Varlociraptor: enhancing sensitivity and controlling false discovery rate in somatic indel discovery

Abstract

Accurate discovery of somatic variants is of central importance in cancer research. However, count statistics on discovered somatic insertions and deletions (indels) indicate that large amounts of discoveries are missed because of the quantification of uncertainties related to gap and alignment ambiguities, twilight zone indels, cancer heterogeneity, sample purity, sampling, and strand bias. We provide a unifying statistical model whose dependency structures enable accurate quantification of all inherent uncertainties in short time. Consequently, false discovery rate (FDR) in somatic indel discovery can now be controlled at utmost accuracy, increasing the amount of true discoveries while safely suppressing the FDR.

Somatic variant analysis of linked-reads sequencing data with Lancet

“…We previously demonstrated the superior accuracy of this approach, especially for InDels, compared to widely used somatic variant callers (Narzisi et al, 2018). Moreover, a recently published and independent testing (Köster et al, 2020), showed Lancet to outperform state-of-the-art methods particularly in the detection of twilight zone InDels (30-250 bp).…”

Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology

“…Multi-mapping, for example as done by BWA-MEM, causes unequal genome coverage altering the signal-to-noise ratio 52 . Hence, alignment uncertainty is problematic for accurate SV detection and should be addressed with a sound statistical model 30 , 31 , 52 . Current estimates suggest ~55 Mb of GRCh38 are “dark regions” inaccessible to IL sequencing due to alignment ambiguity (i.e., repeat-rich regions) or the sequencing chemistry (i.e., GC content) 53 .…”

“…NGS can simultaneously detect different variant types and discover new biomarkers, and is more cost-effective than a series of single-gene assays. Although turn-around times are often longer, sensitivity and precision are maintained 88 provided sufficient sequencing depth is achieved 26 , 31 . As a result, NGS makes pan-cancer biomarker testing feasible, leading to the approval of drugs based on molecular alterations shared by different cancer types like the use of TRK inhibitors for all solid tumors with a NTRK fusion 88 .…”

“…Specialized somatic SV detection tools such as Lancet and Varlociraptor account for challenges specific to the identification of TSSVs (Box 2 ) 31 , 40 . The first challenge in comparing tumor and normal SV callsets are differences in SV breakpoints and types, analogous to the issues with overlapping SV callsets of different algorithms 25 .…”

“…Varlociraptor is a post-processing tool which uses a Bayesian framework to differentiate between somatic and germline breakpoints by calculating false discovery rate (FDR) values from unfiltered callsets 31 . During FDR calculation it quantifies uncertainties due to ambiguous read alignments, how reads support SVs (typing uncertainty), gap-placement bias and strand bias 30 , 31 . This is done by simulating the variant into the reference, re-aligning reads and comparing the statistical support for the adjusted versus unadjusted reference.…”