Schönhuth, A. | Search bioRxiv

bioRxiv2019

Enhancing sensitivity and controlling false discovery rate in somatic indel discovery

As witnessed by various population-scale cancer genome sequencing projects, accurate discovery of somatic variants has become of central importance in modern cancer research. However, count statistics on somatic insertions and deletions (indels) discovered so far point out that large amounts of discoveries must have been missed. The reason is that the combination of uncertainties relating to, for example, gap and alignment ambiguities, twilight zone indels, cancer heterogeneity, sample purity, sampling and strand bias are hard to accurately quantify. Here, a unifying statistical model is provided whose dependency structures enable to accurately quantify all inherent uncertainties in short time. As major consequence, false discovery rate (FDR) in somatic indel discovery can now be controlled at utmost accuracy. As demonstrated on simulated and real data, this enables to dramatically increase the amount of true discoveries while safely suppressing the FDR. Specifically supported by workflow design, our approach can be integrated as a post-processing step in large-scale projects.\n\nThe software is publicly available at https://varlociraptor.github.io and can be easily installed via Bioconda1 [Gruning et al., 2018].

bioinformatics↗

bioRxiv2019

MALVA: genotyping by Mapping-free ALlele detection of known VAriants

The amount of genetic variation discovered and characterized in human populations is huge, and is growing rapidly with the widespread availability of modern sequencing technologies. Such a great deal of variation data, that accounts for human diversity, leads to various challenging computational tasks, including variant calling and genotyping of newly sequenced individuals. The standard pipelines for addressing these problems include read mapping, which is a computationally expensive procedure. A few mapping-free tools were proposed in recent years to speed up the genotyping process. While such tools have highly efficient run-times, they focus on isolated, bi-allelic SNPs, providing limited support for multi-allelic SNPs, indels, and genomic regions with high variant density.\n\nTo address these issues, we introduce MALVA, a fast and lightweight mapping-free method to genotype an individual directly from a sample of reads. MALVA is the first mapping-free tool that is able to genotype multi-allelic SNPs and indels, even in high density genomic regions, and to effectively handle a huge number of variants such as those provided by the 1000 Genome Project. An experimental evaluation on whole-genome data shows that MALVA requires one order of magnitude less time to genotype a donor than alignment-based pipelines, providing similar accuracy. Remarkably, on indels, MALVA provides even better results than the most widely adopted variant discovery tools.

bioinformatics↗