SoftSearch: Integration of Multiple Sequence Features to Identify Breakpoints of Structural Variations

<div><p>Background</p><p>Structural variation (SV) represents a significant, yet poorly understood contribution to an individual’s genetic makeup. Advanced next-generation sequencing technologies are widely used to discover such variations, but there is no single detection tool that is considered a community standard. In an attempt to fulfil this need, we developed an algorithm, SoftSearch, for discovering structural variant breakpoints in Illumina paired-end next-generation sequencing data. SoftSearch combines multiple strategies for detecting SV including split-read, discordant read-pair, and unmated pairs. Co-localized split-reads and discordant read pairs are used to refine the breakpoints. </p> <p>Results</p><p>We developed and validated SoftSearch using real and synthetic datasets. SoftSearch’s key features are 1) not requiring secondary (or exhaustive primary) alignment, 2) portability into established sequencing workflows, and 3) is applicable to any DNA-sequencing experiment (e.g. whole genome, exome, custom capture, etc.). SoftSearch identifies breakpoints from a small number of soft-clipped bases from split reads and a few discordant read-pairs which on their own would not be sufficient to make an SV call. </p> <p>Conclusions</p><p>We show that SoftSearch can identify more true SVs by combining multiple sequence features. SoftSearch was able to call clinically relevant SVs in the BRCA2 gene not reported by other tools while offering significantly improved overall performance.</p> </div

Overlap of true positive calls for the NA12878 and NA18507 datasets.

<p>Overlap of true positive calls for the NA12878 and NA18507 datasets.</p

The general strategy for SoftSearch.

<p>A) Left clipped reads are defined as where the clipped portion of the read is at a smaller genome coordinate than the opposite end (opposite for right clipping). For a left clipped read located on the “+” strand, SoftSearch looks upstream for a discordant read pair where the read is oriented in the “-” direction. The orientation and location of the mate is where SoftSearch links the first region to. To increase the likelihood of exactly detecting the breakpoint, it then looks upstream for a right clipped read cluster. If none is found, then the default breakpoint location is the discordant read mate location; otherwise it is the position of soft clipping at the right clipped read. B) SoftSearch determines discordant read pairs by their insert size and orientation and places them in a temporary BAM file. It also reads the input BAM file for soft clipped reads and converts them to a BED file. Overlapping soft clip locations are counted to identify putative breakpoints, and then queried against the discordant read pair bam file for properly oriented supporting reads, which are then output in VCF format.</p

Example IGV screenshot of a 71bp tandem duplication in the BRCA2 gene identified by SoftSearch.

<p>Discordant reads are blue (plus strand) or red (minus strand). Soft clipped bases appear as multicolour “rainbows”.</p

Additional file 2: of Identification of missing variants by combining multiple analytic pipelines

Table S2. The composition of Tier 1, 2 and 3 variants in BWA-unique, Novo-unique and shared variants. (DOCX 13 kb

Additional file 4: of Identification of missing variants by combining multiple analytic pipelines

Table S4. The genomic location and GC content of multi-unique, single-unique and shared variants. (DOCX 14 kb

Additional file 1: of Identification of missing variants by combining multiple analytic pipelines

Table S1. The genomic location and GC content of BWA-unique, Novo-unique and shared variants. (DOCX 14 kb

Additional file 3: of Identification of missing variants by combining multiple analytic pipelines

Table S3. The percentage of known and novel variants in BWA-unique, Novo-unique and shared variants. (DOCX 13 kb

Additional file 7: of Identification of missing variants by combining multiple analytic pipelines

Table S7. The full list of variants identified in APP, PSEN1 and PSEN2 by each workflow. (XLSX 14 kb

Additional file 5: of Identification of missing variants by combining multiple analytic pipelines

Table S5. The composition of Tier 1, 2 and 3 variants in multi-unique, single-unique and shared variants. (DOCX 14 kb