apraga/org - Change MN5IIXFRUQQTOCWN66V6ZWIPUOUOM62EWE4BAKKYCTK6FOWR3ZDAC

Saving results of simuscop exploration

Created by Alexis Praga on April 30, 2023

MN5IIXFRUQQTOCWN66V6ZWIPUOUOM62EWE4BAKKYCTK6FOWR3ZDAC

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In channels

main

Change contents

Replacement in projects/bisonex.org at line 1 [3.35]

B:BD[3.35] → [4.259:8391]

∅:D[4.8391] → [5.9591:9593]

B:BD[5.9591] → [5.9591:9593]

∅:D[5.9593] → [6.16:74]

B:BD[7.9377] → [6.16:74]

#+title: Bisonex
* Biblio :biblio:
** 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
** 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 in INDELs calling, which could be explained by their divergence in
 sequencing s
tr
ategy that producing different length of reads (all BGI pl

[3.35]

[8.66]

#+title: Bisonex
* Biblio :biblio:
** 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 in INDELs calling, which could be explained by their divergence in
 sequencing strategy that producing different length of reads (all BGI pl

Replacement in projects/bisonex.org at line 39 [3.35]

B:BD[9.24645] → [2.206433:230518]

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-mobidetails.csv",
                    "recoded-failures-mobidetails-redo.csv")
    dInit = @chain DataFrame(CSV.File("variant_success.csv")) begin
        @transform :transcript = :transcript .* ":" .* :coding .* :codingPos .* :codingChange
        @select :file :transcript :classification :zygosity
        @rename :classificationCentogene = :classification
    end
    dTmp = outerjoin(dInit, dNew, on = :transcript)
    CSV.write("variant_genomic.csv", dTmp)
end
fSuccess = "recoded-success.csv"
fFailures = "recoded-failures.csv"
# variantRecoder(fSuccess, fFailures)
# mobidetailsOnFailures(fFailures)
# lastTryMobidetails("recoded-failures-mobidetails.csv")
mergeAllGenomic()
#+end_src
**** DONE Formatter donner pour simuscop
CLOSED: [2023-04-28 Fri 11:55] SCHEDULED: <2023-04-26 Wed>
*** TODO Extraire liste des CNVs
SCHEDULED: <2023-04-17 Mon>
*** WAIT Simuscop :simuscop:
**** DONE Entrainer le modèle sur 63003856/
CLOSED: [2023-04-29 Sat 19:56]
Relancer le modèle pour être sûr
**** DONE Générer fastq avec simuscop (del et ins seulement) 20x
CLOSED: [2023-04-28 Fri 23:35] SCHEDULED: <2023-04-22 Sat>
***** DONE Génerer un profile avec bed de centogène
CLOSED: [2023-04-28 Fri 11:54] SCHEDULED: <2023-04-22 Sat>
NA12878 mais à refaire avec un vrai séquencage
Voir [[*Centogène][Bed Centogène]] pour choix
***** DONE Générer les données en 20x
CLOSED: [2023-04-28 Fri 11:54] SCHEDULED: <2023-04-22 Sat>
capture de centogene
***** DONE Regénérer en supprimant les doublons
CLOSED: [2023-04-28 Fri 17:28]
**** DONE Quelle couverture ?
CLOSED: [2023-04-29 Sat 18:26]
ex sur chr11:16,014,966 où on a 11 reads dans la simulation contre 200 !
***** 200 est la plus proche
#+attr_html: :width 500px
[[./simuscop-200-chr1-1.png]]
#+attr_html: :width 500px
[[./simuscop-200-chr1-2.png]]
***** DONE 20x
CLOSED: [2023-04-29 Sat 15:38]
***** DONE 50x
CLOSED: [2023-04-29 Sat 15:38]
***** DONE 100x
CLOSED: [2023-04-29 Sat 15:39]
***** DONE 200x
CLOSED: [2023-04-29 Sat 15:39]
**** DONE Reads mal centrés sur des petits exons seuls
CLOSED: [2023-04-29 Sat 19:56] SCHEDULED: <2023-04-29 Sat>
Capture ok : [[https://genome-euro.ucsc.edu/cgi-bin/hgTracks?db=hg38&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr1%3A153817168%2D153817824&hgsid=296556270_F4fkENLPXHXidi2oALXls2jxNH9l][UCSC]] (track noire)
Mais mauvaise répartitiopn
#+attr_html: :width 800px
[[./simuscop-error.png]]
À tester
- Problème de profile ?
  - mauvais patient ?
  - mauvaise génération ? -> comparer avec ceux donnés sur github
- nom des chromosomes ?
***** DONE [#A] Tester sur exon 6 GATAD2B pour NC_000001.11:g.153817496A>T
CLOSED: [2023-04-29 Sat 19:56] SCHEDULED: <2023-04-29 Sat>
****** DONE Configuration + Profile 63003856.profile: idem, mal centré
CLOSED: [2023-04-29 Sat 19:18]
Téléchargement des données
#+begin_src sh :dir ~/code/bisonex/test-simuscop
scp meso:/Work/Projects/bisonex/data/genome/GRCh38.p14/genomeRef.fna .
scp meso:Work/Projects/bisonex/data/simuscop/*.profile .
scp -r meso:/Work/Projects/bisonex/data/genome/GRCh38.p13/bwa .
#+end_src
On récupère l'exon (NB: org-mode ne lance pas le code...)
#+begin_src julia
using CSV,DataFramesMeta
d = CSV.read("VCGS_Exome_Covered_Targets_hg38_40.1MB_renamed.bed", header=false, delim="\t", DataFrame)
@subset d :Column1 .== "NC_000001.11" :Column2 .<= 153817496 :Column3 .>= 153817496
#+end_src
NC_000001.11  153817371  153817542
Génération du bed
#+begin_src sh :dir ~/code/bisonex/test-simuscop
echo -e "NC_000001.11\t153817371\t153817542" > gatad2b-exon6.bed
#+end_src
#+RESULTS:
Génération d'un variant
#+begin_src sh :dir ~/code/bisonex/test-simuscop
echo -e "s\tsingle\tNC_000001.11\t153817496\tA\tT\thet"> variant.txt
#+end_src
#+RESULTS:
Génération du fichier de config
#+begin_src sh :dir ~/code/bisonex/test-simuscop
cat > config_wes.txt << EOL
ref = genomeRef.fna
profile = ./63003856.profile
variation = ./variant.txt
target = ./gatad2b-exon6.bed
layout = PE
threads = 1
name = single
output = test-gatad2b
coverage = 20
EOL
#+end_src
#+RESULTS:
On démarre la simulation
#+begin_src sh :dir ~/code/bisonex/test-simuscop
simuReads config_wes.txt
#+end_src
#+RESULTS:
Alignement
#+begin_src sh :dir ~/code/bisonex/test-simuscop
bwa mem -R '@RG\tID:sample\tSM:sample\tPL:ILLUMINA\tPM:Miseq\tCN:lol\tLB:definition_to_add' bwa/genomeRef test-gatad2b/single_1.fq  test-gatad2b/single_2.fq | samtools sort  -o single.bam
#+end_src
#+RESULTS:
****** DONE Profile github  HiSeq2000
CLOSED: [2023-04-29 Sat 19:56]
#+begin_src sh :dir ~/code/bisonex/test-simuscop :result file
wget https://raw.githubusercontent.com/qasimyu/simuscop/master/testData/Illumina_HiSeq2000.profile
#+end_src
#+RESULTS:
#+begin_src sh :dir ~/code/bisonex/test-simuscop
cat > config_wes.txt << EOL
ref = genomeRef.fna
profile = ./Illumina_HiSeq2000.profile
variation = ./variant.txt
target = ./gatad2b-exon6.bed
layout = PE
threads = 1
name = single
output = test-gatad2b-hiseq2000
coverage = 20
EOL
simuReads config_wes.txt
bwa mem -R '@RG\tID:sample\tSM:sample\tPL:ILLUMINA\tPM:Miseq\tCN:lol\tLB:definition_to_add' bwa/genomeRef test-gatad2b-hiseq2000/single_1.fq  test-gatad2b-hiseq2000/single_2.fq | samtools sort  -o single-hiseq2000.bam
samtools index single-hiseq2000.bam
#+end_src
#+RESULTS:
****** KILL Tester exemple sur github
CLOSED: [2023-04-29 Sat 19:56]
#+begin_src sh
git clone https://github.com/qasimyu/simuscop/
cd simuscop
simuReads configFiles/config_test_wes.txt
#+end_src
****** KILL Centrer la fenêtre sur les zones de capture
CLOSED: [2023-04-30 Sun 13:28] SCHEDULED: <2023-04-29 Sat>
1000bp par défaut, ce qui est plus grand que les zones de captures...
Changer fragzip ne fonctionne pas
Si on rajoute un offset sur l'exon: 200bp, est encore plus allongé
NC_000001.11 153817371 153817542 ->
NC_000001.11 153817171 153817742
Si on désactive les target ?
Regarder les target sur le chromosome 1
#+begin_src sh :dir ~/code/bisonex/test-simuscop :results silent
scp meso:/Work/Projects/bisonex/data/simuscop/VCGS_Exome_Covered_Targets_hg38_40.1MB_renamed.bed .
#+end_src
#+begin_src sh :dir ~/code/bisonex/test-simuscop :results silent
head -n 100 VCGS_Exome_Covered_Targets_hg38_40.1MB_renamed.bed > 100exons.bed
echo -e "s\tsingle\tNC_000001.11\t153817496\tA\tT\thet"> variant.txt
cat > config_wes.txt << EOL
ref = genomeRef.fna
profile = ./63003856.profile
variation = ./variant.txt
layout = PE
threads = 4
target = 100exons.bed
name = single
output = test-gatad2b
coverage = 200
EOL
./simuscop/bin/simuReads config_wes.txt
bwa mem bwa/genomeRef test-gatad2b/single_1.fq  test-gatad2b/single_2.fq | samtools sort  -o single.bam
samtools index single.bam
#+end_src
**** KILL Vérifier tous les variants sont retrouvés
CLOSED: [2023-04-29 Sat 19:56] SCHEDULED: <2023-04-22 Sat>
***** DONE Après alignement
CLOSED: [2023-04-29 Sat 18:27] SCHEDULED: <2023-04-28 Fri>
****** DONE SNV: avec doublons
CLOSED: [2023-04-28 Fri 18:12]
On utilise [[file:~/recherche/bisonex/simuscop/checkBam.jl][checkBam.jl]]
#+begin_src julia
d = prepareVariant("../parsevariants/variant_genomic.csv")
root = "/home/alex/code/bisonex/simuscop-centogene/cento"
bam = root * "/preprocessing/applybqsr/cento.bam"
bai = root * "/preprocessing/recalibrated/cento.bam.bai"
snv = getSNV(d, bam, bai)
#+end_src
Nombreux faux homozygouteS
Vérification avec checkFalseHemizygous(snv) : nombreux doublons dans le fichier pour simuscop...
****** DONE SNV sans doublons
CLOSED: [2023-04-29 Sat 18:27]
******* DONE 18 faux homozygote mais avec peu de reads
CLOSED: [2023-04-29 Sat 18:27]
julia> @subset snv :refCount .== 0 :altCount .> 0 :zygosity .== "heterozygous"
18×10 DataFrame
 Row │ chrom         pos        variant         variantType  zygosity      ref        alt        refCount  altCount  readsCount
     │ SubStrin…?    Int64      SubStrin…?      String?      String15      SubStrin…  SubStrin…  Int64     Int64     Int64
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ NC_000022.11   42213078  g.42213078T>G   snv          heterozygous  T          G                 0         1           1
   2 │ NC_000012.12  101680427  g.101680427C>A  snv          heterozygous  C          A                 0         3           3
   3 │ NC_000014.9   105385684  g.105385684G>C  snv          heterozygous  G          C                 0         4           4
   4 │ NC_000011.10  125978299  g.125978299C>T  snv          heterozygous  C          T                 0         3           3
   5 │ NC_000023.11   77998618  g.77998618C>T   snv          heterozygous  C          T                 0         2           2
   6 │ NC_000015.10   66703292  g.66703292C>T   snv          heterozygous  C          T                 0         3           3
   7 │ NC_000010.11   87961118  g.87961118G>A   snv          heterozygous  G          A                 0         3           3
   8 │ NC_000012.12  112477719  g.112477719A>G  snv          heterozygous  A          G                 0         2           2
   9 │ NC_000020.11    6778406  g.6778406C>T    snv          heterozygous  C          T                 0         3           3
  10 │ NC_000023.11   68192943  g.68192943G>A   snv          heterozygous  G          A                 0         2           2
  11 │ NC_000004.12     987858  g.987858C>T     snv          heterozygous  C          T                 0         3           4
  12 │ NC_000015.10   66435145  g.66435145G>A   snv          heterozygous  G          A                 0         1           2
  13 │ NC_000002.12   47809595  g.47809595C>T   snv          heterozygous  C          T                 0         2           2
  14 │ NC_000003.12  136477305  g.136477305C>G  snv          heterozygous  C          G                 0         4           4
  15 │ NC_000005.10  157285458  g.157285458C>T  snv          heterozygous  C          T                 0         3           3
  16 │ NC_000012.12   23604413  g.23604413T>G   snv          heterozygous  T          G                 0         5           5
  17 │ NC_000019.10   52219703  g.52219703C>T   snv          heterozygous  C          T                 0         1           1
  18 │ NC_000016.10   88856757  g.88856757C>T   snv          heterozygous  C          T                 0         8           8
******* DONE 8 non retrouvé => probablement hors de la zjone de capture
CLOSED: [2023-04-28 Fri 19:49]
julia> @subset snv :refCount .== 0 :altCount .== 0
8×10 DataFrame
 Row │ chrom         pos        variant         variantType  zygosity      ref        alt        refCount  altCount  readsCount
     │ SubStrin…?    Int64      SubStrin…?      String?      String15      SubStrin…  SubStrin…  Int64     Int64     Int64
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ NC_000015.10   74343027  g.74343027C>T   snv          heterozygous  C          T                 0         0           0
   2 │ NC_000011.10   20638345  g.20638345A>G   snv          heterozygous  A          G                 0         0           0
   3 │ NC_000004.12  139370252  g.139370252C>T  snv          heterozygous  C          T                 0         0           2
   4 │ NC_000017.11   61966475  g.61966475G>T   snv          heterozygous  G          T                 0         0           0
   5 │ NC_000019.10   54144058  g.54144058G>A   snv          heterozygous  G          A                 0         0           0
   6 │ NC_000023.11   77635947  g.77635947A>G   snv          hemizygous    A          G                 0         0           0
   7 │ NC_000005.10    1258495  g.1258495G>A    snv          heterozygous  G          A                 0         0           0
   8 │ NC_000012.12    2449086  g.2449086C>G    snv          heterozygous  C          G                 0         0           0
***** KILL Après haplotypecaller
CLOSED: [2023-04-29 Sat 19:56] SCHEDULED: <2023-04-28 Fri>
****** KILL 20x
CLOSED: [2023-04-29 Sat 15:39]
Manque 183 sur 766
[[file:~/recherche/bisonex/simuscop/checkVCF.jl][checkVCF.jl]]
#+begin_src julia
@subset leftjoin(d2, dHaplo2, on=:genomic) ismissing.(:Column1)
#+end_src
Problème de profondeur ?
Ex: chr13 nombre de 101081606
NC_000011.10   16014966  g.16014966G>A
1 read sur 11 pour allèle alternative
Sur le patient de référence, 202 reads!
Celui-ci n'est pas le fichier de capture (ni dans le bam !)
ex: NC_000015.10   74343027  g.74343027C>T
Pour les autres, on devrait les retrouver...
Vérifier le nombre de reads sur 63003856
Vérifier la paramétrisation du modèle également
***** KILL Après annotation
CLOSED: [2023-04-29 Sat 19:56] SCHEDULED: <2023-04-28 Fri>
Il en manque !
***** KILL Après filtre annotation
CLOSED: [2023-04-29 Sat 19:56]
*** KILL NEAT : trop lent :neat:
CLOSED: [2023-04-29 Sat 22:06]
**** KILL Génération fastq sur exno 5 GATAD2B
CLOSED: [2023-04-29 Sat 22:06]
Trop lent : pour 1 exon : 1500 secondes !
#+begin_src sh
samtools faidx genomeRef.fna NC_000001.11 | save -f genomeRef_chr1.fna
python gen_reads.py  -r ../test-simuscop/genomeRef_chr1.fna -o lol  -tr ../test-simuscop/gatad2b-exon6.bed  -R 147 --pe 150 10
#+end_src
*** KILL ReSeq : ne gère pas des exons trop petits :reseq:
CLOSED: [2023-04-30 Sun 09:52] SCHEDULED: <2023-04-29 Sat>
#+begin_quote
Can I simulate exome sequencing? Yes. You need to use a reference that only contains the exons as individual scaffolds. Using --refBiasFile you can specify the coverage of individual exons. To simulate intron contamination you can add the whole reference to the reference containing the exons and strongly reduce the coverage for these scaffolds using --refBiasFile.
#+end_quote
**** KILL Fasta pour exons seuls
CLOSED: [2023-04-30 Sun 09:52]
Depuis le GFF
#+begin_src sh :dir ~/code/bisonex/test-reseq
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.25_GRCh37.p13/GCF_000001405.25_GRCh37.p13_genomic.gff.gz
#+end_src
#+RESULTS:
#+begin_src sh :dir ~/code/bisonex/test-reseq
gunzip -c GCF_000001405.25_GRCh37.p13_genomic.gff.gz | grep -w "exon" > exons.gff
#+end_src
#+RESULTS:
Attention, discordance entre les version de refseq..
On retélécharge fasata
#+begin_src
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.25_GRCh37.p13/GCF_000001405.25_GRCh37.p13_genomic.fna.gz
# Samtools wants bgzip
gunzip GCF_000001405.25_GRCh37.p13_genomic.fna.gz
samtools faidx GCF_000001405.25_GRCh37.p13_genomic.fna
#+end_src
#+begin_src sh :dir ~/code/bisonex/test-reseq
bedtools getfasta -fi GCF_000001405.25_GRCh37.p13_genomic.fna -bed exons.gff -fo exons.fa
#+end_src
A tester avec un profile déjà fait :
https://github.com/schmeing/ReSeq-profiles/tree/master/profiles
On cherche l'exons qui nous intéresse
 NC_000001.11 g.153817496 A>T
N'y est pas ??
On test sur les 2 premiers
#+begin_src
head exons.fa -n 2 > 2exons.fna
#+end_src
#+begin_src sh
../ReSeq/bin/reseq illuminaPE -j 32 -R exons.fa -s Ec-Hi2000-TruSeq.reseq --ipfIterations 0 -1 reseq-sim_1.fq reseq_sim_2.fq
#+end_src
#+begin_quote
error: All reference sequences are too short for simulating. They should have at least 1991 bases
#+end_quote
#+begin_src sh
grep '^>NC_000001.10' exons.fa  | sed 's/:/,/;s/-/,/;s/^>//' > exons.csv
#+end_src
*** DONE ART : fonctionne très mal en targeted
CLOSED: [2023-04-30 Sun 11:49] SCHEDULED: <2023-04-30 Sun>
**** DONE Génération de reads
CLOSED: [2023-04-30 Sun 11:49]
***** DONE Avec seulement les exons en séquence
CLOSED: [2023-04-30 Sun 10:24]
 head -n6 exons.fa | save three-exons.fna
../art_bin_MountRainier/art_illumina -ss HS25 -i three-exons.fna -o ./paired_end_com -l 150 -f 10 -p -m 500 -s 10 -sam
Le sam n'est pas visible sur igv mais si on aligne avec bwa mem, on a quelques reads
***** DONE Extraire une zone de capture dans le fasta
CLOSED: [2023-04-30 Sun 11:49]
 NC_000001.11 g.153817496 A>T
****** DONE Essai 1: ne dépasse pas la zone
CLOSED: [2023-04-30 Sun 10:49]
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153817371\t153817542" > gatad2b-exon6.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6.bed -fo gatad2b-exon6.fa
#+end_src
-ss HS25 : nom du profile illumina
-l 150 : reads de 150
-f 10 : coverage de 10
-p : paired end
-m 500 : longueur moyenne des fragment d'ADN
-s 10 : déviation standard
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6.fa -o ./gatad2b -l 150 -f 100 -p -m 500 -s 10
#+end_src
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.b
am
samtools index gatad2b.bam
#+end_src
#+attr_html: :width 800px
[[./art-capture-1.png]]
****** Avec offset
50bp idem
NC_000001.11 153817371  153817542 -> NC_000001.11 153817321  153817592
On essaie 1000
NC_000001.11 153817371  153817542 -> NC_000001.11 153816371  153818542 ->
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153816371\t153818542" > gatad2b-exon6-offset.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6-offset.bed -fo gatad2b-exon6-offset.fa
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6-offset.fa -o ./gatad2b -l 150 -f 100 -p -m 500 -s 10
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.bam
samtools index gatad2b.bam
#+end_src
mieux mais trop large
#+attr_html: :width 800px
[[./art-exon6-offset1000.png]]
Sur les vraies données, on a une large de 500bp environ
#+attr_html: :width 800px
[[./illumina-ref-exon6.png]]
Ici l'exon fait 250bp donc on rajouter 125bp de chaque côté
NC_000001.11 153817371  153817542
NC_000001.11 153817246  153817667
Résulats incohérents :on a 2 colonnes séparées !
Essai avec +200bp de chaque côt
NC_000001.11 153817371  153817542
NC_000001.11 153817171  153817742
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153817171\t153817742"> gatad2b-exon6-offset.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6-offset.bed -fo gatad2b-exon6-offset.fa
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6-offset.fa -o ./gatad2b -l 150 -f 100 -p -m 500 -s 50
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.bam
samtools index gatad2b.bam
#+end_src
En changeant la longueur des reads
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153817171\t153817742"> gatad2b-exon6-offset.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6-offset.bed -fo gatad2b-exon6-offset.fa
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6-offset.fa -o ./gatad2b -l 126 -f 100 -p -m 500 -s 50
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.bam
samtools index gatad2b.bam
#+end_src
Idem, fait 2 "tas" séparés de chaque côté de l'exon
+/- 100
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153817271\t153817642" > gatad2b-exon6-offset.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6-offset.bed -fo gatad2b-exon6-offset.fa
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6-offset.fa -o ./gatad2b -l 125 -f 100 -p -m 500 -s 30
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.bam
samtools index gatad2b.bam
#+end_src
*** NGSNG
https://github.com/RAHenriksen/NGSNGS
**** Essai avec fasta
#+begin_src sh :dir ~/code/bisonex/test-ngsngs :results silent
echo -e "NC_000001.11\t153817371\t153817542" > gatad2b-exon6.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6.bed -fo gatad2b-exon6.fa
#+end_src
-ss HS25 : nom du profile illumina
-l 150 : reads de 150
-f 10 : coverage de 10
-p : paired end
-m 500 : longueur moyenne des fragment d'ADN
-s 10 : déviation standard
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6.fa -o ./gatad2b -l 150 -f 100 -p -m 500 -s 10
#+end_src
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.b
am
samtools index gatad2b.bam
#+end_src
*** Divers
**** DONE Vérifier nombre de reads fastq - bam
CLOSED: [2022-10-09 Sun 22:31]
* DONE Plot : ashkenazim trio
CLOSED: [2023-04-18 Tue 21:28] SCHEDULED: <2023-04-16 Sun>
/Entered on/ [2023-04-16 Sun 17:29]

[9.24645]

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-mobidetails.csv",
                    "recoded-failures-mobidetails-redo.csv")
    dInit = @chain DataFrame(CSV.File("variant_success.csv")) begin
        @transform :transcript = :transcript .* ":" .* :coding .* :codingPos .* :codingChange
        @select :file :transcript :classification :zygosity
        @rename :classificationCentogene = :classification
    end
    dTmp = outerjoin(dInit, dNew, on = :transcript)
    CSV.write("variant_genomic.csv", dTmp)
end
fSuccess = "recoded-success.csv"
fFailures = "recoded-failures.csv"
# variantRecoder(fSuccess, fFailures)
# mobidetailsOnFailures(fFailures)
# lastTryMobidetails("recoded-failures-mobidetails.csv")
mergeAllGenomic()
#+end_src
**** DONE Formatter donner pour simuscop
CLOSED: [2023-04-28 Fri 11:55] SCHEDULED: <2023-04-26 Wed>
*** TODO Extraire liste des CNVs
SCHEDULED: <2023-04-17 Mon>
*** TODO Simuscop :simuscop:
**** DONE Entrainer le modèle sur 63003856/
CLOSED: [2023-04-29 Sat 19:56]
Relancer le modèle pour être sûr
**** DONE Générer fastq avec simuscop (del et ins seulement) 20x
CLOSED: [2023-04-28 Fri 23:35] SCHEDULED: <2023-04-22 Sat>
***** DONE Génerer un profile avec bed de centogène
CLOSED: [2023-04-28 Fri 11:54] SCHEDULED: <2023-04-22 Sat>
NA12878 mais à refaire avec un vrai séquencage
Voir [[*Centogène][Bed Centogène]] pour choix
***** DONE Générer les données en 20x
CLOSED: [2023-04-28 Fri 11:54] SCHEDULED: <2023-04-22 Sat>
capture de centogene
***** DONE Regénérer en supprimant les doublons
CLOSED: [2023-04-28 Fri 17:28]
**** DONE Quelle couverture ?
CLOSED: [2023-04-29 Sat 18:26]
ex sur chr11:16,014,966 où on a 11 reads dans la simulation contre 200 !
***** 200 est la plus proche
#+attr_html: :width 500px
[[./simuscop-200-chr1-1.png]]
#+attr_html: :width 500px
[[./simuscop-200-chr1-2.png]]
***** DONE 20x
CLOSED: [2023-04-29 Sat 15:38]
***** DONE 50x
CLOSED: [2023-04-29 Sat 15:38]
***** DONE 100x
CLOSED: [2023-04-29 Sat 15:39]
***** DONE 200x
CLOSED: [2023-04-29 Sat 15:39]
**** DONE Reads mal centrés sur des petits exons seuls
CLOSED: [2023-04-29 Sat 19:56] SCHEDULED: <2023-04-29 Sat>
Capture ok : [[https://genome-euro.ucsc.edu/cgi-bin/hgTracks?db=hg38&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr1%3A153817168%2D153817824&hgsid=296556270_F4fkENLPXHXidi2oALXls2jxNH9l][UCSC]] (track noire)
Mais mauvaise répartitiopn
#+attr_html: :width 800px
[[./simuscop-error.png]]
À tester
- Problème de profile ?
  - mauvais patient ?
  - mauvaise génération ? -> comparer avec ceux donnés sur github
- nom des chromosomes ?
***** DONE [#A] Tester sur exon 6 GATAD2B pour NC_000001.11:g.153817496A>T
CLOSED: [2023-04-29 Sat 19:56] SCHEDULED: <2023-04-29 Sat>
****** DONE Configuration + Profile 63003856.profile: idem, mal centré
CLOSED: [2023-04-29 Sat 19:18]
Téléchargement des données
#+begin_src sh :dir ~/code/bisonex/test-simuscop
scp meso:/Work/Projects/bisonex/data/genome/GRCh38.p14/genomeRef.fna .
scp meso:Work/Projects/bisonex/data/simuscop/*.profile .
scp -r meso:/Work/Projects/bisonex/data/genome/GRCh38.p13/bwa .
#+end_src
On récupère l'exon (NB: org-mode ne lance pas le code...)
#+begin_src julia
using CSV,DataFramesMeta
d = CSV.read("VCGS_Exome_Covered_Targets_hg38_40.1MB_renamed.bed", header=false, delim="\t", DataFrame)
@subset d :Column1 .== "NC_000001.11" :Column2 .<= 153817496 :Column3 .>= 153817496
#+end_src
NC_000001.11  153817371  153817542
Génération du bed
#+begin_src sh :dir ~/code/bisonex/test-simuscop
echo -e "NC_000001.11\t153817371\t153817542" > gatad2b-exon6.bed
#+end_src
#+RESULTS:
Génération d'un variant
#+begin_src sh :dir ~/code/bisonex/test-simuscop
echo -e "s\tsingle\tNC_000001.11\t153817496\tA\tT\thet"> variant.txt
#+end_src
#+RESULTS:
Génération du fichier de config
#+begin_src sh :dir ~/code/bisonex/test-simuscop
cat > config_wes.txt << EOL
ref = genomeRef.fna
profile = ./63003856.profile
variation = ./variant.txt
target = ./gatad2b-exon6.bed
layout = PE
threads = 1
name = single
output = test-gatad2b
coverage = 20
EOL
#+end_src
#+RESULTS:
On démarre la simulation
#+begin_src sh :dir ~/code/bisonex/test-simuscop
simuReads config_wes.txt
#+end_src
#+RESULTS:
Alignement
#+begin_src sh :dir ~/code/bisonex/test-simuscop
bwa mem -R '@RG\tID:sample\tSM:sample\tPL:ILLUMINA\tPM:Miseq\tCN:lol\tLB:definition_to_add' bwa/genomeRef test-gatad2b/single_1.fq  test-gatad2b/single_2.fq | samtools sort  -o single.bam
#+end_src
#+RESULTS:
****** DONE Profile github  HiSeq2000
CLOSED: [2023-04-29 Sat 19:56]
#+begin_src sh :dir ~/code/bisonex/test-simuscop :result file
wget https://raw.githubusercontent.com/qasimyu/simuscop/master/testData/Illumina_HiSeq2000.profile
#+end_src
#+RESULTS:
#+begin_src sh :dir ~/code/bisonex/test-simuscop
cat > config_wes.txt << EOL
ref = genomeRef.fna
profile = ./Illumina_HiSeq2000.profile
variation = ./variant.txt
target = ./gatad2b-exon6.bed
layout = PE
threads = 1
name = single
output = test-gatad2b-hiseq2000
coverage = 20
EOL
simuReads config_wes.txt
bwa mem -R '@RG\tID:sample\tSM:sample\tPL:ILLUMINA\tPM:Miseq\tCN:lol\tLB:definition_to_add' bwa/genomeRef test-gatad2b-hiseq2000/single_1.fq  test-gatad2b-hiseq2000/single_2.fq | samtools sort  -o single-hiseq2000.bam
samtools index single-hiseq2000.bam
#+end_src
#+RESULTS:
****** KILL Tester exemple sur github
CLOSED: [2023-04-29 Sat 19:56]
#+begin_src sh
git clone https://github.com/qasimyu/simuscop/
cd simuscop
simuReads configFiles/config_test_wes.txt
#+end_src
****** KILL Centrer la fenêtre sur les zones de capture
CLOSED: [2023-04-30 Sun 13:28] SCHEDULED: <2023-04-29 Sat>
1000bp par défaut, ce qui est plus grand que les zones de captures...
Changer fragzip ne fonctionne pas
Si on rajoute un offset sur l'exon: 200bp, est encore plus allongé
NC_000001.11 153817371 153817542 ->
NC_000001.11 153817171 153817742
Si on désactive les target ?
Regarder les target sur le chromosome 1
#+begin_src sh :dir ~/code/bisonex/test-simuscop :results silent
scp meso:/Work/Projects/bisonex/data/simuscop/VCGS_Exome_Covered_Targets_hg38_40.1MB_renamed.bed .
#+end_src
#+begin_src sh :dir ~/code/bisonex/test-simuscop :results silent
head -n 100 VCGS_Exome_Covered_Targets_hg38_40.1MB_renamed.bed > 100exons.bed
echo -e "s\tsingle\tNC_000001.11\t153817496\tA\tT\thet"> variant.txt
cat > config_wes.txt << EOL
ref = genomeRef.fna
profile = ./63003856.profile
variation = ./variant.txt
layout = PE
threads = 4
target = 100exons.bed
name = single
output = test-gatad2b
coverage = 200
EOL
./simuscop/bin/simuReads config_wes.txt
bwa mem bwa/genomeRef test-gatad2b/single_1.fq  test-gatad2b/single_2.fq | samtools sort  -o single.bam
samtools index single.bam
#+end_src
**** TODO Vérifier tous les variants sont retrouvés
SCHEDULED: <2023-04-22 Sat>
***** DONE Après alignement
CLOSED: [2023-04-29 Sat 18:27] SCHEDULED: <2023-04-28 Fri>
****** DONE SNV: avec doublons
CLOSED: [2023-04-28 Fri 18:12]
On utilise [[file:~/recherche/bisonex/simuscop/checkBam.jl][checkBam.jl]]
#+begin_src julia
d = prepareVariant("../parsevariants/variant_genomic.csv")
root = "/home/alex/code/bisonex/simuscop-centogene/cento"
bam = root * "/preprocessing/applybqsr/cento.bam"
bai = root * "/preprocessing/recalibrated/cento.bam.bai"
snv = getSNV(d, bam, bai)
#+end_src
Nombreux faux homozygouteS
Vérification avec checkFalseHemizygous(snv) : nombreux doublons dans le fichier pour simuscop...
****** DONE SNV sans doublons
CLOSED: [2023-04-29 Sat 18:27]
******* DONE 18 faux homozygote mais avec peu de reads
CLOSED: [2023-04-29 Sat 18:27]
julia> @subset snv :refCount .== 0 :altCount .> 0 :zygosity .== "heterozygous"
18×10 DataFrame
 Row │ chrom         pos        variant         variantType  zygosity      ref        alt        refCount  altCount  readsCount
     │ SubStrin…?    Int64      SubStrin…?      String?      String15      SubStrin…  SubStrin…  Int64     Int64     Int64
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ NC_000022.11   42213078  g.42213078T>G   snv          heterozygous  T          G                 0         1           1
   2 │ NC_000012.12  101680427  g.101680427C>A  snv          heterozygous  C          A                 0         3           3
   3 │ NC_000014.9   105385684  g.105385684G>C  snv          heterozygous  G          C                 0         4           4
   4 │ NC_000011.10  125978299  g.125978299C>T  snv          heterozygous  C          T                 0         3           3
   5 │ NC_000023.11   77998618  g.77998618C>T   snv          heterozygous  C          T                 0         2           2
   6 │ NC_000015.10   66703292  g.66703292C>T   snv          heterozygous  C          T                 0         3           3
   7 │ NC_000010.11   87961118  g.87961118G>A   snv          heterozygous  G          A                 0         3           3
   8 │ NC_000012.12  112477719  g.112477719A>G  snv          heterozygous  A          G                 0         2           2
   9 │ NC_000020.11    6778406  g.6778406C>T    snv          heterozygous  C          T                 0         3           3
  10 │ NC_000023.11   68192943  g.68192943G>A   snv          heterozygous  G          A                 0         2           2
  11 │ NC_000004.12     987858  g.987858C>T     snv          heterozygous  C          T                 0         3           4
  12 │ NC_000015.10   66435145  g.66435145G>A   snv          heterozygous  G          A                 0         1           2
  13 │ NC_000002.12   47809595  g.47809595C>T   snv          heterozygous  C          T                 0         2           2
  14 │ NC_000003.12  136477305  g.136477305C>G  snv          heterozygous  C          G                 0         4           4
  15 │ NC_000005.10  157285458  g.157285458C>T  snv          heterozygous  C          T                 0         3           3
  16 │ NC_000012.12   23604413  g.23604413T>G   snv          heterozygous  T          G                 0         5           5
  17 │ NC_000019.10   52219703  g.52219703C>T   snv          heterozygous  C          T                 0         1           1
  18 │ NC_000016.10   88856757  g.88856757C>T   snv          heterozygous  C          T                 0         8           8
******* DONE 8 non retrouvé => probablement hors de la zjone de capture
CLOSED: [2023-04-28 Fri 19:49]
julia> @subset snv :refCount .== 0 :altCount .== 0
8×10 DataFrame
 Row │ chrom         pos        variant         variantType  zygosity      ref        alt        refCount  altCount  readsCount
     │ SubStrin…?    Int64      SubStrin…?      String?      String15      SubStrin…  SubStrin…  Int64     Int64     Int64
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ NC_000015.10   74343027  g.74343027C>T   snv          heterozygous  C          T                 0         0           0
   2 │ NC_000011.10   20638345  g.20638345A>G   snv          heterozygous  A          G                 0         0           0
   3 │ NC_000004.12  139370252  g.139370252C>T  snv          heterozygous  C          T                 0         0           2
   4 │ NC_000017.11   61966475  g.61966475G>T   snv          heterozygous  G          T                 0         0           0
   5 │ NC_000019.10   54144058  g.54144058G>A   snv          heterozygous  G          A                 0         0           0
   6 │ NC_000023.11   77635947  g.77635947A>G   snv          hemizygous    A          G                 0         0           0
   7 │ NC_000005.10    1258495  g.1258495G>A    snv          heterozygous  G          A                 0         0           0
   8 │ NC_000012.12    2449086  g.2449086C>G    snv          heterozygous  C          G                 0         0           0
***** TODO Après haplotypecaller
SCHEDULED: <2023-04-28 Fri>
****** KILL 20x
CLOSED: [2023-04-29 Sat 15:39]
Manque 183 sur 766
[[file:~/recherche/bisonex/simuscop/checkVCF.jl][checkVCF.jl]]
#+begin_src julia
@subset leftjoin(d2, dHaplo2, on=:genomic) ismissing.(:Column1)
#+end_src
Problème de profondeur ?
Ex: chr13 nombre de 101081606
NC_000011.10   16014966  g.16014966G>A
1 read sur 11 pour allèle alternative
Sur le patient de référence, 202 reads!
Celui-ci n'est pas le fichier de capture (ni dans le bam !)
ex: NC_000015.10   74343027  g.74343027C>T
Pour les autres, on devrait les retrouver...
Vérifier le nombre de reads sur 63003856
Vérifier la paramétrisation du modèle également
****** TODO 200x
SCHEDULED: <2023-04-30 Sun>
120 manquants !
Procédure : https://gatk.broadinstitute.org/hc/en-us/articles/360043491652-When-HaplotypeCaller-and-Mutect2-do-not-call-an-expected-variant
On vérifie dans IGV (vcf + bam après alignement) :
******* snv NC_000015.10   74343027
- rien d'appelé
- pas une région répétée
- base quality (voir [[*Phred score][Phred score]] ) à 37 donc ok
- variant retrouvé à 26/42
- Bam après aplybqsr: base qualità 35 donc ok
chr15 également à 89318565, variant retrouvé à 25/33 avec basequal de 37
Sans oublier de charger les instructions avx
#+begin_src sh
module load gcc@11.3.0/gcc-12.1.0
#+end_src
On coupe le .bam par chromosome pour débugger (sur le mesocentre)
#+begin_src sh :dir /ssh:meso:/Work/Users/apraga/bisonex/simuscop-centogene-200x/cento/testing :results silent
ln -s ../preprocessing/applybqsr/cento.bam .
ln -s ../preprocessing/recalibrated/cento.bam.bai .
ln -s /Work/Projects/bisonex/data/dbSNP/GRCh38.p13/dbSNP.gz .
ln -s /Work/Projects/bisonex/data/dbSNP/GRCh38.p13/dbSNP.gz.tbi .
ln -s /Work/Projects/bisonex/data/genome/GRCh38.p13/genomeRef.dict .
ln -s /Work/Projects/bisonex/data/genome/GRCh38.p13/genomeRef.fna .
ln -s /Work/Projects/bisonex/data/genome/GRCh38.p13/genomeRef.fna.fai .
#+end_src
On doit lancer à la main (org-mode ne connait pas le chemin de samtools)
samtools view -b cento.bam NC_000015.10 > cento_chr15.bam
samtools index cento_chr15.bam
Puis on se restreint au chronmosome 15
samtools faidx genomeRef.fna NC_000015.10 > genomeRef_chr15.fa
samtools faidx genomeRef_chr15.fa
gatk CreateSequenceDictionary -R genomeRef_chr15.fa -O genomeRef_chr15.dict
On restreint au chromosome 15 avec l'option -L (dure = 1min)
gatk --java-options "-Xmx3072M" HaplotypeCaller --input cento_chr15.bam \
    --output test.vcf.gz --reference genomeRef.fna --dbsnp dbSNP.gz --tmp-dir . --max-mnp-distance 2 -L NC_000015.10
******* DONE Supprimer --max-mnp-distance = 2: idem
CLOSED: [2023-04-30 Sun 15:42]
******* DONE --debug &> run.log : Non appelé...
CLOSED: [2023-04-30 Sun 15:52]
******* DONE --linked-de-bruijn-graph: idem
CLOSED: [2023-04-30 Sun 15:55]
******* DONE --recover-all-dangling-branches
CLOSED: [2023-04-30 Sun 16:01]
******* DONE --min-pruning 0 : plus mais pas celui là
CLOSED: [2023-04-30 Sun 15:59]
******* DONE --bam-output
CLOSED: [2023-04-30 Sun 16:50]
******** DONE : rien !
CLOSED: [2023-04-30 Sun 16:08]
******** DONE + --recover-all-dangling-branches : rien !
CLOSED: [2023-04-30 Sun 16:08]
******* DONE Données filtrées ? apparement non
CLOSED: [2023-04-30 Sun 16:41]
183122 read(s) filtered by: MappingQualityReadFilter
3674 read(s) filtered by: NotDuplicateReadFilter
******** DONE --disable-read-filter MappingQualityReadFilter: idem
CLOSED: [2023-04-30 Sun 16:34]
On a bien  - 0 read(s) filtered by: MappingQualityAvailableReadFilter
******** DONE --disable-read-filter NotDuplicateReadFilter: idem
CLOSED: [2023-04-30 Sun 16:40]
******* DONE Essayer freebayes : idem
CLOSED: [2023-04-30 Sun 16:22]
freebayes -f genomeRef.fna -r NC_000015.10 cento_chr15.bam > freebayes-test-chr15.vcf
******* DONE Avec toutes les options : idem
--linked-de-bruijn-graph --recover-all-dangling-branches --min-pruning 0 --bam-output debug.bam
CLOSED: [2023-04-30 Sun 16:50]
******* DONE Vérifier qu'on regarde le même bam : oui
CLOSED: [2023-04-30 Sun 16:50]
******* DONE Désactiver dbSNP : idem
CLOSED: [2023-04-30 Sun 16:52]
******* DONE Changer kmer size : idem
CLOSED: [2023-04-30 Sun 16:56]
par exemple[[https://gatk.broadinstitute.org/hc/en-us/community/posts/360075653152-REAL-Variant-not-called-by-HaplotypeCaller][forum gatk]] --kmer-size 18 --kmer-size 22
******* TODO  --adaptive-pruning true
***** TODO Après annotation
SCHEDULED: <2023-04-28 Fri>
***** TODO Après filtre annotation
*** KILL NEAT : trop lent :neat:
CLOSED: [2023-04-29 Sat 22:06]
**** KILL Génération fastq sur exno 5 GATAD2B
CLOSED: [2023-04-29 Sat 22:06]
Trop lent : pour 1 exon : 1500 secondes !
#+begin_src sh
samtools faidx genomeRef.fna NC_000001.11 | save -f genomeRef_chr1.fna
python gen_reads.py  -r ../test-simuscop/genomeRef_chr1.fna -o lol  -tr ../test-simuscop/gatad2b-exon6.bed  -R 147 --pe 150 10
#+end_src
*** KILL ReSeq : ne gère pas des exons trop petits :reseq:
CLOSED: [2023-04-30 Sun 09:52] SCHEDULED: <2023-04-29 Sat>
#+begin_quote
Can I simulate exome sequencing? Yes. You need to use a reference that only contains the exons as individual scaffolds. Using --refBiasFile you can specify the coverage of individual exons. To simulate intron contamination you can add the whole reference to the reference containing the exons and strongly reduce the coverage for these scaffolds using --refBiasFile.
#+end_quote
**** KILL Fasta pour exons seuls
CLOSED: [2023-04-30 Sun 09:52]
Depuis le GFF
#+begin_src sh :dir ~/code/bisonex/test-reseq
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.25_GRCh37.p13/GCF_000001405.25_GRCh37.p13_genomic.gff.gz
#+end_src
#+RESULTS:
#+begin_src sh :dir ~/code/bisonex/test-reseq
gunzip -c GCF_000001405.25_GRCh37.p13_genomic.gff.gz | grep -w "exon" > exons.gff
#+end_src
#+RESULTS:
Attention, discordance entre les version de refseq..
On retélécharge fasata
#+begin_src
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.25_GRCh37.p13/GCF_000001405.25_GRCh37.p13_genomic.fna.gz
# Samtools wants bgzip
gunzip GCF_000001405.25_GRCh37.p13_genomic.fna.gz
samtools faidx GCF_000001405.25_GRCh37.p13_genomic.fna
#+end_src
#+begin_src sh :dir ~/code/bisonex/test-reseq
bedtools getfasta -fi GCF_000001405.25_GRCh37.p13_genomic.fna -bed exons.gff -fo exons.fa
#+end_src
A tester avec un profile déjà fait :
https://github.com/schmeing/ReSeq-profiles/tree/master/profiles
On cherche l'exons qui nous intéresse
 NC_000001.11 g.153817496 A>T
N'y est pas ??
On test sur les 2 premiers
#+begin_src
head exons.fa -n 2 > 2exons.fna
#+end_src
#+begin_src sh
../ReSeq/bin/reseq illuminaPE -j 32 -R exons.fa -s Ec-Hi2000-TruSeq.reseq --ipfIterations 0 -1 reseq-sim_1.fq reseq_sim_2.fq
#+end_src
#+begin_quote
error: All reference sequences are too short for simulating. They should have at least 1991 bases
#+end_quote
#+begin_src sh
grep '^>NC_000001.10' exons.fa  | sed 's/:/,/;s/-/,/;s/^>//' > exons.csv
#+end_src
*** DONE ART : fonctionne très mal en targeted
CLOSED: [2023-04-30 Sun 11:49]
**** DONE Génération de reads
CLOSED: [2023-04-30 Sun 11:49]
***** DONE Avec seulement les exons en séquence
CLOSED: [2023-04-30 Sun 10:24]
 head -n6 exons.fa | save three-exons.fna
../art_bin_MountRainier/art_illumina -ss HS25 -i three-exons.fna -o ./paired_end_com -l 150 -f 10 -p -m 500 -s 10 -sam
Le sam n'est pas visible sur igv mais si on aligne avec bwa mem, on a quelques reads
***** DONE Extraire une zone de capture dans le fasta
CLOSED: [2023-04-30 Sun 11:49]
 NC_000001.11 g.153817496 A>T
****** DONE Essai 1: ne dépasse pas la zone
CLOSED: [2023-04-30 Sun 10:49]
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153817371\t153817542" > gatad2b-exon6.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6.bed -fo gatad2b-exon6.fa
#+end_src
-ss HS25 : nom du profile illumina
-l 150 : reads de 150
-f 10 : coverage de 10
-p : paired end
-m 500 : longueur moyenne des fragment d'ADN
-s 10 : déviation standard
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6.fa -o ./gatad2b -l 150 -f 100 -p -m 500 -s 10
#+end_src
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.b
am
samtools index gatad2b.bam
#+end_src
#+attr_html: :width 800px
[[./art-capture-1.png]]
****** Avec offset
50bp idem
NC_000001.11 153817371  153817542 -> NC_000001.11 153817321  153817592
On essaie 1000
NC_000001.11 153817371  153817542 -> NC_000001.11 153816371  153818542 ->
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153816371\t153818542" > gatad2b-exon6-offset.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6-offset.bed -fo gatad2b-exon6-offset.fa
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6-offset.fa -o ./gatad2b -l 150 -f 100 -p -m 500 -s 10
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.bam
samtools index gatad2b.bam
#+end_src
mieux mais trop large
#+attr_html: :width 800px
[[./art-exon6-offset1000.png]]
Sur les vraies données, on a une large de 500bp environ
#+attr_html: :width 800px
[[./illumina-ref-exon6.png]]
Ici l'exon fait 250bp donc on rajouter 125bp de chaque côté
NC_000001.11 153817371  153817542
NC_000001.11 153817246  153817667
Résulats incohérents :on a 2 colonnes séparées !
Essai avec +200bp de chaque côt
NC_000001.11 153817371  153817542
NC_000001.11 153817171  153817742
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153817171\t153817742"> gatad2b-exon6-offset.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6-offset.bed -fo gatad2b-exon6-offset.fa
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6-offset.fa -o ./gatad2b -l 150 -f 100 -p -m 500 -s 50
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.bam
samtools index gatad2b.bam
#+end_src
En changeant la longueur des reads
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153817171\t153817742"> gatad2b-exon6-offset.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6-offset.bed -fo gatad2b-exon6-offset.fa
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6-offset.fa -o ./gatad2b -l 126 -f 100 -p -m 500 -s 50
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.bam
samtools index gatad2b.bam
#+end_src
Idem, fait 2 "tas" séparés de chaque côté de l'exon
+/- 100
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
echo -e "NC_000001.11\t153817271\t153817642" > gatad2b-exon6-offset.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6-offset.bed -fo gatad2b-exon6-offset.fa
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6-offset.fa -o ./gatad2b -l 125 -f 100 -p -m 500 -s 30
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.bam
samtools index gatad2b.bam
#+end_src
*** NGSNG
https://github.com/RAHenriksen/NGSNGS
**** Essai avec fasta
#+begin_src sh :dir ~/code/bisonex/test-ngsngs :results silent
echo -e "NC_000001.11\t153817371\t153817542" > gatad2b-exon6.bed
bedtools getfasta -fi ../test-simuscop/genomeRef.fna -bed gatad2b-exon6.bed -fo gatad2b-exon6.fa
#+end_src
-ss HS25 : nom du profile illumina
-l 150 : reads de 150
-f 10 : coverage de 10
-p : paired end
-m 500 : longueur moyenne des fragment d'ADN
-s 10 : déviation standard
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
../art_bin_MountRainier/art_illumina -ss HS25 -i gatad2b-exon6.fa -o ./gatad2b -l 150 -f 100 -p -m 500 -s 10
#+end_src
#+begin_src sh :dir ~/code/bisonex/test-art :results silent
bwa mem ../test-simuscop/bwa/genomeRef gatad2b1.fq gatad2b2.fq | samtools sort -o gatad2b.b
am
samtools index gatad2b.bam
#+end_src
*** Divers
**** DONE Vérifier nombre de reads fastq - bam
CLOSED: [2022-10-09 Sun 22:31]
* DONE Plot : ashkenazim trio
CLOSED: [2023-04-18 Tue 21:28] SCHEDULED: <2023-04-16 Sun>
/Entered on/ [2023-04-16 Sun 17:29]