Workflow. Reference Genome. Variant Calling. Galaxy Format Conversion Groomer. Mapping BWA GATK Preprocess

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A L I M E N T A T I O N

S. Marthey / O. Rué

Fastq

Reference Genome

Galaxy

Format

Conversion

---

Groomer

Quality

Control

---

FastQC

Mapping

---

BWA

Format

conversion

---

Sam-to-Bam

Removing PCR

duplicates

---

MarkDup

Preprocess

GATK

---

Indel

Realignment

Variant

Calling

GATK

---Unified

Genotyper

Variant

Calling

VarScan

VCF

Filtering

VCF

Annotation

Preprocess

GATK

---

Base

Recalibration

Mpileup

Workflow

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S. Marthey / O. Rué

Plan

•

Introduction

•

Prétraitements des données NGS

•

Recherche de Variants

•

Pourquoi faire la Real/Recab ?

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The Genome Analysis Toolkit : A MapReduce framework for analyzing next-generation DNA

sequencing data

, McKenna et al. (2010)

•

GATK : Genome Analysis ToolKit

•

http://www.broadinstitute.org/gatk/about/

•

Développé par l'équipe de développement du Broad Institute (USA)

•

Utilisé dans de nombreux projets (1000 Genomes Project, The Cancer Genome

Atlas...)

•

A la base développé pour génetique humaine mais maintenant générique

•

Développé en Java

•

Citations :

Sources

2010

2011

2012

2013

GATK Website*

2

9

25

…

Google Scholar

28

145

436

767

* Nature, Science, Nature Genetics, Nature Biotechnology, New England Journal of Medicine, Cell, and Genome Research.

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Comment est détecté un SNP ?

Complex bayesian algorithms based on :

Base scale Read scale Position scale Genotype scale

ALT allele count

REF allele count

ALT / REF Read Depth Overall genotype association SNP quality Forward/Reverse

Phred-Quality Base Mapping quality

10 => P_error = 1 / 10 30 => P_error = 1 / 1000

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S. Marthey / O. Rué

Comment est détecté un SNP ?

Biais de séquençage connus:

– GA / Hi-Seq : Base Quality

– 454 : Homopolymères

– SOLiD : Base Quality + Color space traduction

Base scale Read scale Position scale Genotype scale

ALT allele count

REF allele count

ALT / REF Read Depth Overall genotype association SNP quality Forward/Reverse

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S. Marthey / O. Rué

Raw reads

• Produits par les logiciels des

Séquenceurs

• Une première étape de

recalibration/correction des reads

peut être effectuée :

– 454 : Pyrobayes / Pyrocleaner

– SOLiD : Rsolid

– Illumina : Ibis

/BayesCall

+

Taux erreur amélioré de 5 à 30 %

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Raw reads

• Fastq

• csFasta + Qual

@HISEQ4_0105:4:1101:1533:1998#TAGCTT/1 NATAAATGCTGTCATACAGACTTGTTGGTGTTGTAAGGCAGCAGACTCCTTTGAGCTTTCATCCGAGAACAATTGAGACTAAATTCCTGGTGCAAAGTCCA +HISEQ4_0105:4:1101:1533:1998#TAGCTT/1 BP\cceeegffgghfhiiiefgihhhii[baegegfgiiiihhiiihhfhfhighihiiifhhfihieeegaceeedcdddd`bcbcccbbcbcccccbcb @HISEQ4_0105:4:1101:2421:1947#TAGCTT/1 NAAGAAGGCACGAAGCAACTACTTCACTGCATGCTGCCTGTCCTTGGGCTGTTTGCTGCCTTTGGCTAACACCTTTGATTATTTCTGGCTAAGTAGATAGG +HISEQ4_0105:4:1101:2421:1947#TAGCTT/1 BS\ceeeegggfgiiiiiiiiiiiiiiiiiiiiiiiiiiihiiiiihhghihhihiiiiiihhiihgggfgeeedddddddbededcccc`bcbeccddcc @HISEQ4_0105:4:1101:3251:1984#TAGCTT/1 NAGAGCTATTTATGAAAACGAGGATGACTAAAACTGCCCAGAAAAAAAACCAACCAACCACGTTTCCAGTGACTGCCACCCTTAGCAAGCAAGGTAATAAC

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Mapping

• Alignement reads VS Génome de

référence

• Tout logiciel produisant des BAM

– Ex: BWA, Bowtie, Gsnap, SOAP, SSAHA

http://seqanswers.com/forums/showthread.php?t=43

1 fichier par lane / individu / condition

ou

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Mapping

PHOSPHORE:181:C0KD3ACXX:8:2101:3676:147949 73 13 10354712 37 101M = 10354712 0

GCCTAGTCCTTTGAGACAGGAGTAAGACAAGAACTCAGGTTAGGGACCTCAAGGACTTGCTGAAGCCCACAAAGATTAGGACAAGCTAATGGAACTCAGAC

@@CFDFDFHGHHHIIJJJIJJJCFHIJIJIIFIJJJIJECFGGIGJIIJIJIIJJIGIIIIGGIJJJIGHHEFDFFFDDDCCED?BDDCCDDCDDDDCAC: X0:i:1 X1:i:0 MD:Z:101 RG:Z:ind1 XG:i:0 AM:i:0 NM:i:0 SM:i:37 XM:i:0 XO:i:0 XT:A:U

PHOSPHORE:181:C0KD3ACXX:8:2101:3676:147949 133 13 10354712 0 * = 10354712 0

GTTAGGGACCTTAAGGATCAATCTTGTCTGAGTTCCATTAGCTTGTCCTAATCTTTGTGGGCTTCAGCAAGTCCTTGAGGTCCCTAACCTGAGTTCTTGTC @@CFFFFFHHHHHJJJJIJJJJIIIIGIIJJJFHIJIJIIJJJJE?DGGCGHIJIJIGIIIIDGFHIIIIGHIJJF@CEH@CFF@CCEEA=CC;@ACA@C5 RG:Z:ind1

PHOSPHORE:181:C0KD3ACXX:8:1206:13256:144743 99 13 10355951 60 101M = 10355989 139

TGGGAAGGCTTACTGTCTTCATGCAGGATCTGTGTGGCTCCTTACTTTCAACAGCCTCCATTACCAATTCCAGGGAAAGTCTCCATCAACCAGGAATGCAT

@@CFDFF?DHFHHIIHGHIJJG@HG<FHIIIIJJGGGDGIIJIIJJIGGEBD*?DDGHGGGIGHIH>GG;C>AAAC@DFD;@CECAACDCBBBB9A>>@CA X0:i:1 X1:i:0 MD:Z:101 RG:Z:ind2 XG:i:0 AM:i:37 NM:i:0 SM:i:37 XM:i:0 XO:i:0 XT:A:U

PHOSPHORE:181:C0KD3ACXX:8:1206:13256:144743 147 13 10355989 60 101M = 10355951 -139

TCCTTACTTTCAACAGCCTCCATTACCAATTCCAGGGAAAGTCTCCATCAACCAGGAATGCATCAGTATAAGGCACTCTGAAAGAAAGCAATCTAAATCCC

:>DCDDDECAA>>@BFFEC@EIHE;GBHF=GFGHGGGGIIHFHGDG@GDB9IIJIIGHHGGGHIIGDIIHFHHEFGEIIJHGH?GBGIHHGGDFFDFFCC@ X0:i:1 X1:i:0 MD:Z:101 RG:Z:ind2 XG:i:0 AM:i:37 NM:i:0 SM:i:37 XM:i:0 XO:i:0 XT:A:U

PHOSPHORE:181:C0KD3ACXX:8:1202:6947:20338 99 13 10358279 29 99M = 10358378 154

GCAGGCTTTTAAGAATATGTTCTGTTTTCAAATAGTAACCCAAAAAGGGGTGGGGGCGGGGGCAAAGTGCTGTGTGTGTGTGTGTGTGTGTGTGTGTGT

CC@FFFFFGHGFHFGGGII>JHGGEHIJIIEHHEGHIGHIJJGGIJFGIJ@FHIIHFBDBDDBB@BC44@:@4?><8A2<2?8?<B<<2<2<<A<ABB? X0:i:1 X1:i:0 MD:Z:99 RG:Z:ind2 XG:i:0 AM:i:29 NM:i:0 SM:i:29 XM:i:0 XO:i:0 XT:A:U

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S. Marthey / O. Rué

Duplicate

Marking/Removing

• Duplicats PCR (construction des librairies)

• Samtools rmdup || Picard MarkDuplicates

Identification

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Local Realignment

• Identification des régions à

réaligner :

• Réalignement des reads

“ The algorithm begins by first identifying regions for

realignment where 1) at least one read contains an

indel, 2) there exists a cluster of mismatching bases

or 3) an already known indel segregates at the site …”

DePristo et al (2011)

“ Next, all reads are realigned against just the best

haplotype Hi and the reference (H0), and each read Rj

is assigned to Hi or H0 …”

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Base quality recalibration

Mean BQ = 32,8 - Median = 36,7

R

aw

data

« The per-base quality scores, which convey the

probability that the called base in the read is

the true sequenced base, are quite inaccurate and

co-vary with features like sequencing technology,

machine cycle and sequence context »

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Conséquences

Base quality recalibration

Mean BQ = 32,8 - Median = 36,7 Mean BQ = 28,8 – Median = 28,7

R aw data R ec al ibrat ed data • Baisse de la variabilité

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S. Marthey / O. Rué

Analysis-ready reads

• Nouveau fichier BAM

• Peut être utilisé ensuite avec

d’autre outils pour la suite des

analyses (Samtools mpileup,

Popoolation, etc…)

R

aw

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Single vs Multiple sample analysis

Data processing and analysis of genetic variation using nextgeneration sequencing Mark DePristo Dec. 8th, 2011 (http://www.broadinstitute.org/gatk/best-practices.htm)

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Unified Genotyper

• Outil GATK

• Multiple sample analysis

• Différents modes de détection

– SNP

– Indels

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Format VCF

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Comparaison d’outils de SNP calling

• SIGENAE Team – LGC - INRA

• APACHE Project (Alain Vignal)

• To find SNPs (Single Nucleotide Polymorphism) which differentiate

populations

• Barbary Duck : no reference genome (Beijing duck genome is

available)

Beijing duck Barbarie duck

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raw data realigned data recalibrated data Realigned & recalibrated data 0 10000 20000 30000 40000 50000 60000 70000 80000 Mpileup Mpileup -B Mpileup -E GATK Popoolation2 Δ = 777% Δ = 714% Δ = 42% Δ = 45%

Impact of realignment / recalibration on

SNP count

• More homogenous SNP count

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Reliable results with other species ?

raw data realigned datarecalibrated dataRealigned & recalibrated data 0 10000 20000 30000 40000 50000 60000 70000 80000 0 10000 20000 30000 40000 50000 60000 70000 80000 DUCK Mpileup Mpileup -B Mpileup -E GATK BAMs bruts BAMs réalignés BAMs recalibrés BAMs réalignés/recalibrés 0 50000 100000 150000 200000 0 50000 100000 150000 200000 PIG Mpileup Mpileup -B Mpileup -E GATK

BAMs brutsBAMs réalignésBAMs recalibrésBAMs réalignés/recalibrés 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 CHICKEN Mpileup Mpileup -B Mpileup -E GATK

Raw data Realigned/Recal data

Δ tools 234% 4%

Δ tools 777% 20%

Δ tools 454% 9%

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Conclusion

• Variability between called SNP by different tools

• GATK realignment/recalibration greatly helps to reduce this variability

• High impact of base quality score

• Reliable on various DNA data, but not on RNA data

« … We recommend a recalibration of per-base quality

scores as in GATK or SOAPsnp … »

« … Several additional steps can be taken to improve

genotype calls, such as local realignments ... »

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Bilan

GATK nécessite un peu d'habitude

Points forts :

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Assez rapide d'exécution grâce à la parallélisation possible

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Comptage allélique

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Prise en compte des positions multi-alléliques

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Beaucoup de fonctionnalités et d'options

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SNPs semblent être fiables

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Améliorations fréquentes

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Site Internet

Points faibles :



Recalibration basée sur des SNPs connus...

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À l'origine créé pour l'analyse de génomes humains

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Beaucoup d'étapes avant de lancer l'UnifiedGenotyper

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Le site de référence GATK

• Download logiciels + ressources (vcf)

• Guide Analyse

• Best Practices

• Forum

• Documentation Technique

• Etc…

http://www.broadinstitute.org/gatk/index.php

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References

• Samtools :

Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R. and 1000 Genome Project Data Processing Subgroup - The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 25, 2078-9 (2009).

Li H, Ruan J, Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Research 18:1851-8 (2008).

• GATK

A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics 43, 491 (2011).

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. McKenna AH, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, Depristo M. Genome Res. (2010).

• Popoolation2

R. Kofler, R. V. Pandey, C. Schlotterer. PoPoolation2: identifying differentiation between populations using sequencing of pooled DNA samples (Pool-Seq). Bioinformatics (2011).

• Pyrobayes: an improved base caller for SNP discovery in pyrosequences. Quinlan AR, Stewart DA, Strömberg MP, Marth GT. Nat Methods (2008) • BayesCall: a model-based base-calling algorithm for high-throughput short-read sequencing. Kao W-C, Stevens K, Song YS. Genome Res (2009). • Ibis

Improved base calling for the Illumina Genome Analyzer using machine learning strategies. Kircher M, Stenzel U, Kelso J. . Genome Biol. (2009). • Pyrocleaner