Enhanced structural variant and breakpoint detection using SVMerge by integration of multiple detection methods and local assembly
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This document describes SVMerge, a meta structural variant calling pipeline that integrates multiple SV detection methods and uses local assembly to validate calls. It summarizes SVMerge's workflow of running various SV callers individually on BAM files, merging and validating calls using local de novo assembly. The document also provides examples of how SVMerge was applied to a HapMap trio dataset, demonstrating it discovered more SVs than individual callers and had a low false positive rate compared to a curated database. Future work areas are identified like cataloging complex SV events and improving heterozygous call support.
Enhanced structural variant and breakpoint detection using SVMerge by integration of multiple detection methods and local assembly
- 1. Enhanced structural variant and breakpoint detection using SVMerge by integration of multiple detection methods and local assembly Kim Wong/Thomas Keane Vertebrate Resequencing Informatics http://svmerge.sourceforge.net Vertebrate Resequencing Informatics 22nd March, 2011
- 2. Genomic Structural Variation Large DNA rearrangements (>100bp) Frequent causes of disease Referred to as genomic disorders Mendelian diseases or complex traits such as behaviors E.g. increase in gene dosage due to increase in copy number Prevalent in cancer genomes Many types of genomic structural variation (SV) Insertions, deletions, copy number changes, inversions, translocations & complex events Comparative genomic hybridization (CGH) traditionally used to for copy number discovery CNVs of 1–50 kb in size have been under-ascertained Next-gen sequencing revolutionised field of SV discovery Parallel sequencing of ends of large numbers of DNA fragments Examine alignment distance of reads to discover presence of genomic rearrangments Resolution down to ~100bp Vertebrate Resequencing Informatics 22nd March, 2011
- 3. Simple types of Structural Variation Vertebrate Resequencing Informatics 22nd March, 2011
- 4. Deletion SV Visualisation LookSeq viewer Read pairs displayed Y axis is aligned insert size Deletions are easily spotted Read pairs are mapped further apart than expected Coverage is zero across the deletion sequence Deletion in NOD/ShiLtJ Vertebrate Resequencing Informatics 22nd March, 2011
- 5. Inversion Mate pairs align in the same orientation Coverage zero at breakpoints Vertebrate Resequencing Informatics 22nd March, 2011
- 6. Insertion One end mapped reads Coverage zero at breakpoint Vertebrate Resequencing Informatics 22nd March, 2011
- 7. Complex SV Events Inversion Insertion Insertion Vertebrate Resequencing Informatics 22nd March, 2011
- 8. Human Examples Stankiewicz and Lupski (2010) Ann. Rev. Med. Vertebrate Resequencing Informatics 22nd March, 2011
- 9. Example 2: Transposable element insertion in mice Vertebrate Resequencing Informatics 22nd March, 2011
- 10. SVMerge Initially developed for mouse genomes project Several software packages currently available to discover SVs Various approaches using information from anomalously mapped read pairs OR read depth analysis No single SV caller is able to detect the full range of structural variants Paired-end mapping information, for example, cannot detect SVs where the read pairs do not flank the SV breakpoints Insertion calls made using the split-mapping approach are also size-limited because the whole insertion breakpoint must be contained within a read Read-depth approaches can identify copy number changes without the need for read-pair support, but cannot find copy number neutral events SVMerge, a meta SV calling pipeline, which makes SV predictions with a collection of SV callers Input is a BAM file per sample Run callers individually + outputs sanitized into standard BED format SV calls merged, and computationally validated using local de novo assembly Primarily a SV discovery/calling + validation tool Vertebrate Resequencing Informatics 22nd March, 2011
- 11. SVMerge Workflow Wong et al (2010) Vertebrate Resequencing Informatics 22nd March, 2011
- 12. SV Callers Wong et al (2010) Vertebrate Resequencing Informatics 22nd March, 2011
- 13. Local Assembly Validation Key to the approach is the computational validation step Local assembly and breakpoint refinement All SV calls (except those lacking read pair support e.g. CNG/CNL) Algorithm Gather mapped reads, and any unmapped mate-pairs (<1kb of a insertion breakpoint, <2kb of all other SV types) Run local velvet assembly Realign the contigs produced with exonerate Detect contig breaks proximal to the breakpoint(s) Vertebrate Resequencing Informatics 22nd March, 2011
- 14. Breakpoint Improvement (simulated) Vertebrate Resequencing Informatics 22nd March, 2011
- 15. Breakpoint Improvement (Real data) Yalchin and Wong et al, in prep Vertebrate Resequencing Informatics 22nd March, 2011
- 16. Application to HapMap trio dataset High-depth HapMap trio (NA18506, NA18507, NA18508) 42x, 42x and 40x Reads processed through Vert. Reseq. Pipeline Aligned to the GRCh37 human reference using BWA Single BAM file for each individual BreakDancerMax, Pindel, RDXplorer, SECluster, and RetroSeq Exclude calls 600 bp from a reference sequence gap 1 Mb from a centromere or telomere Computational validation of raw candidate calls Vertebrate Resequencing Informatics 22nd March, 2011
- 17. NA18506 Results Vertebrate Resequencing Informatics 22nd March, 2011
- 18. Does multiple callers discover more SVs? Vertebrate Resequencing Informatics 22nd March, 2011
- 19. How do the calls measure up? Compared the overlap of the deletion, gain, and inversion calls against the curated Database of Genomic Variants Overlapped with calls in DGV at a rate significantly higher than expected by random chance Deletions in DGV: 71% (NA18506), 81% (NA18507), and 71% (NA18508) Copy number gains in DGV: 29% (NA18506), 32% (NA18507), and 36% (NA18508) Inversions in DGV: 47% (NA18506), 69% (NA18507), and 51% (NA18508) Child calls not in DGV also called in the parents Further 18% deletions, 32% inversions, 54% duplications Estimated max. false positive rate of 11%, 21%, and 17% All child-only SV calls comprise 11% of the child's final SV call Considerable improvement from 'merged raw’ (50% unique) Vertebrate Resequencing Informatics 22nd March, 2011
- 20. Complex SV Types Yalchin and Wong et al, in prep Vertebrate Resequencing Informatics 22nd March, 2011
- 21. Future Work SVMerge primarily a discovery and validation tool Extensible pipeline so that calls from any method to be easily incorporated Developed primarily for mouse genomes project Successfully applied to human trio dataset Computationally validation approach reduces false positives Complex SVs Cataloging repeating combinations of multiple SV events in small loci 2011 development Low coverage cross-population SV discovery Genotyping existing SVs in new samples Better support for heterozygous calls Integration of SVMerge into Vert. Reseq. pipeline for UK10K Vertebrate Resequencing Informatics 22nd March, 2011