Fast and accurate mutation detection in whole genome sequences of multiple isogenic samples with IsoMut

Abstract

BackgroundDetection of somatic mutations is one of the main goals of next generation DNA sequencing. A wide range of experimental systems are available for the study of spontaneous or environmentally induced mutagenic processes. However, most of the routinely used mutation calling algorithms are not optimised for the simultaneous analysis of multiple samples, or for non-human experimental model systems with no reliable databases of common genetic variations. Most standard tools either require numerous in-house post filtering steps with scarce documentation or take an unpractically long time to run. To overcome these problems, we designed the streamlined IsoMut tool which can be readily adapted to experimental scenarios where the goal is the identification of experimentally induced mutations in multiple isogenic samples.MethodsUsing 30 isogenic samples, reliable cohorts of validated mutations were created for testing purposes. Optimal values of the filtering parameters of IsoMut were determined in a thorough and strict optimization procedure based on these test sets.ResultsWe show that IsoMut, when tuned correctly, decreases the false positive rate compared to conventional tools in a 30 sample experimental setup; and detects not only single nucleotide variations, but short insertions and deletions as well. IsoMut can also be run more than a hundred times faster than the most precise state of art tool, due its straightforward and easily understandable filtering algorithm.ConclusionsIsoMut has already been successfully applied in multiple recent studies to find unique, treatment induced mutations in sets of isogenic samples with very low false positive rates. These types of studies provide an important contribution to determining the mutagenic effect of environmental agents or genetic defects, and IsoMut turned out to be an invaluable tool in the analysis of such data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1492-4) contains supplementary material, which is available to authorized users.

Correlation of homologous recombination deficiency induced mutational signatures with sensitivity to PARP inhibitors and cytotoxic agents

“…1a, Additional file 1: Table S1). Base substitution mutations and small indels were identified by simultaneously analyzing all sequences together with IsoMut [37]. The mutations detected by IsoMut are unique to one sample, thereby providing an accurate catalog of genetic changes that took place during the experiment (Additional file 1: Tables S2 and S3).…”

“…The alignment of reads was done as described, using the Galgal4.73 reference genome (16). Independently arising SNVs and short indels were identified using IsoMut, run on all samples together with default settings [37]. The output was post-filtered such that no more than five SNVs and one indel were detected in the ancestral clones, as mutations detected as unique in these samples provide an internal control for false positives.…”

Single-Cell Omics Analyses Enabled by Microchip Technologies

“…Genetic mutations and variations can be inherited or arise during cell proliferation and differentiation, ultimately resulting in genotypic heterogeneity (26)(27)(28)(29). Through whole-genome sequencing, the mutations, structural variations, aneuploidies, and recombination in the genome can be characterized (30)(31)(32)(33)(34)(35)(36). It has also been used to study cell-lineage relationships as well as the diversity, evolution, and role of genetic mosaicism (37)(38)(39)(40).…”

Detecting Rare AID-Induced Mutations in B-Lineage Oncogenes from High-Throughput Sequencing Data Using the Detection of Minor Variants by Error Correction Method

“…However, specific identification of these somatic point mutations becomes a challenging issue because HTS methods are impaired by an error rate that is usually of the same order of magnitude as the sample's mutation rate. Typically, HTS analysis pipelines dedicated to mutation detection include two major consecutive steps: read mapping that aligns each reach to a reference sequence (12,13) and variant calling that reports nucleotide substitutions, insertions, or deletions based on tunable filtering criteria (14)(15)(16)(17). General guidelines recommend to systematically exclude base substitutions when found at a frequency below 1-5% because they could be considered as sequencing errors.…”

“…Among available tools dedicated to identify rare variants in an ultra-deep sequenced dataset, we chose IsoMut (17), MuTect (16), and VarScan2 (15) variant calling tools to challenge DeMinEr. We first used our Cd83 deep sequencing datasets from wt and Ung 2/2 mice to compare basic outputs: detection of putative substitutions (identified as single-nucleotide polymorphism) and base insertions or deletions.…”