------------+------------------+----------------+-----------------+------------------------+------------------------+---------------------------+---------------------------|
| INDEL | ALL    |        4871 |     3678 |     1193 |        7036 |     1299 |      2011 |   208 |   217 |      0.755081 |         0.741493 |       0.285816 |        0.748225 |                        |                        |        1.6174985978687606 |        2.5240506329113925 |
| INDEL | PASS   |        4871 |     3678 |     1193 |        7036 |     1299 |      2011 |   208 |   217 |      0.755081 |         0.741493 |       0.285816 |        0.748225 |                        |                        |        1.6174985978687606 |        2.5240506329113925 |
| SNP   | ALL    |       46032 |    41138 |     4894 |       47694 |     1622 |      4930 |   362 |    31 |      0.893683 |         0.962071 |       0.103367 |        0.926617 |      2.529551552318896 |     2.4124463519313304 |        1.6206857273037931 |        1.6888675840288743 |
| SNP   | PASS   |       46032 |    41138 |     4894 |       47694 |     1622 |      4930 |   362 |    31 |      0.893683 |         0.962071 |       0.103367 |        0.926617 |      2.529551552318896 |     2.4124463519313304 |        1.6206857273037931 |         1.688867584028874 |
***** KILL Résultats sans trimming
CLOSED: [2023-06-25 Sun 15:53] SCHEDULED: <2023-06-26 Mon>
***** DONE Refaire : HiSeq4000 + agilent sureselect + génome "prêt à l'emploi"
CLOSED: [2023-06-30 Fri 22:08] SCHEDULED: <2023-06-25 Sun>
#+begin_src
nextflow run workflows/compareVCF.nf -profile standard,helios --outdir=out/HG001-SRX11061486_SRR14724513-GRCh38 --query=out/HG001-SRX11061486_SRR14724513-GRCh38/callVariant/haplotypecaller/HG001-SRX11061486_SRR14724513-GRCh38.vcf.gz --compare=vcfeval,happy -lib lib --capture=capture/Agilent_SureSelect_All_Exons_v7_hg38_Regions.bed  --id=HG001
#+end_src
Meilleurs résultats !
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |       0.89071 |          0.88551 |       0.378198 |        0.888102 |                        |                        |          1.86096256684492 |         2.247272727272727 |
| INDEL | PASS   |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |       0.89071 |          0.88551 |       0.378198 |        0.888102 |                        |                        |          1.86096256684492 |         2.247272727272727 |
| SNP   | ALL    |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |       0.162184 |        0.975588 |      3.007110300820419 |       2.78468624064479 |        1.5918102430965306 |        1.8161449399656946 |
| SNP   | PASS   |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |       0.162184 |        0.975588 |      3.007110300820419 |       2.78468624064479 |        1.5918102430965306 |        1.8161449399656946 |
***** KILL Utiliser d'autres données brutes ?
CLOSED: [2023-06-25 Sun 15:58]
https://zenodo.org/record/3597727
Capture en hg37 également. Serait intéressant mais pas le temps..
***** KILL Comparer avec UCSCS liftover
CLOSED: [2023-06-26 Mon 19:02] SCHEDULED: <2023-06-25 Sun>
Picard liftoverinterval est basé sur UCSCS
Mais on n'aurait pas la différence pour NA12878 qu'on voit...
**** DONE HG002 :hg002:hg38:
CLOSED: [2023-07-30 Sun 14:25] SCHEDULED: <2023-07-25 Tue>
#+begin_src
    NXF_OPTS=-D"user.name=${USER}" nextflow run workflows/giabFastq.nf -profile standard,helios
    NXF_OPTS=-D"user.name=${USER}" nextflow run main.nf -profile standard,helios -resume --input="/Work/Groups/bisonex/data/giab/GRCh38/HG002_{1,2}.fq.gz --test.id=HG002
Only the capture file differs. Results are better using the capture file given by Agilent, stored in data/
    NXF_OPTS=-D"user.name=${USER}" nextflow run workflows/compareVCF.nf -profile standard,helios -resume --outdir=compareHG002 --test.id=HG002 --test.query=out/HG002_1/variantCalling/haplotypecaller/HG002_1.vcf.gz  --test.compare=vcfeval,happy --test.capture=data/AgilentSureSelectv05_hg38.bed
#
#+end_src
***** DONE Mauvais résultats
CLOSED: [2023-04-14 Fri 09:42]
avec vcfeval
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    0.000              24585          24390      10060      39415     0.7080       0.3841     0.4980
     None              24585          24390      10060      39415     0.7080       0.3841     0.4980
La sortie du variantCalling est celle d'happy ???
On relance...
***** DONE Vérifier vcf en hg38
CLOSED: [2023-04-12 Wed 10:33] SCHEDULED: <2023-04-12 Wed>
***** KILL Capture en hg19 ?
CLOSED: [2023-04-13 Thu 09:46] SCHEDULED: <2023-04-12 Wed>
***** KILL Vraiment fichier de capture ou zone d'intérêt ?
CLOSED: [2023-04-13 Thu 09:45] SCHEDULED: <2023-04-12 Wed>
"target region" +/- 50bp
[[https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/OsloUniversityHospital_Exome_GATK_jointVC_11242015/README.txt][README]]
 list file describing the variant calling regions (target regions extended with 50 bp on each end)
***** DONE .bed fourni par AGilent: sensbilité très mauvaise
CLOSED: [2023-04-13 Thu 09:46] SCHEDULED: <2023-04-13 Thu>
Agilent SureSelect Human All Exon V5 kit
Disponible en hg38
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    0.000              19653          19501       6410      21657     0.7526       0.4757     0.5830
     None              19653          19501       6410      21657     0.7526       0.4757     0.5830
***** DONE Trier par nom avec samtools sort : bons résultats
CLOSED: [2023-04-14 Fri 09:25] SCHEDULED: <2023-04-13 Thu>
Avec capture fourni par GIAB
vcf eval
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    5.000              57443          57032        984       6557     0.9830       0.8975     0.9383
     None              57457          57046       1009       6543     0.9826       0.8978     0.9383
Happy
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
|-------+--------+-------------+----------+----------+-------------+----------+-----------+-------+-------+---------------+------------------+----------------+-----------------+------------------------+------------------------+---------------------------+---------------------------|
| INDEL | ALL    |        6150 |     5007 |     1143 |        6978 |      556 |      1346 |   151 |   168 |      0.814146 |         0.901278 |       0.192892 |          0.8555 |                        |                        |        1.5434221840068787 |        1.9467178175618074 |
| INDEL | PASS   |        6150 |     5007 |     1143 |        6978 |      556 |      1346 |   151 |   168 |      0.814146 |         0.901278 |       0.192892 |          0.8555 |                        |                        |        1.5434221840068787 |        1.9467178175618074 |
| SNP   | ALL    |       57818 |    52464 |     5354 |       56016 |      500 |      3046 |    90 |    30 |      0.907399 |         0.990561 |       0.054377 |        0.947158 |     2.4892012548262548 |      2.426824047458871 |        1.5904527117884357 |        1.6107795598657217 |
| SNP   | PASS   |       57818 |    52464 |     5354 |       56016 |      500 |      3046 |    90 |    30 |      0.907399 |         0.990561 |       0.054377 |        0.947158 |     2.4892012548262548 |      2.426824047458871 |        1.5904527117884357 |        1.6107795598657217 |
***** DONE Capture agilent légment meilleur que celui fourni par GIAB (padding ?)
CLOSED: [2023-04-14 Fri 09:48]
GIAB:
vcf eval
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    5.000              57443          57032        984       6557     0.9830       0.8975     0.9383
     None              57457          57046       1009       6543     0.9826       0.8978     0.9383
Happy
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
|-------+--------+-------------+----------+----------+-------------+----------+-----------+-------+-------+---------------+------------------+----------------+-----------------+------------------------+------------------------+---------------------------+---------------------------|
| INDEL | ALL    |        6150 |     5007 |     1143 |        6978 |      556 |      1346 |   151 |   168 |      0.814146 |         0.901278 |       0.192892 |          0.8555 |                        |                        |        1.5434221840068787 |        1.9467178175618074 |
| INDEL | PASS   |        6150 |     5007 |     1143 |        6978 |      556 |      1346 |   151 |   168 |      0.814146 |         0.901278 |       0.192892 |          0.8555 |                        |                        |        1.5434221840068787 |        1.9467178175618074 |
| SNP   | ALL    |       57818 |    52464 |     5354 |       56016 |      500 |      3046 |    90 |    30 |      0.907399 |         0.990561 |       0.054377 |        0.947158 |     2.4892012548262548 |      2.426824047458871 |        1.5904527117884357 |        1.6107795598657217 |
| SNP   | PASS   |       57818 |    52464 |     5354 |       56016 |      500 |      3046 |    90 |    30 |      0.907399 |         0.990561 |       0.054377 |        0.947158 |     2.4892012548262548 |      2.426824047458871 |        1.5904527117884357 |        1.6107795598657217 |
Agilent
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    6.000              37241          36965        449       4069     0.9880       0.9015     0.9428
     None              37248          36972        461       4062     0.9877       0.9017     0.9427
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |        2909 |     2477 |      432 |        3229 |      207 |       519 |    52 |    50 |      0.851495 |         0.923616 |       0.160731 |        0.886091 |                        |                        |        1.4964850615114236 |        1.8339222614840989 |
| INDEL | PASS   |        2909 |     2477 |      432 |        3229 |      207 |       519 |    52 |    50 |      0.851495 |         0.923616 |       0.160731 |        0.886091 |                        |                        |        1.4964850615114236 |        1.8339222614840989 |
| SNP   | ALL    |       38406 |    34793 |     3613 |       36935 |      275 |      1868 |    37 |    15 |      0.905926 |         0.992158 |       0.050575 |        0.947083 |     2.6247759222568168 |     2.5752854654538417 |         1.588953331534934 |        1.6192536889897844 |
| SNP   | PASS   |       38406 |    34793 |     3613 |       36935 |      275 |      1868 |    37 |    15 |      0.905926 |         0.992158 |       0.050575 |        0.947083 |     2.6247759222568168 |     2.5752854654538417 |         1.588953331534934 |        1.6192536889897844 |
***** DONE Refaire : HiSeq4000 + agilent sureselect + génome "prêt à l'emploi"
CLOSED: [2023-07-30 Sun 14:24] SCHEDULED: <2023-07-23 Sun>
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |         626 |      536 |       90 |         900 |       23 |       334 |     6 |    14 |       0.85623 |         0.959364 |       0.371111 |        0.904868 |                        |                        |        1.5696202531645569 |         2.406844106463878 |
| INDEL | PASS   |         626 |      536 |       90 |         900 |       23 |       334 |     6 |    14 |       0.85623 |         0.959364 |       0.371111 |        0.904868 |                        |                        |        1.5696202531645569 |         2.406844106463878 |
| SNP   | ALL    |       23136 |    22346 |      790 |       26289 |      103 |      3845 |    77 |     4 |      0.965854 |         0.995411 |       0.146259 |         0.98041 |     2.9282199219412863 |     2.7752583237657866 |        1.6348301800775018 |        1.8423330808354075 |
| SNP   | PASS   |       23136 |    22346 |      790 |       26289 |      103 |      3845 |    77 |     4 |      0.965854 |         0.995411 |       0.146259 |         0.98041 |     2.9282199219412863 |     2.7752583237657866 |        1.6348301800775018 |        1.8423330808354075 |
**** DONE HG003 :hg003:hg38:
CLOSED: [2023-07-30 Sun 14:26]
***** Notes
#+begin_src sh
NXF_OPTS=-D"user.name=${USER}" nextflow run main.nf -profile standard,helios  --input /Work/Groups/bisonex/data/giab/GRCh38/HG003_{1,2}.fq.gz -bg
#+end_src
#+begin_src  sh
NXF_OPTS=-D"user.name=${USER}" nextflow run workflows/compareVCF.nf -profile standard,helios -resume --outdir=compareHG003  --test.id=HG003 --test.query=out/HG003_1/variantCalling/haplotypecaller/HG003_1.vcf.gz  --test.compare=vcfeval,happy --test.capture=data/AgilentSureSelectv05_hg38.bed
#+end_src
vcfeval
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    5.000              36745          36473        486       3988     0.9869       0.9021     0.9426
     None              36748          36476        495       3985     0.9866       0.9022     0.9425
$ zcat NA12878.snp_roc.tsv.gz  | tail -n 1 | awk '{print $7 $6}'
happy
Type Filter  TRUTH.TOTAL  TRUTH.TP  TRUTH.FN  QUERY.TOTAL  QUERY.FP  QUERY.UNK  FP.gt  FP.al  METRIC.Recall  METRIC.Precision  METRIC.Frac_NA  METRIC.F1_Score  TRUTH.TOTAL.TiTv_ratio  QUERY.TOTAL.TiTv_ratio  TRUTH.TOTAL.het_hom_ratio  QUERY.TOTAL.het_hom_ratio
INDEL    ALL         2731      2290       441         3092       208        577     62     53       0.838521          0.917296        0.186611         0.876141                     NaN                     NaN                   1.505145                   1.888993
INDEL   PASS         2731      2290       441         3092       208        577     62     53       0.838521          0.917296        0.186611         0.876141                     NaN                     NaN                   1.505145                   1.888993
  SNP    ALL        37997     34481      3516        36861       306       2074     33     13       0.907466          0.991204        0.056265         0.947488                2.611269                2.565915                   1.555780                   1.621727
  SNP   PASS        37997     34481      3516        36861       306       2074     33     13       0.907466          0.991204        0.056265         0.947488                2.611269                2.5659
***** DONE Refaire : HiSeq4000 + agilent sureselect + génome "prêt à l'emploi"
CLOSED: [2023-07-30 Sun 14:25] SCHEDULED: <2023-07-23 Sun>
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |         644 |      538 |      106 |         914 |       32 |       337 |     7 |    19 |      0.835404 |         0.944541 |       0.368709 |        0.886626 |                        |                        |        1.7444933920704846 |        2.3138686131386863 |
| INDEL | PASS   |         644 |      538 |      106 |         914 |       32 |       337 |     7 |    19 |      0.835404 |         0.944541 |       0.368709 |        0.886626 |                        |                        |        1.7444933920704846 |        2.3138686131386863 |
| SNP   | ALL    |       23126 |    22271 |      855 |       26405 |      135 |      4002 |    90 |    20 |      0.963029 |         0.993974 |       0.151562 |        0.978257 |      2.949462182004439 |     2.7766657134686876 |        1.6080333972695475 |        1.8465106245280984 |
| SNP   | PASS   |       23126 |    22271 |      855 |       26405 |      135 |      4002 |    90 |    20 |      0.963029 |         0.993974 |       0.151562 |        0.978257 |      2.949462182004439 |     2.7766657134686876 |        1.6080333972695475 |        1.8465106245280984 |
**** TODO HG004 :hg38:hg004:
#+begin_src sh
NXF_OPTS=-D"user.name=${USER}" nextflow run main.nf -profile standard,helios  --input /Work/Groups/bisonex/data/giab/GRCh38/HG004_{1,2}.fq.gz -bg
#+end_src
vcfeval
Threshold  True-pos-baseline  True-pos-call  False-pos  False-neg  Precision  Sensitivity  F-measure
----------------------------------------------------------------------------------------------------
    6.000              36938          36678        421       4040     0.9887       0.9014     0.9430
     None              36942          36682        432       4036     0.9884       0.9015     0.9429
happy
 Type Filter  TRUTH.TOTAL  TRUTH.TP  TRUTH.FN  QUERY.TOTAL  QUERY.FP  QUERY.UNK  FP.gt  FP.al  METRIC.Recall  METRIC.Precision  METRIC.Frac_NA  METRIC.F1_Score  TRUTH.TOTAL.TiTv_ratio  QUERY.TOTAL.TiTv_ratio  TRUTH.TOTAL.het_hom_ratio  QUERY.TOTAL.het_hom_ratio
INDEL    ALL         2787      2388       399         3183       195        580     53     38       0.856835          0.925086        0.182218         0.889654                     NaN                     NaN                   1.507834                   1.848649
INDEL   PASS         2787      2388       399         3183       195        580     53     38       0.856835          0.925086        0.182218         0.889654                     NaN                     NaN                   1.507834                   1.848649
  SNP    ALL        38185     34560      3625        36921       254       2107     46      7       0.905067          0.992704        0.057068         0.946862                2.589175                2.553546                   1.632595                   1.653534
  SNP   PASS        38185     34560      3625        36921       254       2107     46      7       0.905067          0.992704        0.057068         0.946862                2.589175                2.553546                   1.632595                   1.653534
***** DONE Refaire : HiSeq4000 + agilent sureselect + génome "prêt à l'emploi"
CLOSED: [2023-07-30 Sun 14:39] SCHEDULED: <2023-07-23 Sun>
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |         588 |      511 |       77 |         873 |       15 |       332 |     7 |     8 |      0.869048 |         0.972274 |       0.380298 |        0.917767 |                        |                        |        1.6111111111111112 |                 2.3984375 |
| INDEL | PASS   |         588 |      511 |       77 |         873 |       15 |       332 |     7 |     8 |      0.869048 |         0.972274 |       0.380298 |        0.917767 |                        |                        |        1.6111111111111112 |                 2.3984375 |
| SNP   | ALL    |       23041 |    22225 |      816 |       26302 |      114 |      3972 |    87 |     5 |      0.964585 |         0.994895 |       0.151015 |        0.979505 |       2.91407709288504 |     2.7368645271229766 |        1.6883829538820783 |        1.8632748665431964 |
| SNP   | PASS   |       23041 |    22225 |      816 |       26302 |      114 |      3972 |    87 |     5 |      0.964585 |         0.994895 |       0.151015 |        0.979505 |       2.91407709288504 |     2.7368645271229766 |        1.6883829538820783 |        1.8632748665431964 |
**** STRT HG001 :hg001:T2T:
Avec liftover : 10x moins de variants...
Type,Filter,TRUTH.TOTAL,TRUTH.TP,TRUTH.FN,QUERY.TOTAL,QUERY.FP,QUERY.UNK,FP.gt,FP.al,METRIC.Recall,METRIC.Precision,METRIC.Frac_NA,METRIC.F1_Score,TRUTH.TOTAL.TiTv_ratio,QUERY.TOTAL.TiTv_ratio,TRUTH.TOTAL.het_hom_ratio,QUERY.TOTAL.het_hom_ratio
INDEL,ALL,413,246,167,751,289,215,2,93,0.595642,0.460821,0.286285,0.519629,,,2.4285714285714284,2.4651162790697674
INDEL,PASS,413,246,167,751,289,215,2,93,0.595642,0.460821,0.286285,0.519629,,,2.4285714285714284,2.4651162790697674
SNP,ALL,11236,10985,251,23597,9771,2841,26,58,0.977661,0.529245,0.120397,0.686734,3.1146100329549617,2.857049501715406,3.640644361833953,2.1146328578975173
SNP,PASS,11236,10985,251,23597,9771,2841,26,58,0.977661,0.529245,0.120397,0.686734,3.1146100329549617,2.857049501715406,3.640644361833953,2.1146328578975173
***** TODO Comprendre mauvais résultat
****** DONE 2x moins de variants, beaucoup trop de FP : erreur de FILTER
CLOSED: [2023-07-08 Sat 11:17] SCHEDULED: <2023-07-07 Fri>
******* DONE [#A] Certains FP sont des vrais FP: erreur de flag et non d'happy
CLOSED: [2023-07-08 Sat 10:43] SCHEDULED: <2023-07-07 Fri>
******** Ex: chr1:408793
G	C	600.64	.	BS=408793;Regions=CONF,TS_contained	GT:BD:BK:BI:BVT:BLT:QQ	./.:.:.:.:NOCALL:nocall:.	0/1:FP:.:tv:SNP:het:600.64
CLOSED: [2023-07-07 Fri 20:03]
********* DONE Vérifier que l'on est bien dans la zone "high confidence" définie dans le bed
oui:
chr1	408514	414397	.	500	+
********* DONE Vérifier que le variant est bien dans le truth et query
CLOSED: [2023-07-07 Fri 20:14]
truth
chr1    408793  .       G       C       50      MismatchedRefAllele     AttemptedAlleles=C*->G;AttemptedLocus=chr1:408793-408793;SwappedAlleles;callable=CS_HiSeqPE300xGATK_callable,CS_10XLRGATK_callable,CS_CCS15kb_20kbGATK4_callable,CS_CGnormal_callable,CS_HiSeqPE300xfreebayes_callable;callsetnames=HiSeqPE300xGATK,CCS15kb_20kbGATK4,CGnormal,HiSeqPE300xfreebayes,CCS15kb_20kbDV,10XLRGATK;callsets=6;datasetnames=HiSeqPE300x,CCS15kb_20kb,CGnormal,10XChromiumLR;datasets=4;datasetsmissingcall=IonExome,SolidSE75bp;filt=CS_CCS15kb_20kbDV_filt,CS_CCS15kb_20kbGATK4_filt;platformnames=Illumina,PacBio,CG,10X;platforms=4       GT:AD:ADALL:DP:GQ       1/0:153,159:141,132:689:99
query
chr1    408793  .       G       C       600.64  .       AC=1;AF=0.500;AN=2;BaseQRankSum=-1.390;DP=64;ExcessHet=0.0000;FS=0.961;MLEAC=1;MLEAF=0.500;MQ=60.00;MQRankSum=0.000;QD=9.53;ReadPosRankSum=-3.146;SOR=0.906     GT:AD:DP:GQ:PL  0/1:33,30:63:99:608,0,798
********* DONE Tester happy sur ce variant: non retrouvé
CLOSED: [2023-07-08 Sat 10:42]
Il faut au moins 2 variant + les index (sinon "no chromosome in common")
Donc on augmente la taille de la région
#+begin_src sh :dir ~/code/bisonex/out
bcftools view HG001_GRCh38_1_22_v4_lifted_merged.vcf.gz  -o lifted_test.vcf.gz chr1:408793-408900
bcftools view HG001-SRX11061486_SRR14724513-T2T.vcf.gz -o run_test.vcf.gz chr1:408793-408900
bcftools index lifted_test.vcf.gz
bcftools index run_test.vcf.gz
#+end_src
 #+begin_src sh
 hap.py lifted_test.vcf.gz run_test.vcf.gz --reference ../data/chm13v2.0.fa -o test
 #+end_src
On trouve bien les 2 variants. L'indel est retrouvé mais pas le SNP
chr1    408793  .       G       C       600.64  .       BS=408793       GT:BD:BK:BI:BVT:BLT:QQ  ./.:.:.:.:NOCALL:nocall:.       0/1:FP:.:tv:SNP:het:600.64
chr1    408874  .       CGAA    C       1476.6  .       BS=408874       GT:BD:BK:BI:BVT:BLT:QQ  0/1:TP:gm:d1_5:INDEL:het:1476.6 0/1:TP:gm:d1_5:INDEL:het:1476.6
********* DONE Changer flag : mismatch -> PASS: OUI
CLOSED: [2023-07-08 Sat 10:42]
********* DONE Changer haplotype 1/0 -> 0/1 truth: idem
CLOSED: [2023-07-08 Sat 10:35]
********* DONE Tester avec vcfeval : idemnt
CLOSED: [2023-07-08 Sat 10:43]
******* DONE Comparer hg38 et T2T: 2x moinsr de variants, trop de FP et FN
CLOSED: [2023-07-07 Fri 18:38]
T2T
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |         413 |      246 |      167 |         751 |      289 |       215 |     2 |    93 |      0.595642 |         0.460821 |       0.286285 |        0.519629 |                    NaN |                    NaN |                  2.428571 |                  2.465116 |
| INDEL | PASS   |         413 |      246 |      167 |         751 |      289 |       215 |     2 |    93 |      0.595642 |         0.460821 |       0.286285 |        0.519629 |                    NaN |                    NaN |                  2.428571 |                  2.465116 |
| SNP   | ALL    |       11236 |    10985 |      251 |       23597 |     9771 |      2841 |    26 |    58 |      0.977661 |         0.529245 |       0.120397 |        0.686734 |                3.11461 |                2.85705 |                  3.640644 |                  2.114633 |
| SNP   | PASS   |       11236 |    10985 |      251 |       23597 |     9771 |      2841 |    26 |    58 |      0.977661 |         0.529245 |       0.120397 |        0.686734 |                3.11461 |                2.85705 |                  3.640644 |                  2.114633 |
Hg38
| Type  | Filter | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision | METRIC.Frac_NA | METRIC.F1_Score | TRUTH.TOTAL.TiTv_ratio | QUERY.TOTAL.TiTv_ratio | TRUTH.TOTAL.het_hom_ratio | QUERY.TOTAL.het_hom_ratio |
| INDEL | ALL    |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |      0.890710 |         0.885510 |       0.378198 |        0.888102 |                    NaN |                    NaN |                  1.860963 |                  2.247273 |
| INDEL | PASS   |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |      0.890710 |         0.885510 |       0.378198 |        0.888102 |                    NaN |                    NaN |                  1.860963 |                  2.247273 |
| SNP   | ALL    |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |       0.162184 |        0.975588 |                3.00711 |               2.784686 |                  1.591810 |                  1.816145 |
| SNP   | PASS   |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |       0.162184 |        0.975588 |                3.00711 |               2.784686 |                  1.591810 |                  1.816145 |
******** Résumé
T2T
| Type  | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision |
| INDEL |         413 |      246 |      167 |         751 |      289 |       215 |     2 |    93 |      0.595642 |         0.460821 |
| SNP   |       11236 |    10985 |      251 |       23597 |     9771 |      2841 |    26 |    58 |      0.977661 |         0.529245 |
Hg38
| Type  | TRUTH.TOTAL | TRUTH.TP | TRUTH.FN | QUERY.TOTAL | QUERY.FP | QUERY.UNK | FP.gt | FP.al | METRIC.Recall | METRIC.Precision |
| INDEL |         549 |      489 |       60 |         899 |       64 |       340 |     8 |    17 |      0.890710 |         0.885510 |
| SNP   |       21973 |    21462 |      511 |       26285 |      563 |      4263 |    68 |    16 |      0.976744 |         0.974435 |
****** DONE Interesection des bed: similaire
CLOSED: [2023-07-04 Tue 23:11]
HG38
 #+begin_src sh
 bedtools intersect -a capture/Agilent_SureSelect_All_Exons_v7_hg38_Regions.bed -b /Work/Groups/bisonex/data/giab/GRCh38/HG001_GRCh38_1_22_v4.2.1_benchmark.bed  | wc -l
 #+end_src
 204280
 T2T
 #+begin_src sh
 bedtools intersect -a /Work/Groups/bisonex/data/giab/T2T/Agilent_SureSelect_All_Exons_v7_hg38_Regions_hg38_T2T.bed -b /Work/Groups/bisonex/data/giab/T2T/HG001_GRCh38_1_22_v4.2.1_benchmark_hg38_T2T.bed  | wc -l
 #+end_src
 204021
****** DONE Vérifier la ligne de commande
CLOSED: [2023-07-04 Tue 23:38]
#+begin_src sh
hap.py \
    HG001_GRCh38_1_22_v4_lifted_merged.vcf.gz \
    HG001-SRX11061486_SRR14724513-T2T.vcf.gz \
     \
    --reference chm13v2.0.fa \
    --threads 6 \
     \
    -T Agilent_SureSelect_All_Exons_v7_hg38_Regions_hg38_T2T.bed \
    --false-positives HG001_GRCh38_1_22_v4.2.1_benchmark_hg38_T2T.bed \
     \
    -o HG001
#+end_src
****** DONE Corriger FILTER : mieux mais toujours trop de négatifs. 3/4 SNP retrouvés
CLOSED: [2023-07-08 Sat 15:19] SCHEDULED: <2023-07-08 Sat>
 Type Filter  TRUTH.TOTAL  TRUTH.TP  TRUTH.FN  QUERY.TOTAL  QUERY.FP  QUERY.UNK  FP.gt  FP.al  METRIC.Recall  METRIC.Precision  METRIC.Frac_NA  METRIC.F1_Score  TRUTH.TOTAL.TiTv_ratio  QUERY.TOTAL.TiTv_ratio  TRUTH.TOTAL.het_hom_ratio  QUERY.TOTAL.het_hom_ratio
INDEL    ALL          413       246       167          751       289        215      2     98       0.595642          0.460821        0.286285         0.519629                     NaN                     NaN                   2.428571                   2.465116
INDEL   PASS          413       246       167          751       289        215      2     98       0.595642          0.460821        0.286285         0.519629                     NaN                     NaN                   2.428571                   2.465116
  SNP    ALL        15883     15479       404        23597      5277       2841     46     44       0.974564          0.745760        0.120397         0.844947                3.017198                 2.85705                   5.560099                   2.114633
  SNP   PASS        15883     15479       404        23597      5277       2841     46     44       0.974564          0.745760        0.120397         0.844947                3.017198                 2.85705                   5.560099                   2.114633
******* DONE Vérifier qu'il ne reste plus de filtre autre que PASS
CLOSED: [2023-07-08 Sat 15:19]
#+begin_src
$ zgrep -c 'PASS' HG001_GRCh38_1_22_v4_lifted_merged.vcf.gz
3730505
$ zgrep -c '^chr' HG001_GRCh38_1_22_v4_lifted_merged.vcf.gz
3730506
#+end_src
****** TODO 1/4 SNP manquant ?
SCHEDULED: <2023-07-08 Sat>
******* DONE Regarder avec Julia si ce sont vraiment des FP: 61/5277 qui ne le sont pas
CLOSED: [2023-07-09 Sun 12:09]
******* HOLD Examiner les FP
******* HOLD Tester un FP
  2 │ chr1        608765  A           G           ./.:.:.:.:NOCALL:nocall:.  1/1:FP:.:ti:SNP:homalt:188
  liftDown UCSC: rien en GIAB : vrai FP
 3 │ chr1        762943  A           G           ./.:.:.:.:NOCALL:nocall:.  1/1:FP:.:ti:SNP:homalt:287
 4 │ chr1        762945  A           T           ./.:.:.:.:NOCALL:nocall:.  1/1:FP:.:tv:SNP:homalt:287
 Remaniements complexes ? Pas dans le gène en HG38
******* DONE La plupart des FP (4705/5566) sont homozygotes: erreur de référence ?
CLOSED: [2023-07-12 Wed 21:10] SCHEDULED: <2023-07-09 Sun>
Sur les 2 premiers variants, ils montrent en fait la différence entre T2T et GRCh38
Erreur à l'alignement ?
******** KILL relancer l'alignement
CLOSED: [2023-07-09 Sun 17:36]
******** DONE vérifier reads identiques hg38 et T2T: oui
CLOSED: [2023-07-09 Sun 16:36]
T2T CHR1608765
38   	chr1:1180168-1180168 (
SRR14724513.24448214
SRR14724513.24448214
******* TODO Enlever les FP qui correspondent à un changement dans le génome
******** Condition:
- pas de variation à la position en GRCh38
- variantion homozygote
- la varation en T2T correspond au changement de pair de base GRC38 -> T2T
  pour les SNP:
  alt_T2T[i] = DNA_GRC38[j]
  avec i la position en T2T et j la position en GRCh38
  Note: définir un ID n'est pas correct car les variants peuvent être modifié par happy !
******** Idée
 - Pour chaque FP, c'est un "faux" FP si
     - REF en hg38 == ALT en T2T
     - et REF en hg38 != REF en T2T
     - et variant homozygote
Comment obtenir les séquences de réferences ?
1. liftover
2. blat sur la séquence autour du variant
3. identifier quelques reads contenant le variant et regarder leur aligneement en hg38
Après discussion avec Alexis: solution 3
******** Algorithme
1. Extraire les coordonnées en T2T des faux positifs *homozygote*
2. Pour chaque faux positif
   1. lister 10 reads contenant le variant
   2. pour chacun de ces reads, récupérer la séquence en T2T et GRCh38 via le nom du read dans le bam
   3. si la séquence en T2T modifiée par le variant est "identique" à celle en GRCh38, alors on ignore ce faux positif
Note: on ignore les reads qui ont changé de chromosome entre les version
******** DONE Résultat préliminaire
CLOSED: [2023-07-23 Sun 14:30]
cf [[file:~/roam/research/bisonex/code/giab/giab-corrected.csv][script julia]]
3498 faux positifs en moins, soit 0.89 sensibilité
julia> tp=15479
julia> fp=5277
julia> tp/(tp+fp)
0.7457602620928888
julia> tp/(tp+(fp-3498))
0.8969173716537258
On est toujours en dessous des 97%
******** PROJ Corriger proprement VCF ou résultats Happy
******* DONE Vérifier quelques variants sur IGV
CLOSED: [2023-07-09 Sun 17:36]
******* KILL Répartition des FP : cluster ?
CLOSED: [2023-07-09 Sun 17:36]
****** TODO Méthodologie du pangenome
SCHEDULED: <2023-07-30 Sun>
***** KILL Mail Yannis
CLOSED: [2023-07-08 Sat 10:44]
***** DONE Mail GIAB pour version T2T
CLOSED: [2023-07-07 Fri 18:37]
**** TODO HG002 :hg002:T2T:
**** TODO HG003 :hg003:T2T:
**** TODO HG004 :hg004:T2T:
**** TODO Plot : ashkenazim trio :hg38:
SCHEDULED: <2023-07-29 Sat>
/Entered on/ [2023-04-16 Sun 17:29]
Refaire résultats
*** KILL Platinum genome
CLOSED: [2023-06-14 Wed 22:37]
https://emea.illumina.com/platinumgenomes.html
*** TODO Séquencer NA12878 :cento:hg001:
Discussion avec Paul : sous-traitant ne nous donnera pas les données, il faut commander l'ADN
**** DONE ADN commandé
CLOSED: [2023-06-30 Fri 22:29]
**** DONE Sauvegarder les données brutes
CLOSED: [2023-07-30 Sun 14:22] SCHEDULED: <2023-07-19 Wed>
K, scality, S
**** KILL Récupérer le fichier de capture
CLOSED: [2023-07-30 Sun 14:25] SCHEDULED: <2023-07-23 Sun>
Par défaut, on utilisera https://www.twistbioscience.com/products/ngs/alliance-panels#tab-3
ANnonce récente pour nouveau panel Twist : https://www.centogene.com/news-events/news/newsdetails/twist-bioscience-and-centogene-launch-three-panels-to-advance-rare-disease-and-hereditary-cancer-research-and-support-diagnostics
Masi pas de fichier BED
***** DONE Mail centogène
CLOSED: [2023-07-30 Sun 14:22] DEADLINE: <2023-07-23 Sun>
**** TODO Vérifier si le panel Twist Alliance VCGS Exome suffit
SCHEDULED: <2023-07-30 Sun>
**** STRT Comparer à GIAB
SCHEDULED: <2023-07-18 Tue>
#+begin_src sh
nextflow run main.nf -profile standard,helios --input="/Work/Groups/bisonex/centogene/2300346867_63118093_NA12878/63118093_S260_R{1,2}_001.fastq.gz"  --id=2300346867_63118093_NA12878-GRCh38 --genome=GRCh38 -bg
#+end_src
** TODO Insilico :cento:
*** TODO tous les variants centogène
**** DONE Extraire liste des SNVs
CLOSED: [2023-04-22 Sat 17:32] SCHEDULED: <2023-04-17 Mon>
***** DONE Corriger manquant à la main
CLOSED: [2023-04-22 Sat 17:31]
La sortie est sauvegardé dans git-annex : variants_success.csv
***** DONE Automatique
CLOSED: [2023-04-22 Sat 17:31]
**** DONE Convert SNVs : transcript -> génomique
CLOSED: [2023-06-03 Sat 17:16]
***** DONE Variant_recoder
CLOSED: [2023-04-26 Wed 21:21] SCHEDULED: <2023-04-22 Sat>
****** KILL Haskell: 160 manquant : recoded-success.csv
CLOSED: [2023-04-25 Tue 18:32]
La liste des variants a été générée en Haskel   l et nettoyée à la main.
On générer une liste de variant pour variant_rec            oder et on soumet tout d'un coup.
[[file:~/recherche/bisonex/parsevariants/app/Main.hs][parsevariant]]
#+begin_src haskell
recodeVariant = do
  prepareVariantRecod   er "variant_success.csv" "renamed.csv"
  runVariantRecoder "renamed.csv" "recoded.json"
#+end_src
#+RESULTS:
: <interactive>:4:3-19: error:
:     Variable not in scope: runVariantRecoder :: String -> String -> t
: gh
Problème : 160 n'ont pas pu être lu sur 820, probablement à cause du numéro mineur de transcrit
La sortie est sauvegardé dans git-annex : variants-recoded-raw.json.
****** KILL Julia
CLOSED: [2023-04-25 Tue 18:32]
On regénère la liste de variant et on passe à Julia pour préparer l'appel en parallèle à variant recoder
[[file:~/recherche/bisonex/parsevariants/variantRecoder.jl][variantRecoder.jl]]
#+begin_src julia
setupVariantRecoder(unique(init), n)
#+end_src
Puis
#+begin_src sh
parallel -a parallel-recoder.sh --jobs 10
#+end_src
On récupère les résultats
#+begin_src julia
(fails, success) = mergeVariantRecoder(n)
CSV.write(fSuccess, success)
CSV.write(fFailures, fails)
#+end_src
Certains variants ne sont pas trouvé, donc on prépare un nouveau job en enlevant les versionrs mineures des transcrits
#+begin_src julia
# Cleanup json and txt
if isfile(fSuccess) && isfile(fFailures)
    foreach(rm, variantRecoderInput())
    foreach(rm, variantRecoderOutput())
end
redoFails(fFailures)
#+end_src
Puis
#+begin_src sh
parallel -a parallel-recoder.sh --jobs 3
#+end_src
Il manque encore 70 transcrits
***** DONE Julia avec mobidetails: recode-failures-mobidetails.csv
CLOSED: [2023-04-25 Tue 18:58]
Nouvelle stratégie : on essaie une fois variant recoder.
Pour tous les échecs, on utilise mobidetails (~170).
Si l'ID n'est pas trouvé, on incrémente le numéro de version 2 fois
***** DONE Reste une dizaine à corriger à la main
CLOSED: [2023-04-26 Wed 21:21]
- [X] certains transcrits ont juste été supprimé
- [X] Erreur de parsing, manque souvent un -
#+begin_src julia
lastTryMobidetails("recoded-failures-mobidetails.csv")
#+end_src
***** DONE Fusionner données
CLOSED: [2023-04-26 Wed 22:35]
#+begin_src julia
function mergeAllGenomic()
    dNew = mergeAll("recoded-success.csv",
                    "recoded-failures-mobideta

Replacement in projects/bisonex.org at line 48 [95.35]

B:BD[102.40366] → [102.40366:40993]

B:BD[102.40993] → [14.91205:92581]

B:BD[14.92581] → [96.139655:142133]

.5%  chr15_ML143370v1_fix  +      172243    172370    128   What is chrom_fix?
browser details YourSeq   124     1   128   128    98.5%  chr15                 +    74342974  74343101    128
browser details YourSeq    23     1    25   128    96.0%  chr19                 -    33396097  33396121     25
Second
--------------------------------------------------------------------------------------------------------------
browser details YourSeq   126     1   128   128    99.3%  chr15_ML143370v1_fix  -      172243    172370    128   What is chrom_fix?
browser details YourSeq   126     1   128   128    99.3%  chr15     
            -    74342974  74343101    128
browser details YourSeq    23   104   128   128    96.0%  chr19                 +    33396097  33396121     25
******** DONE Bwa mem à la main GRCh38.p13 : on est dans une zone NW
CLOSED: [2023-06-04 Sun 21:51]
On met les 2 reads dans des fichiers séparés puis
#+begin_src sh
cd /Work/Users/apraga/bisonex/tests/xamscissors/align
bwa mem /Work/Groups/bisonex/data/genome/GRCh38.p13/bwa/genomeRef test1.fq test2.fq
#+end_src
A00853:477:HMLWYDSX3:2:2444:22354:28870	97	NW_021160016.1	172243	0	128M	=	172243	128	CACCGTGTCCACCCCTCCTGCCGGCATCTCTGTGACGTTGGCCTTGATGTCCTTGAAGGACATCTTGCTGTCTCCCAGGAGTCTGTAGAGGATGCCACGGTAATCGTGGTGAACACTTCCTTTCTGTC	FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFF:FFFFFFFFFF::FFFFFFFFFFF:FFFFFFFFFFFFFF:FFFFFFF,FFFFFF,FFFFFFFFFFFF:FF::FF	NM:i:2	MD:Z:22A30C7MC:Z:128M	AS:i:118	XS:i:118	XA:Z:NC_000015.10,+74342974,128M,2;
A00853:477:HMLWYDSX3:2:2444:22354:28870	145	NW_021160016.1	172243	0	128M	=	172243	-128	CACCGTGTCCACCCCTCCTGCCGGCATCTCTGTGACGTTGGCCTTGATGTCCTCGAAGGACATCTTGCTGTCTCCCAGGAGTCTGTAGAGGATGCCACGGTAATCGTGGTGAACACTTCCTTTCTGTC	FFFFFFFFFFFFF:FFFFFF,FFF:,FFFFFFFFFFFFFFFF:FFFFFFFFFFFFFF:FF:F:FFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FF:FFF:FF	NM:i:1	MD:Z:22A105	MC:Z:128M	AS:i:123	XS:i:123	XA:Z:NC_000015.10,-74342974,128M,1;
******** DONE GRCh38.p14: idem
CLO
SED: [2023-06-04 Sun 21:51]
A00853:477:HMLWYDSX3:2:2444:22354:28870	97	NW_021160016.1	172243	0	128M	=	172243	128	CACCGTGTCCACCCCTCCTGCCGGCATCTCTGTGACGTTGGCCTTGATGTCCTTGAAGGACATCTTGCTGTCTCCCAGGAGTCTGTAGAGGATGCCACGGTAATCGTGGTGAACACTTCCTTTCTGTC	FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFF:FFFFFFFFFF::FFFFFFFFFFF:FFFFFFFFFFFFFF:FFFFFFF,FFFFFF,FFFFFFFFFFFF:FF::FF	NM:i:2	MD:Z:22A30C7MC:Z:128M	AS:i:118	XS:i:118	XA:Z:NC_000015.10,+74342974,128M,2;
A00853:477:HMLWYDSX3:2:2444:22354:28870	145	NW_021160016.1	172243	0	128M	=	172243	-128	CACCGTGTCCACCCCTCCTGCCGGCATCTCTGTGACGTTGGCCTTGATGTCCTCGAAGGACATCTTGCTGTCTCCCAGGAGTCTGTAGAGGATGCCACGGTAATCGTGGTGAACACTTCCTTTCTGTC	FFFFFFFFFFFFF:FFFFFF,FFF:,FFFFFFFFFFFFFFFF:FFFFFFFFFFFFFF:FF:F:FFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FF:FFF:FF	NM:i:1	MD:Z:22A105	MC:Z:128M	AS:i:123	XS:i:123	XA:Z:NC_000015.10,-74342974,128M,1;
******** DONE GRCh38 : ok
CLOSED: [2023-06-04 Sun 22:15]
 bwa mem /Work/Projects/bisonex/data/genome/GRCh38/GCA_000001405.15_GRCh38_full_analysis_set.fna test1.fq test2.fq
******* DONE Vérifier que les reads ont la même qualité sur les fichiers d'origine: oui
CLOSED: [2023-06-04 Sun 21:07]
******* DONE Supprimer les NW_ ?
CLOSED: [2023-06-10 Sat 10:40] SCHEDULED: <2023-06-04 Sun>
@A00853:477:HMLWYDSX3:3:2114:14742:8860
CAGGCCAGCCGCTCAGCCCGCTCCTTTCACCCTCTGCAGGAGAGCCTCGTGGCAGGCCAGTGGAGGGACATGATGGACTACATGCTCCAAGGGGTGGCGCAGCCGAGCATGGAAGAGGGCTCTGGACAGCTCCTGGAAGGGCACTTGCAC
+
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
@A00853:477:HMLWYDSX3:3:2114:14742:8860
CTTTTGCTTGTCCCCAGGACGCACCTCAGGGTGGTGAAGCAAAAAAACCACGGCCCAGGAGAGGGTGGGTGCTGTGGTCTCAGTGCCACCGATCAGGAGGTCCACTGCAGCCATGTGCAAGTGCCCTTCCAGGAGCTGTCCAGAGCCCTCT
+
FFFFFFFFFFFFFFFFFFFFFFF:FFF:FFFFFFFFFFFFF,FFFFFFFFFFFF:F:FFFF:FFFFF,,FFF:FFFFFFFFFF,FFFFFFF,FFFFFFFFFFF,FFFFFFFFF:FFFF,F:FFFFF:FFFFFFFFF:FFFF,FFFFFFFFF
******* DONE Supprimer NW_ et NT_
***** TODO Phase 2 : chr22, vaf variable :T2T:
SCHEDULED: <2023-08-02 Wed>
****** TODO Phase 3 : tous SNV, vaf variable :T2T:
SCHEDULED: <2023-07-27 Thu>
***** TODO Test Indel
**** Divers
***** DONE Vérifier nombre de reads fastq - bam
CLOSED: [2022-10-09 Sun 22:31]
*** KILL Liste varants "clinically relevent" (Clinge - CT-R d)
CLOSED: [2023-06-25 Sun 15:53] SCHEDULED: <2023-06-25 Sun>
[cite:@wilcox2021]
Vu avec alexis: pas notre cas d'usage

[102.40366]

.5%  chr15_ML143370v1_fix  +      172243    172370    128   What is chrom_fix?
browser details YourSeq   124     1   128   128    98.5%  chr15                 +    74342974  74343101    128
browser details YourSeq    23     1    25   128    96.0%  chr19                 -    33396097  33396121     25
Second
--------------------------------------------------------------------------------------------------------------
browser details YourSeq   126     1   128   128    99.3%  chr15_ML143370v1_fix  -      172243    172370    128   What is chrom_fix?
browser details YourSeq   126     1   128   128    99.3%  chr15                 -    74342974  74343101    128
browser details YourSeq    23   104   128   128    96.0%  chr19                 +    33396097  33396121     25
******** DONE Bwa mem à la main GRCh38.p13 : on est dans une zone NW
CLOSED: [2023-06-04 Sun 21:51]
On met les 2 reads dans des fichiers séparés puis
#+begin_src sh
cd /Work/Users/apraga/bisonex/tests/xamscissors/align
bwa mem /Work/Groups/bisonex/data/genome/GRCh38.p13/bwa/genomeRef test1.fq test2.fq
#+end_src
A00853:477:HMLWYDSX3:2:2444:22354:28870	97	NW_021160016.1	172243	0	128M	=	172243	128	CACCGTGTCCACCCCTCCTGCCGGCATCTCTGTGACGTTGGCCTTGATGTCCTTGAAGGACATCTTGCTGTCTCCCAGGAGTCTGTAGAGGATGCCACGGTAATCGTGGTGAACACTTCCTTTCTGTC	FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFF:FFFFFFFFFF::FFFFFFFFFFF:FFFFFFFFFFFFFF:FFFFFFF,FFFFFF,FFFFFFFFFFFF:FF::FF	NM:i:2	MD:Z:22A30C7MC:Z:128M	AS:i:118	XS:i:118	XA:Z:NC_000015.10,+74342974,128M,2;
A00853:477:HMLWYDSX3:2:2444:22354:28870	145	NW_021160016.1	172243	0	128M	=	172243	-128	CACCGTGTCCACCCCTCCTGCCGGCATCTCTGTGACGTTGGCCTTGATGTCCTCGAAGGACATCTTGCTGTCTCCCAGGAGTCTGTAGAGGATGCCACGGTAATCGTGGTGAACACTTCCTTTCTGTC	FFFFFFFFFFFFF:FFFFFF,FFF:,FFFFFFFFFFFFFFFF:FFFFFFFFFFFFFF:FF:F:FFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FF:FFF:FF	NM:i:1	MD:Z:22A105	MC:Z:128M	AS:i:123	XS:i:123	XA:Z:NC_000015.10,-74342974,128M,1;
******** DONE GRCh38.p14: idem
CLOSED: [2023-06-04 Sun 21:51]
A00853:477:HMLWYDSX3:2:2444:22354:28870	97	NW_021160016.1	172243	0	128M	=	172243	128	CACCGTGTCCACCCCTCCTGCCGGCATCTCTGTGACGTTGGCCTTGATGTCCTTGAAGGACATCTTGCTGTCTCCCAGGAGTCTGTAGAGGATGCCACGGTAATCGTGGTGAACACTTCCTTTCTGTC	FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFF:FFFFFFFFFF::FFFFFFFFFFF:FFFFFFFFFFFFFF:FFFFFFF,FFFFFF,FFFFFFFFFFFF:FF::FF	NM:i:2	MD:Z:22A30C7MC:Z:128M	AS:i:118	XS:i:118	XA:Z:NC_000015.10,+74342974,128M,2;
A00853:477:HMLWYDSX3:2:2444:22354:28870	145	NW_021160016.1	172243	0	128M	=	172243	-128	CACCGTGTCCACCCCTCCTGCCGGCATCTCTGTGACGTTGGCCTTGATGTCCTCGAAGGACATCTTGCTGTCTCCCAGGAGTCTGTAGAGGATGCCACGGTAATCGTGGTGAACACTTCCTTTCTGTC	FFFFFFFFFFFFF:FFFFFF,FFF:,FFFFFFFFFFFFFFFF:FFFFFFFFFFFFFF:FF:F:FFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FF:FFF:FF	NM:i:1	MD:Z:22A105	MC:Z:128M	AS:i:123	XS:i:123	XA:Z:NC_000015.10,-74342974,128M,1;
******** DONE GRCh38 : ok
CLOSED: [2023-06-04 Sun 22:15]
 bwa mem /Work/Projects/bisonex/data/genome/GRCh38/GCA_000001405.15_GRCh38_full_analysis_set.fna test1.fq test2.fq
******* DONE Vérifier que les reads ont la même qualité sur les fichiers d'origine: oui
CLOSED: [2023-06-04 Sun 21:07]
******* DONE Supprimer les NW_ ?
CLOSED: [2023-06-10 Sat 10:40] SCHEDULED: <2023-06-04 Sun>
@A00853:477:HMLWYDSX3:3:2114:14742:8860
CAGGCCAGCCGCTCAGCCCGCTCCTTTCACCCTCTGCAGGAGAGCCTCGTGGCAGGCCAGTGGAGGGACATGATGGACTACATGCTCCAAGGGGTGGCGCAGCCGAGCATGGAAGAGGGCTCTGGACAGCTCCTGGAAGGGCACTTGCAC
+
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
@A00853:477:HMLWYDSX3:3:2114:14742:8860
CTTTTGCTTGTCCCCAGGACGCACCTCAGGGTGGTGAAGCAAAAAAACCACGGCCCAGGAGAGGGTGGGTGCTGTGGTCTCAGTGCCACCGATCAGGAGGTCCACTGCAGCCATGTGCAAGTGCCCTTCCAGGAGCTGTCCAGAGCCCTCT
+
FFFFFFFFFFFFFFFFFFFFFFF:FFF:FFFFFFFFFFFFF,FFFFFFFFFFFF:F:FFFF:FFFFF,,FFF:FFFFFFFFFF,FFFFFFF,FFFFFFFFFFF,FFFFFFFFF:FFFF,F:FFFFF:FFFFFFFFF:FFFF,FFFFFFFFF
******* DONE Supprimer NW_ et NT_
***** TODO Phase 2 : chr22, vaf variable :T2T:
***** TODO Phase 3 : tous SNV, vaf variable :T2T:
***** TODO Test Indel
**** Divers
***** DONE Vérifier nombre de reads fastq - bam
CLOSED: [2022-10-09 Sun 22:31]
*** KILL Liste varants "clinically relevent" (Clinge - CT-R d)
CLOSED: [2023-06-25 Sun 15:53] SCHEDULED: <2023-06-25 Sun>
[cite:@wilcox2021]
Vu avec alexis: pas notre cas d'usage

Deletion in inbox.org at line 2 [104.197]

B:BD[103.152] → [4.797:875]

* TODO Lettre recommandée annonce déménagement
SCHEDULED: <2023-07-29 Sat>

Insertion in archive/projects.org_archive at line 1517 [105.9]

[14.96846]


* DONE Cadeaux Maxence + Emy
CLOSED: [2023-07-07 Fri 18:40]
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:46
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Famille
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: DONE
:END:
/Entered on/ [2023-07-02 Sun 10:53]
Maxence : Éveil musical
* Japonais
:PROPERTIES:
:CATEGORY: japonais
:ARCHIVE_TIME: 2023-07-30 Sun 14:46
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Langues
:ARCHIVE_CATEGORY: japonais
:END:
** Miura [7/7]
*** DONE Leçon 1 [/]
**** DONE Lire
**** DONE Anki
*** KILL Leçon 2 [2/2]
CLOSED: [2023-07-07 Fri 18:48]
**** DONE Lire
**** KILL Anki
CLOSED: [2023-07-07 Fri 18:48]
***** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:48]
*** KILL Leçon 3
CLOSED: [2023-07-07 Fri 18:44]
**** DONE Lire
**** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
*** KILL Leçon 4
CLOSED: [2023-07-07 Fri 18:44]
**** DONE Lire
**** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
*** KILL Leçon 5
CLOSED: [2023-07-07 Fri 18:44]
**** DONE Lire
**** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
*** KILL Leçon 6
CLOSED: [2023-07-07 Fri 18:44]
**** DONE Lire
**** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
*** KILL Leçon 7
CLOSED: [2023-07-07 Fri 18:44]
**** KILL Lire
CLOSED: [2023-07-07 Fri 18:44]
**** KILL Anki
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Grammaire
CLOSED: [2023-07-07 Fri 18:44]
*** Lire
** Leçon Aya
:PROPERTIES:
:CATEGORY: aya
:END:
*** KILL Lire dialogue fin leçon 10
CLOSED: [2022-12-03 Sat 12:34] SCHEDULED: <2022-07-30 Sat>
* CentoX
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:47
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Projets personnel
:ARCHIVE_CATEGORY: projects
:END:
** Raw data
*** DONE Ne plus utiliser les .size mais récupérer directement la taille sur le serveur
CLOSED: [2022-07-26 Tue 17:36]
*** DONE Faire fonctionner taches récurrente avec Windows
CLOSED: [2022-11-19 Sat 17:34]
Erreur 0x1 : a priori résolu en démarrant le script dans le bon répertoire...
** Notes
*** Extraction de données
- Trop compliqué de travailles sur la structure du pdf
- Extraire le tableau avec python ?
- Comparaison
  - pdftotext : bon résultats sur page 1. Sur les ségrégations, il n’y a pas de retour à la ligne avant l’indication
- pdfminer donne des résultats légèrement supérieurs, beaucoup d’espaces sur les ségrégations
  - pypdf2 : trop de retours à la lignes intempestifs dans le texte
La structure du  tableau est perdue dans tous les cas
*** Parser
Liste de parser : https://tomassetti.me/parsing-in-python/
* Assistant
:PROPERTIES:
:CATEGORY: assistant
:ARCHIVE_TIME: 2023-07-30 Sun 14:47
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Projets personnel
:ARCHIVE_CATEGORY: assistant
:END:
** DONE Regarder ce qu'Yvain a fait
* DONE Amende
CLOSED: [2023-07-30 Sun 14:47]
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:47
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Voiture/Mazda 5
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: DONE
:ARCHIVE_ITAGS: voiture
:END:
** DONE Changement d'adresse carte grise
CLOSED: [2023-06-11 Sun 21:40]
** DONE Envoyer photocopie carte grise pour éviter majoration
CLOSED: [2023-07-02 Sun 10:52] SCHEDULED: <2023-06-18 Sun>
* DONE Contrôle technique
CLOSED: [2023-07-27 Thu 23:32] DEADLINE: <2023-06-01 Thu>
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:47
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Voiture/Mazda 5
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: DONE
:ARCHIVE_ITAGS: voiture
:END:
** DONE Prendre RV
CLOSED: [2023-05-28 Sun 10:03] SCHEDULED: <2023-05-07 Sun>
** DONE Changer amortisseurs
CLOSED: [2023-07-02 Sun 10:52] SCHEDULED: <2023-06-13 Tue>
** DONE Changer phare avant
CLOSED: [2023-07-02 Sun 10:52] SCHEDULED: <2023-06-13 Tue>
** DONE Contrôle pollution
CLOSED: [2023-07-02 Sun 10:52] SCHEDULED: <2023-06-13 Tue>
** DONE Prendre rendez-vous pour contre-visite
CLOSED: [2023-07-21 Fri 17:45] SCHEDULED: <2023-07-02 Sun>
** DONE Contre-visite
CLOSED: [2023-07-27 Thu 23:31] DEADLINE: <2023-07-26 Wed>
* KILL Changer plaquettes +/- disques
CLOSED: [2023-06-11 Sun 18:39] SCHEDULED: <2023-05-09 Tue>
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:47
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Voiture/Mazda 5
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: KILL
:ARCHIVE_ITAGS: voiture
:END:
Écrou grippé ? Voir avec fils M. Chouffe à Gennes, chemin du vernois (voiture 607 bleue)
* DONE Changer pneus avant
CLOSED: [2022-09-11 Sun 22:05] SCHEDULED: <2022-09-03 Sat>
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:47
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Voiture/Mazda 5
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: DONE
:ARCHIVE_ITAGS: voiture
:END:
Trop abimé pour prendre le risque, on changera les arrières plus tard
* DONE Héberger arbre généaloqiue
CLOSED: [2023-06-24 Sat 15:42] SCHEDULED: <2023-04-14 Fri>
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:49
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Divers
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: DONE
:END:
- Github : genealogy
- Sourcehut : genealogy
- Proton drive
- Mail <2023-06-24 Sat>
* DONE Enterrement Mme Karl
CLOSED: [2023-05-28 Sun 10:02]
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:49
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Divers
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: DONE
:END:
** DONE Commander bouquets
CLOSED: [2023-04-13 Thu 09:11] SCHEDULED: <2023-04-12 Wed>
** DONE Message avec les bouquets
CLOSED: [2023-04-13 Thu 09:11] SCHEDULED: <2023-04-13 Thu>
** DONE Remboursement [5/5]
CLOSED: [2023-05-28 Sun 10:02] SCHEDULED: <2023-04-20 Thu>
*** DONE Aurélien
CLOSED: [2023-04-22 Sat 15:27]
*** DONE Élise
CLOSED: [2023-04-22 Sat 15:27]
*** DONE Yvain
CLOSED: [2023-05-28 Sun 10:02]
*** DONE Papa
CLOSED: [2023-04-22 Sat 15:27]
*** DONE Thierry
CLOSED: [2023-04-13 Thu 09:12]
** DONE Carte personnalisée
CLOSED: [2023-04-26 Wed 21:10] SCHEDULED: <2023-04-17 Mon>
* DONE Don index nzb
CLOSED: [2023-04-22 Sat 15:27] SCHEDULED: <2023-04-19 Wed>
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:49
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Divers
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: DONE
:END:
/Entered on/ [2023-04-16 Sun 16:53]
* DONE Remboursement TER Grenoble du 9 avril
CLOSED: [2023-05-06 Sat 09:06] SCHEDULED: <2023-05-03 Wed>
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:49
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Divers
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: DONE
:END:
/Entered on/ [2023-04-26 Wed 21:03]
Demande envoyé <2023-04-26 Wed>
Refusé
* DONE Demander accès déchetteries Saône
CLOSED: [2023-07-30 Sun 14:49]
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:49
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Divers
:ARCHIVE_CATEGORY: projects
:ARCHIVE_TODO: DONE
:END:
/Entered on/ [2023-05-28 Sun 10:02]
* Mustard :mustard:
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 14:51
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Recherche
:ARCHIVE_CATEGORY: recherche
:END:
** Scripts
*** DONE Script pour données labkey
on convertit tous les pdf en png puis OCR avec tesseract pour les transformer en texte
On supprimer les header et footer à la main
Cf ~/code/scripts/python/mustard/courrier.py
*** DONE Renommer les dossiers PED
#+begin_src python :results output
import pandas as pd
import os
import os.path
dir1 = "/alexi/Documents/mustard/"
dir2 = "/alexi/Documents/mustard-new/"
p  = pd.read_csv(os.path.join(dir1, "Patients_2022-02-02_11-44-03.tsv"), sep='\t')
# id + p.nom + " " + p.prenom + " " + p.date_de_naissance
for i in p.index:
    split = p['patientID'][i].split(".")
    # Only store the index case
    if split[1] == "1":
        dest = p.nom[i].upper() + " " + p.prenom[i] + " " + p.date_de_naissance[i]
        print(f"ok {split[0]} {dest}")
        src = os.path.join(dir1, split[0])
        if os.path.exists(src):
            if p.nom[i] != "Non renseigné":
                os.rename(src, os.path.join(dir2, dest))
            else:
                os.rename(src, os.path.join(dir2, split[0]))
#+end_src
*** DONE Générer clinique
#+begin_src python :results output
import pandas as pd
import os
import os.path
dir = "/alexi/Documents/mustard/"
p  = pd.read_csv(os.path.join(dir, "Patients_2022-02-02_11-44-03.tsv"), sep='\t')
# id + p.nom + " " + p.prenom + " " + p.date_de_naissance
f = open(os.path.join(dir, "clinique2.csv"), 'w')
for i in p.index:
    split = p['patientID'][i].split(".")
    # Only store the index case
    if split[1] == "1":
        folder = p.nom[i].upper() + " " + p.prenom[i] + " " + p.date_de_naissance[i]
        if os.path.exists(os.path.join(dir, folder)):
            f.write(split[0] + ";" + p.nom[i].upper() + ";" + p.prenom[i] + ";" + p.date_de_naissance[i] + "\n")
#+end_src
*** KILL Stats sur la balance allélique pour Paul
CLOSED: [2023-07-07 Fri 18:47]
**** DONE Besançon seul : total, par variant
CLOSED: [2022-10-28 Fri 10:57]
**** DONE Dijon + Besançon seul : total, par variant et par type de prélèvement
CLOSED: [2022-12-03 Sat 12:35]
Dans "variations à vérifier". 1 seul variant normalement en miseq, parfois plusieurs en exome
AB = "allelic balance"
**** KILL Rajouter une colonne balance allélique
CLOSED: [2023-07-07 Fri 18:47]
***** KILL Ancien panel
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Nouveau panel
CLOSED: [2023-07-07 Fri 18:44]
***** KILL Dijon
CLOSED: [2023-07-07 Fri 18:44]
**** KILL Version executable pour paul
CLOSED: [2023-07-07 Fri 18:44]
Avec colonne dédiée
** Données
*** DONE Import Labkey
*** KILL Clinique, OCR et nettoyage données labkey [1199/1199]
CLOSED: [2023-07-07 Fri 18:44]
DONE = sur scality (mis dans ~/annex/mustard/done)
SRT = traité, non transféré (en attente dans ~/annex/mustard)
**** DONE PED0052
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CLOSED: [2022-08-01 Mon 09:44]
**** DONE PED1034
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CLOSED: [2022-11-08 Tue 22:20]
**** DONE PED1040
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CLOSED: [2022-11-08 Tue 22:20]
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*** DONE Fusionner exome dijon pour Paul
CLOSED: [2022-08-04 Thu 17:42]
**** DONE Enlever les doublons
CLOSED: [2022-09-13 Tue 21:36]
*** DONE Fusionner panel Dijons
*** DONE Fusion variants à vérifier de dijon
CLOSED: [2022-12-04 Sun 22:32]
*** KILL Dxcare
**** DONE Demande Dijon
**** KILL Demande DPO Besançon
*** KILL donnée pierre
**CLOSED: [2022-05-05 jeu. 17:53]
***** KILL Format de données final
CLOSED: [2023-07-07 Fri 18:44]
Voir avec Paul
** Stockage
*** DONE Accès scality au travail
*** KILL VPN pour Jehanne
CLOSED: [2023-05-28 Sun 10:04]
** Notes
- Sur phénotype mélanocytaire, il peut valoir le coup de faire de la CGH sur biopsie
  Inconvénient du panel : on passe à côté
  Inconvénient de l’exome : faible profondeur
  En général, pas d’ADN suffisant pour les 3 !
- Idée de pipeline : CNV (mais il faut les références)
- 2 approches : exome direct ou CGH + panel
- Exome envoyé à integragen (ou CNR): problème = perte de financement car plus de centre de référence à Dijon
  envoi dans le privé compliqué vu le coût...
* KILL DIU dysmorpho
:PROPERTIES:
:CATEGORY: dysmorpho
:ARCHIVE_TIME: 2023-07-30 Sun 15:03
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Génétique
:ARCHIVE_CATEGORY: dysmorpho
:ARCHIVE_TODO: KILL
:END:
** KILL Relire + notes [1/92]
*** KILL Intro dysmorpho - Verloes
CLOSED: [2023-07-07 Fri 18:43]
*** KILL Empreinte génomique
*** KILL Beckwith, Silver Russel
*** KILL Scoliose
*** KILL Syndromes cytogénétique - Salanville
*** KILL Dysostose mandibulo faciale
*** KILL Williams dup 7p11.2
*** KILL Pathologie génétique de la reproduction
*** KILL Malformations oculaires
*** KILL Comprendre les test génétiques
*** KILL Fente
*** KILL Gonosome
*** KILL Smith-Mangenis
*** KILL 22q11
*** KILL Dysmorpho nouveau-né
*** KILL Autopsie foetale
*** KILL Dysmorphologie - généralités (A Verloes)
*** KILL Dysmorphologie du nouveau né (M Vincent)
*** KILL Registre des malformations (N Lelong)
*** KILL Comprendre les tests génétiques - Mutations - NGS (Y Vial)
*** KILL Cytogénétique (C Missirian)
*** KILL NGS et syndromologie (F Tran-Mau-Them)
*** KILL Empreinte génomique (F Brioudé) (seq 15 Beckwith Wiedemann Syndrome et SRussel S)
*** KILL Autopsie foetale (F Guimiot)
*** KILL Tumeur et développement (H Cave)
*** KILL Dysmorphologie foetale (MH Saint Frison)
*** KILL Pathologie génétique de la reproduction (F Vialard)
*** KILL Le dysmorphologiste en prénatal (N Gruchy)
*** KILL Régulation génique et  anomalies du développement (F Petit)
*** KILL Echographie fœtale et dysmorphologie (C Rozel)
*** KILL Déficience intellectuelle (A Curie)
*** KILL Autisme et génétique (A Maruani)
*** KILL Tests neuropsy
*** KILL XLID(A Toutain)
*** KILL Anomalies du développement embryonnaire précoce (C Quelin)
*** KILL Anomalies de fermeture du tube neural (C Quelin)
*** KILL FAS (D Germanaud)
*** KILL Médicaments et grossesse (C Vauzelle)
*** KILL Syndromes avec fentes oro-faciales- (J Van-Gils)
*** KILL Syndromes avec craniosténose (C Collet)
*** KILL Dents & syndromes (I Bailleul)
*** KILL Dysostoses Mandibulo faciales (J Amiel)
*** KILL Avances staturales (A Putoux)
*** KILL Retards staturaux syndromiques (A Putoux)
*** KILL Syndromes avec obésité (G Diene)
*** KILL Spliceosomopathies (P Edery)
*** KILL Microcéphalies (S Passemard)
*** KILL Anomalies du cervelet : Joubert, NPH ... (L Burglen)
*** KILL Epilepsie et syndromes (C Mignot)
*** KILL Holoprosencéphalie (S Odent)
*** KILL Hydrocephalie (S Odent)
*** KILL Anomalies de migration (S Passemard)
*** KILL Chondrodysplasies (G Baujat)
*** KILL Anomalies de segmentation et scoliose (J Thévenon)
*** KILL Génétique du développement des membres et principaux syndromes (F Petit)
*** KILL Classification des malformations des membres (F Petit)
*** KILL Prise en charge des anomalies des membres (N Quintero)
*** KILL Syndromes avec anomalies uro-néphrologiques pré- et postnatal (G Morin)
*** KILL Syndromes avec anomalies génitales et DSD (B Leheup)
*** KILL Du coeur au syndrome (D Genevieve)
*** KILL Malformation cardiaque en anténatal (D Genevieve)
*** KILL Base génétique du déterminisme du sexe (C Colson)
*** KILL Surdités syndromiques (S Marlin)
*** KILL Malformations oculaires (N Chassaing)
*** KILL Dermatologie et développement (P Vabres)
*** KILL Dysmorphologie et métabolisme (M Barth)
*** KILL Maladies de surcharge (D Germain)
*** KILL Trisomie 21 (R Touraine)
*** KILL S. Williams - duplication 7q11.2 (M Rossi)
*** KILL Délétion 22q11.2 (L Perrin)
*** KILL Syndromes cytogénétiques (D Sanlaville)
*** KILL Gonosomes (J Leger)
*** KILL Parcours de soin des patients avec anomalies du développement (N Jean-Marçais)
*** KILL Prise en charge médicosociale du handicap (D Juzeau)
*** KILL Fanconi (T Leblanc)
*** KILL Ehlers-Danlos (D Germain)
*** KILL Chromatinopathies: TAD - Kabuki, Rubinstein-Taybi, Wiedemann-Steiner, SBYSS... (D Genevieve)
*** KILL Marfan et syndromes apparentés (G Jondeau)
*** KILL RASopathies (Y Capri)
*** KILL Syndromes de Pitt Hopkins, Angelman, Rett et Rett-like (N Bahi-Buisson)
*** KILL Filaminopathies A (C Goizet)
*** KILL Achondroplasie (G Baujat)
*** KILL OI (G Baujat)
*** KILL Ciliopathies: approche globale (T Attie-Bitach)
*** KILL Smith-Magenis (L Perrin)
*** KILL Cohésinopathies : Cornelia de Lange, Coffin-Siris/NB, CHOPS... (A Goldenberg)
*** KILL Albinisme et syndromes apparentés (B Arveiler)
*** KILL Beckwith Wiedemann Syndrome & Silver Russel Syndrome (F Brioude)
*** KILL Neurofibromatoses - STB (C Goizet)
*** KILL Cowden, Gorlin (P Goizet)
*** KILL Syndrome de Kleefstra (L Perrin)
*** KILL Téloméropathies (T Leblanc)

Insertion in archive/projects.org_archive at line 3206 [105.9]

[14.96847]


* DONE DES [4/4]
CLOSED: [2023-07-07 Fri 18:47]
:PROPERTIES:
:ARCHIVE_TIME: 2023-07-30 Sun 15:03
:ARCHIVE_FILE: ~/roam/personal/projects.org
:ARCHIVE_OLPATH: Génétique
:ARCHIVE_CATEGORY: maison
:ARCHIVE_TODO: DONE
:END:
** DONE Valider cours sur sides
** KILL Vérifier que toutes les diapos sont sur one drive
CLOSED: [2023-07-07 Fri 18:47]
** DONE Examen sur sides
** KILL Lire les cours
CLOSED: [2023-07-07 Fri 18:47]
*** KILL Presentiel session 1 [9/9]
CLOSED: [2022-11-19 Sat 17:43]
**** DONE Introduction à la dysmorphologie
**** DONE Structuration du génome et mécanismes mutationnels
**** DONE Oncogénétique: introduction
**** KILL Diagnostic prénatal
CLOSED: [2022-11-19 Sat 17:35]
**** DONE Grandes technologies et bioinformatique
**** DONE Aspects réglementaires et éthiques
**** DONE Mucoviscidose
CLOSED: [2022-09-10 Sat 18:34]
**** KILL Bases sur le conseil génétique
CLOSED: [2022-11-19 Sat 17:35]
**** KILL SEPI et TD
CLOSED: [2022-11-19 Sat 17:35]
*** DONE E-learning session 1 [6/6]
**** DONE maladies endocriniennes et métabolisme
**** DONE anomalies de la croissance
**** DONE hématologie
**** DONE maladies du tissu conjonctif
**** DONE Oncogénétique
**** DONE dermatogénétique
*** KILL Presentiel session 2 [0/5]
CLOSED: [2022-11-19 Sat 17:43]
**** KILL Déficience intellectuelle
CLOSED: [2022-11-19 Sat 17:35]
**** KILL Génétique clinique et formelle
CLOSED: [2022-11-19 Sat 17:35]
**** KILL Pathologies fréquentes en génétique clinique
CLOSED: [2022-11-19 Sat 17:35]
**** KILL Génome humain : normal et pathologique
CLOSED: [2022-11-19 Sat 17:35]
**** KILL Maladies métaboliques
CLOSED: [2022-11-19 Sat 17:35]
*** KILL E-learning session 2 [6/6]
CLOSED: [2023-07-07 Fri 18:47]
**** DONE Infertilité
-> cours 1, diapo 31
**** DONE Syndromes microdélétionnels
**** DONE Dysgonosomies
**** DONE Cancer du colon: Maladie de Lynch et CMMRD
**** DONE Déficience intellectuelle
**** KILL Pathologies neuromusculaires
CLOSED: [2023-07-07 Fri 18:47]