Variant Detection in Next Generation Sequencing Data. John Osborne Sept 14, 2012
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1 + Variant Detection in Next Generation Sequencing Data John Osborne Sept 14, 2012
2 + Overview My Bias Talk slanted towards analyzing whole genomes using Illumina paired end reads with open source tools Background Alignment Software Detecting Variation Nucleotide Structural Analyzing and Interpreting Variation Best practices change extremely rapidly
3 + Next Generation Sequencing Taken from Illumina website
4 + Short Read Alignment Making comparisons is very difficult! Test Parameters Read length size Introduced errors Paired versus single end reads Metrics Discovery? Accuracy? Area under curve? What is correct? Downstream analysis Comparisons are time consuming to do and are typically only done when somebody releases a new aligner
5 + From homepage of Heng Li:
6 + Short Read Aligner Conclusions The differences between aligners are not that large anymore BWA, Bowtie2 are all available on cheaha I currently recommend BWA, but I suspect it will be supplanted by something else Bowtie2 Novoalign SeqAlto or something newer For longer reads (>=200bp) I would recommend BWA-SW, Bowtie2 (long read version) or CUSHAW2 (new) Select your aligner based on your downstream workflow, for example use of BWA is recommended by GATK
7 + HugeSeq Workflow From Detecting and annotating genetic variations using the HugeSeq pipeline Lam et al., 2012
8 + Variation Detection Nucleotide Polymorphisms Structural Variants / Rearrangements VCF 4.1 Format
9 + SNP Detection Most advanced and reliable variant detection New version of GATK can detect MNPs as well Coverage and Toolkit matter Problem isn t finding SNPs, it is finding the right SNPs
10 + Coverage versus Sensitivity From Detecting and annotating genetic variations using the HugeSeq pipeline Lam et al., 2012
11 + GATK and SAMTools Variant Calling From Detecting and annotating genetic variations using the HugeSeq pipeline Lam et al., 2012
12 + False Positives -Data from human monozygotic twins -Artifacts from borderline low coverage, top twin has 17 high quality reads (7 A) and the bottom has 23 high quality reads (2 A)
13 + Structural Variation Methods Small indels within single reads (GATK) Discordant paired-end reads (Breakdancer, VariationHunter) Depth of coverage (CNVnator, SegSeq) Split reads (Pindel, ClipCrop) Very active area of research Combined approaches becoming more common
14 + PINDEL Sample Output
15 + Neither deletion was detected by Pindel..
16 + Pitfalls of Structural Variant Detection with NGS Tips Get as much coverage as possible Not possible to find breakpoints with 5 fold coverage Use multiple approaches Remove duplicates If it is important and you have time look In twin study, only 2 out of 12 SVs found by Pindel Personal Bias GATK (small indels), Breakdancer (rearrangements), Pindel (split reads) and CNVator (repeat size estimation)
17 + Interpreting Variation Getting some variants is easy, analyzing them is hard Commonly used tools in CCTS IGV, BedTools, VCFTools, SNPEff Pipelines are becoming more popular Annovar (Sift, Polyphen2) Online Resources
18 + Questions?
19 + Fast gapped-read alignment with Bowtie 2, Langmead and Saltzburg (2012)
20 + Fast and Accurate Read Alignment for Resequencing, Mu et al, (2012)
21 + Fast and Accurate Read Alignment for Resequencing, Mu et al, (2012)
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24 + Key Points Best practices change extremely rapidly We don t know what the single best workflow is today Core variant toolset used by UAB CCTS BWA for reference based alignment Picard (duplicate removal) GATK for SNP calling, realignment and recalibration Breakdancer, Pindel for Structural Variant Detection BedTools, VCFtools, IGV for interpretation
25 + Actual GATK Data chr G A GT:AD:DP:GQ:PL 1/1:1,23:24:48.11:555,48,0 chr G A GT:AD:DP:GQ:PL 0/1:31,12:43:84.36:84,0,458 chr G A GT:AD:DP:GQ:PL 0/1:9,15:27:10.30:379,0,10 3 genotypes (0/0, 0/1, 1/1) GQ:PL Genotype Quality AD:DP Average Depth : Depth Quality
26 + Workflow Overview Workflow from Consensus Rules in Variant Detection from Next-Generation Sequencing Data, Jia et al. (2012)
27 + Variation Detection Nucleotide Polymorphisms SNPs MNPs Structural Variants / Rearrangements Insertions/Deletions (small and large) Inversions Tandem Duplications Translocations
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