Distinguishing the driver mutations from somatic mutations in a tumor genome is one of the major challenges of cancer research. This challenge is more acute and far from solved for non-coding mutations.
A method that estimates the accumulated functional impact bias of somatic mutations in any genomic region of interest based on a local simulation of the mutational process affecting it. OncodriveFML may be applied to all genomic elements to detect likely drivers amongst them. OncodriveFML can discover signals of positive selection when only a small fraction of the genome, like a panel of genes, has been sequenced.
Permits users to implement several genomic investigations in silico. DIGS can be used to interrogate and manipulate the data produced in a study. It eases the inclusion of automated screens that can be performed on a large scale. This tool performs sequence similarity searches using the BLAST program. It was applied to vertebrate genomes for nonretroviral endogenous viral elements (EVEs).
Enables tightly integrated comparative variant analysis and visualization of thousands of next generation sequencing (NGS) data samples and millions of variants. BasePlayer is a highly efficient and user-friendly software for biological discovery in large-scale NGS data. It transforms an ordinary desktop computer into a large-scale genomic research platform, enabling also a non-technical user to perform complex comparative variant analyses, population frequency filtering and genome level annotations under intuitive, scalable and highly-responsive user interface to facilitate everyday genetic research as well as the search of novel discoveries.
A computational method to identify regulatory elements with cancer-associated signatures of accelerated somatic evolution (SASE). SASE-hunter searches for genomic regions with a significantly higher abundance of somatic mutations in a genomic element (e.g. gene promoters) than that expected by chance and prioritizes those loci that carry the signature in multiple cancer patients. We identified a novel signature of accelerated somatic evolution marked by a significant excess of somatic mutations localized in a genomic locus, and prioritized those loci that carried the signature in multiple cancer patients.
Recognizes highly mutated noncoding regulatory elements. LARVA employs whole genome sequencing (WGS) variant data from multiple genetic disease patients. It consists of a computational framework designed to simplify the study of noncoding variants. This tool includes a comprehensive set of noncoding functional elements, modeling their mutation count with a beta-binomial distribution to handle overdispersion.