An integrative probabilistic model for identification of structural variation in sequencing data

Abstract

Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments. This results in reduced sensitivity to detect SVs, especially in repetitive regions. We introduce GASVPro, an algorithm combining both paired read and read depth signals into a probabilistic model that can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50 to 90% improvement in specificity on deletions and a 50% improvement on inversions. GASVPro is available at http://compbio.cs.brown.edu/software.

Unraveling overlapping deletions by agglomerative clustering

“…We set the minimum cluster cardinality to 2 and used both, the option to output all maximal sets of overlapping trapezoids (see "Method") which we will refer to as GASV_max, and the default to output merged clusters, referred to as GASV. Although a follow-up version GASVPro [33] has been published recently, the available software does not yet include all necessary preprocessing tools to process BAM files. Secondly, we also ran CLEVER [37] as one of the most recent and accurate variation detection tools.…”

“…Also tools exist which combine techniques already during the detection, e.g. inGAPsv [31], CNVer [32], GASVPro [33], SVseq2 [34], or the method by Nord et al [35]. The method presented here is based on paired-end mapping.…”

“…[5,6,22,33,37,38], assign all given mappings to clusters, where each cluster corresponds to one deletion that is supported by all mappings in the cluster. Figure 2 shows an example for such a deletion cluster.…”

Discovering and Genotyping Twilight Zone Deletions

“…[2,6,8,21,24,25]). They can be distinguished by their definition of discordant read, their clustering/grouping techniques, and their details in deriving predictions from groups of discordant reads.…”

“…A predominant example is the discovery of deletions of more than 200 base pairs (bp), which has been addressed by a large variety of approaches: examples are Breakdancer [2], VariationHunter [6], (MATE-)CLEVER [16,17], DELLY [22], GASV(-Pro) [24,25], see also the references therein, and again the above-mentioned reviews [1,20].…”