| description |
Motivation: Detection of somatic mutations is one of the main goals of next generation sequencing. However, most of the routinely used mutation calling algorithms are not optimised for the analysis of genomic DNA isolated from frequently used non-human experimental model systems, where reliable databases of common variations among individuals are not available. We designed IsoMut which can be readily adapted to experimental scenarios where the goal is the identification of experimentally induced mutations in multiple isogenic samples.Results: We 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. |