GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly

Abstract

The identification of genomic rearrangements with high sensitivity and specificity using massively parallel sequencing remains a major challenge, particularly in precision medicine and cancer research. Here, we describe a new method for detecting rearrangements, GRIDSS (Genome Rearrangement IDentification Software Suite). GRIDSS is a multithreaded structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph-based assembler. By combining assembly, split read, and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line, and patient tumor data, recently winning SV subchallenge #5 of the ICGC-TCGA DREAM8.5 Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods while matching or exceeding their sensitivity. GRIDSS identifies nontemplate sequence insertions, microhomologies, and large imperfect homologies, estimates a quality score for each breakpoint, stratifies calls into high or low confidence, and supports multisample analysis.

Towards a better understanding of the low recall of insertion variants with short-read based variant callers

“…They were chosen according to their good performances in recent benchmarks [7] and to maximise the methodological diversity. GRIDSS [11], Manta [20] and SvABA [6] are based on a first mapping step to the reference genome, contrary to MindThe-Gap (MTG) [10] which uses solely an assembly data structure (the De Bruijn graph). Two types of recall were computed depending on the precision and information given for each call: insertion-site only recall only evaluated if an insertion was called at an expected genomic position regardless of the predicted size or inserted sequence.…”

“…Bam index and reference dictionary were obtained by picard tools v2.18.2 [32]. GRIDSS v2.8.0, Manta v1.6.0, MindTheGap v2.2.1 and SvABA v1.1.0 were all run using recommended, or otherwise default, parameters [11,20,10,6]. Only "PASS" insertions, that were larger than 50 bp, were kept for the recall and false positive calculation.…”

“…Because the inserted sequence is absent from the reference genome, or at least at the given locus of insertion, calling such variants and resolving the exact inserted sequence requires finely tuned approaches such as de novo or local assembly [10,11]. This increased difficulty is well exemplified by the dramatic under-representation of such SV type in usual reference databases or standard variant callsets.…”

sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data

“…For example, DELLY (Rausch et al, 2012) relies on split reads and discordant read pairs while LUMPY (Layer et al, 2014) additionally utilizes read depth information. Furthermore, callers such as Manta (Chen et al, 2016) and GRIDSS (Cameron et al, 2017) also incorporate short-read assembly. To obtain a more comprehensive and/or accurate callset, ensemble approaches have yielded promising results (English et al, 2015;Mohiyuddin et al, 2015;Becker et al, 2018;Fang et al, 2018).…”

Peer Review #1 of "sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data (v0.2)"

“…For example, DELLY (Rausch et al, 2012) relies on split reads and discordant read pairs while LUMPY (Layer et al, 2014) additionally utilizes read depth information. Furthermore, callers such as Manta (Chen et al, 2016) and GRIDSS (Cameron et al, 2017) also incorporate short-read assembly. To obtain a more comprehensive and/or accurate callset, ensemble approaches have yielded promising results (English et al, 2015;Mohiyuddin et al, 2015;Becker et al, 2018;Fang et al, 2018).…”