Somatic variant callers identify mutations found within cancer genome sequencing data through mapping sequencing reads to a universal reference genome and inferring likelihoods from statistical models. False positives, however, are common among various tools as mismatches with the universal reference can also occur due to germline variants. Previous applications of personalized reference construction are not amenable with cancer genome analysis. Here, we describe an individualized approach for somatic variant discovery through the step-by-step usage of Personalized Reference Editor for Somatic Mutation discovery in cancer genomics (PRESM), a personalized reference editor for somatic mutation discovery in cancer genomes.
Keywords: Bioinformatics; Cancer genomics; Next-generation sequencing; PRESM; Personalized reference; Somatic mutations; Somatic variants.