Variant Discovery. Jie (Jessie) Li PhD Bioinformatics Analyst Bioinformatics Core, UCD

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1 Variant Discovery Jie (Jessie) Li PhD Bioinformatics Analyst Bioinformatics Core, UCD

2 Variant Type Alkan et al, Nature Reviews Genetics 2011 doi: /nrg2958

3 Variant Type

4 Venn diagrams showing the number of identified variants for tested germline (A), somatic (B), CNV (C) and exome CNV (D) tools. Pabinger S et al. Brief Bioinform 2013;bib.bbs086 The Author Published by Oxford University Press.

5 Variant discovery large scale variants breakdancer Predicts five types of structural variants: insertions, deletions, inversions, inter- and intra-chromosomal translocations. delly Predicts deletions, tandem duplications, inversions and translocations.

6 Variant discovery SNPs/InDels SAMtools FreeBayes SNPs and short INDELs A Bayesian genetic variant detector designed to find SNPs, indels, MNPs (multi-nucleotide polymorphisms), and complex events (composite insertion and substitution events) smaller than the length of a short-read sequencing alignment. GATK A suite of tools including local realigner, quality score recalibrator, and SNP/INDEL caller.

7 Variant calling with SAMtools Statistics is carried out to calculate the likelihood score for each mis-match from the reference genome present in mapped reads. Number of reads that share a mis-match The sequence quality Expected sequencing error rates Limitation Limited by the mapping quality of the reads Assuming sample is diploid Example Samtools mpileup -ud -r 2L:100, ,000 -f reference.fasta input1.bam input2.bam Bcftools view -bvcg - > raw.bcf Bcftools view raw.bcf vcfutils.pl varfilter -D100 > variants.vcf Heng Li, Bioinformatics, Vol. 27, no. 21, 2011, p2987

8 Variant calling with Freebayes FreeBayes Call variants based upon haplotypes up to the length of sequencing reads Nearby variants are identified on the same haplotype, so they are in phase Able to handle polyploidy Limitation All types of variants are called simultaneously Limited by the mapping quality of reads Example Freebayes -f reference.fasta region 20: input1.bam input2.bam > raw.vcf Erik Garrison, Gabor Marth. arxiv: v2 [q-bio.gn]

9 GATK variant calling workflow

10 Mark Duplicates Why duplicates are bad

11 Indel Realignment Several Consecutive SNPs only found on reads ending on the right of the homopolymer Several consecutive SNPs only found on reads ending on the left of the homopolymer

12 Indel Realignment

13 Indel Realignment Information used to carry out indel realignment Known sites (dbsnp, 1000 Genomes) Indels present in original alignments (in CIGARs) Sites where evidence suggests a hidden indel

14 Indel Realignment + Known sites Input BAM file not necessary if processing only at known indels Known indels speed up processing and improve accuracy, but is not required At indels seen in the original BAM is the recommended mode Only at known indels speed up processing, accurate for ~90-95% of indels Using full Smith-Waterman realignment is the most accurate, but has heavy computational cost and not really necessary with the new techs

15 Indel Realignment DePristo M., Banks E., Poplin R. et. al., A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Gen.

16 Base Quality Score Recalibration Quality scores are critical for all downstream analysis The quality scores issued by sequencers are inaccurate and biased Recalibration information is obtained by analyzing covariation among several features of a base, e.g.: Reported quality score Position within the read (machine cycle) Preceding and current nucleotide (sequencing chemistry effect) Known variants are used to discount most of the real genetic variation present in the sample All other differences from the reference are assumed to be sequencing errors Indel Realignments first reduces noise from misalignments

17 Base Quality Score Recalibration

18 Read Compression Keeps only essential information for variant calling Variable vs consensus regions

19 Initial Variant Calling Distinguish genetic variant and random machine noise Large scale Bayesian modeling problem Assembly of large genomes using second-generation sequencing. Schatz. Genome Research. 2010

20 Variant Quality Score Recalibration or Hard Filtering Initial variant calling has very large set that is full of false positives Hand-tuned filtering requires time and expertise Statistical model could be used to recalibrate variants Each variant has a set of statistics associated with them that are called variant annotations Real variants tend to cluster together via these statistics The cluster tend to be Gaussianly distributed, which makes possible to use a Gaussian mixture model to fit the data and new potential variants can be evaluated against this model

21 Variant Quality Score Recalibration Training resources: SNP (HapMap, Omni, 1000G, dbsnp) INDEL (Mills)

22 Variant Quality Score Recalibration

23 Variant Filtering Small data sets in terms of both number of samples or size of targeted regions No available high confidence known variants database For SNPs: QD < 2.0 MQ < 40.0 FS > 60.0 MQRankSum < ReadPosRankSum < -8.0 For indels: QD < 2.0 ReadPosRankSum < InbreedingCoeff < -0.8 FS > See more at:

24 GATK variant calling workflow

25 Thank you!