Detection and analysis of SNP polymorphisms · Detection and analysis of SNP polymorphisms Alexis DEREEPER 21 November 2013 Training course in Bioinformatics, Montpellier, 18-22 November

Detection and analysis of SNP polymorphisms

Alexis DEREEPER

21 November 2013 Training course in Bioinformatics, Montpellier, 18-22 November 2013

What is a SNP?


• A SNP is defined as a single base change in a DNA sequence that occurs in a significant proportion of a large population

• SNPs in coding regions may (or may not) alter the protein structure and function

Why studying SNP?


• Population genetics: population stratification, linkage disequilibrium…

• Define markers for genetic maps

• Analysis of genome structure, genome evolution

• Genome Wide Association studies. SNPs can be used for estimating predisposition to disease, for predicting specific genetic traits • Functional analysis: alteration of protein structure and function

Re-sequencing projects: a deluge of SNPs


Using NGS technologies: RESEQUENCING Mapping SNPs

SNPs from RNASeq: example of Arcad project


Available reference ? genome/transcriptome

454 sequencing

De novo reference assembly

Solexa sequencing

Mapping on reference

Polymorphism database in adapted format • redundancy • open reading frame • CDS/UTR

Diversity study • Comparative domestication • Life history trait impact

• Functionnal evolution

Yes

No

Ortholog/paralogs assignation

Solexa sequencing

CROP Breeding SNP database

• functional annotation • selection footprint

Strategy : comparative population genomics with transcriptomics data

Strategy for SNP discovery from NGS


Global BAM with read group

FastQ Groomer

Mapping BWA

IndelRealigner

UnifiedGenotyper

VCF file

Fastq (ind1)

BAM with read group

FastQ Groomer

Mapping BWA

Fastq (ind2)

BAM with read group

FastQ Groomer

Mapping BWA

Fastq (ind3)

BAM with read group

FastQ Groomer

Mapping BWA

Fastq (ind4)

BAM with read group

….

mergeSam

Add or Replace Groups Add or Replace Groups Add or Replace Groups Add or Replace Groups

DepthOfCoverage

Depth file

GATK

PicardTools

FASTQ Format


• Standard format of short reads from NGS data

FASTQ file → TEXT file

STRUCTURE:

@HWUSI-EAS454_0006:1:112:14105:5498#CTTGTA

CGCCAAGAAGTGTAGCAAAACGGCAGAGCTCGTGGATTAAACAAACAGAGGATTTCGGTGAGGATTGAGGGGGAGT

+

cfffcfeffdeefefffcffffffffcffeffffdffffafcfffffdffffdfefeddfêececfffdfcbffb

@HWUSI-EAS454_0006:1:37:16314:3410#CTTGTA

AGTGTAGCAAAACGGCAGAGCTCGTGGATTAAACAAACAGAGGATTTCGGTGAGGATTGAGGGGGAGTGGTGGCCG

+

`bTbbccccceeeeeceeeecccYeedded`ceec]ddddeâ`deeeec\`dddcbaadadYd`]]Jc_^bc^^\

FASTQ Format


f → Quality = 38 (102 – 64)

@HWUSI-EAS454_0006:1:112:14105:5498#CTTGTA

CGCCAAGAAGTGTAGCAAAACGGCAGAGCTCGTGGATTAAACAAACAGAGGATTTCGGTGAGGATTGAGGGGGAGT

+

cfffcfeffdeefefffcffffffffcffeffffdffffafcfffffdffffdfefeddfêececfffdfcbffb

SAM Format


• Standard format for mapping of NGS data

• Sequence Alignment Mapping (SAM) Binary Alignment Mapping (BAM)

Visualization of SNPs in mapping alignment


Tablet

• Graphical viewer for assembly of NGS data

• Accepts different formats: ACE, SAM, BAM

SNP discovery from NGS data


GATK (Genome Analysis Tool Kit)

• Package for analysis of NGS data.

• Developed for the analysis of Human medical resequencing projects (1000 Genomes, The Cancer Genome Atlas)

• Includes tools for depth analysis, quality score recalibration, SNP/InDel discovery

• Complementary of 2 other packages: SamTools, PicardTools

PREPROCESS: * Index human genome (Picard), we used HG18 from UCSC. * Convert Illumina reads to Fastq format * Convert Illumina 1.6 read quality scores to standard Sanger scores FOR EACH SAMPLE: 1. Align samples to genome (BWA), generates SAI files. 2. Convert SAI to SAM (BWA) 3. Convert SAM to BAM binary format (SAM Tools) 4. Sort BAM (SAM Tools) 5. Index BAM (SAM Tools) 6. Identify target regions for realignment (Genome Analysis Toolkit) 7. Realign BAM to get better Indel calling (Genome Analysis Toolkit) 8. Reindex the realigned BAM (SAM Tools) 9. Call Indels (Genome Analysis Toolkit) 10. Call SNPs (Genome Analysis Toolkit)

11. View aligned reads in BAM/BAI (Integrated Genome Viewer)

VCF Format (Variant Call Format)


##fileformat=VCFv4.0

##fileDate=20090805

##source=myImputationProgramV3.1

##reference=1000GenomesPilot-NCBI36

##phasing=partial

##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data">

##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">

##INFO=<ID=AF,Number=.,Type=Float,Description="Allele Frequency">

##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele">

##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership, build 129">

##INFO=<ID=H2,Number=0,Type=Flag,Description="HapMap2 membership">

##FILTER=<ID=q10,Description="Quality below 10">

##FILTER=<ID=s50,Description="Less than 50% of samples have data">

##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">

##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">

##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth">

##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality">

#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002

20 14370 rs6054257 G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0|0:48:1:51,51 1|0:48:8:51,51

20 17330 . T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0|0:49:3:58,50 0|1:3:5:65,3

Advantages: describes the variations for each position + genotype assignation

GATK (other functionalities)


• DepthOfCoverage module: Enables to inform sequencing depth of coverage for each gene, each position and each individual

• ReadBackedPhasing module: Enables to define if possible allele association (phase or haplotype) in case of heterozygosity…

And not AGG GGA

GATK (other functionalities)


FastQ Groomer

Mapping BWA

IndelRealigner

UnifiedGenotyper

VCF file

Fastq (ind1)

BAM with read group

FastQ Groomer

Mapping BWA

Fastq (ind2)

BAM with read group

FastQ Groomer

Mapping BWA

Fastq (ind3)

BAM with read group

FastQ Groomer

Mapping BWA

Fastq (ind4)

BAM with read group

….

mergeSam


DepthOfCoverage

Depth file ReadBackedPhasing

VariantFiltration

Phased VCF

Filtered VCF

The SNiPlay project


The SNiPlay project


SNiPlay: Web-based application for polymorphism analysis

http://sniplay.cirad.fr



FastQ Groomer

Mapping BWA

IndelRealigner

UnifiedGenotyper

VCF file

Fastq (RC1)

BAM with read group

FastQ Groomer

Mapping BWA

Fastq (RC2)

BAM with read group

FastQ Groomer

Mapping BWA

Fastq (RC3)

BAM with read group

FastQ Groomer

Mapping BWA

Fastq (RC4)

BAM with read group

….

mergeSam


DepthOfCoverage

Depth file

SNiPlay : parameters and options

21 November 2013

Select the VCF format

Load the VCF file, the reference and the depth file

Indicate groups of individuals to make SNP comparison

SNiPlay : parameters and options

21 November 2013

Filter SNPs respecting minimum Depth coverage

Select the banana genome

Check the steps to be performed


SNiPlay: SNP statistics


SNiPlay and Illumina genotyping chip

Cartesian coordinates

Genotyping file

Submission file for Illumina

Analysis with the BeadStudio software

Design of Illumina chip


SNiPlay : Allelic files

@DARwin 5.0 - ALLELIC - 2

33 20

NÂ° 50 50 122 122 218 218 245 245 261 261 290 290 356

1 1 1 1 1 3 3 3 3 4 4 2 2 2

2 1 1 1 1 3 3 1 3 4 4 2 2 2

3 1 1 1 1 3 3 3 3 4 4 2 2 2

4 1 1 1 1 3 3 3 3 4 4 2 2 2

33

10

P 49 121 217 244 260 289

SSSSSSSSSS

#cARB

A A G G T C C A T T

A A G G T C C A T T

#cSYR

A A G A T C C A T C

A A G G T C C A T T

• PED format

• DARwin format

• .inp format for Phase • Format for TASSEL (association studies)

cARB 1 0 0 1 0 1 1 1 1 3 3 3 3 4 4 2 2 2 2 1 1 4 4 4 4

cSYR 2 0 0 1 0 1 1 1 1 3 3 1 3 4 4 2 2 2 2 1 1 4 4 2 4

cARA 3 0 0 1 0 1 1 1 1 3 3 3 3 4 4 2 2 2 2 1 1 4 4 4 4

33 10:2

50 122 218 245 261 290 356 461 467 560

cARB A:A A:A G:G G:G T:T C:C C:C A:A T:T T:T

cSYR A:A A:A G:G A:G T:T C:C C:C A:A T:T C:T

cARA A:A A:A G:G G:G T:T C:C C:C A:A T:T T:T

cORL A:A A:A G:G G:G T:T C:C C:C A:A T:T T:T

cLAR A:G A:G A:G A:G C:T C:C C:C A:A T:T C:T

Provides various formats of allelic files:


SNiPlay : Annotation of SNPs

1) Locate SNP on a genome

• using Blast • or using GFF if reference correspond to gene/CDS

2) Annotate SNPs with SnpEff program


SNiPlay : Annotation of SNPs


SNiPlay : Diversity analysis

SeqLib library


SNiPlay : Haplotype network

High frequency haplotypes

Low frequency haplotype

Group distribution whithin this haplotype

Distance between 2 haplotypes (nb of mutations)

Haplophyle


SNiPlay : Comparison of SNPs between groups

Individu, group

Ind1, Table

Ind2, Table

Ind3, Table

Ind4, East

Ind5, East

Ind6, East

Ind7, East

Ind8, West

External file (optional)

SNiPlay: Population structure analysis


Admixture

• Test different values of K (estimates of probability that samples are structured in K populations)

• For the best value of K, the application shows Q estimates for each individual (probability that the individual belongs to each population)

SNiPlay : GWAS analysis


Tassel, MLMM…

• Genome Wide Association Studies (GWAS)

• Estimate the association between SNPs and a phenotypic trait

• Display Manhattan plots: GWAS statistical tests (-log10 pvalue) along the chromosomes

SNP analysis in an allopolyploid


SNiPloid

• Compare SNPs observed in allotetraploid to those observed between parental genomes

• Categorize SNP in different evolution scenarii

• Attempt to assign alleles to subgenomes • Estimates the subgenomic contribution to the transcriptome


SNPs in the Banana Genome Hub

Objectives of the exercises


• To know and manipulate available packages/tools for SNP and INDEL detection from NGS data (assembly of NGS data)

• To think about difficulties encountered when analysing new generation sequencing data (differentiate sequencing errors, paralogs and allelic variation)

• Detect SNP and assign genotypes to every polymorphic positions

• Simply exploit polymorphisms data via a Web-based application (genetic diversity, LD)

• Obtain an exploitable dataset to send for the design of a high-throughput SNP chip (Illumina VeraCode technology)

Short reads Solexa

Mapping SAM

Exploitation of polymorphism data

Design of a Illumina SNP chip

Assignation of genotypes

Ind1 ATTGTGTCGTAACGTATGTCATGTCGT Ind2 ATTGTGTCGGAACGTATGTCATGTCGT Ind3 ATTGTGTCGKAACGTATGTCATGTCGT

Allelic variations

List of SNPs

867 A/G 1998 T/C 2341 T/G

Documents

Detection and analysis of SNP polymorphisms · Detection and analysis of SNP polymorphisms Alexis DEREEPER 21 November 2013 Training course in Bioinformatics, Montpellier, 18-22 November