Enhanced structural variant and breakpoint detection using SVMerge by integration of multiple detection methods and local assembly

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

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


Simple types of Structural Variation


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


Inversion

Mate pairs align in the same orientation

Coverage zero at breakpoints


Insertion

One end
mapped reads

Coverage zero at breakpoint


Complex SV Events

Inversion

Insertion Insertion

Human Examples

Stankiewicz and Lupski (2010) Ann. Rev. Med.


Example 2: Transposable element insertion in mice


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


SVMerge Workflow

Wong et al (2010)


SV Callers

Wong et al (2010)


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)


Breakpoint Improvement (simulated)


Breakpoint Improvement (Real data)

Yalchin and Wong et al, in prep


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


NA18506 Results


Does multiple callers discover more SVs?


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)


Complex SV Types

Yalchin and Wong et al, in prep


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

Enhanced structural variant and breakpoint detection using SVMerge by integration of multiple detection methods and local assembly

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