Given the unprecedented sensitivity of gene fusion detection, and the repeated identification of fusion transcripts in normal cells, it is increasingly important to separate driver fusions from passenger mutations. Although many fusion detection tools encode their own filters in order to cut down on false positive calls, the criteria are most often based on read mapping quality and the presence of certain sequence features. Biological approaches that rank fusion candidates by some notion of functional importance are complementary and can offer a significant improvement in removing false positive calls.
Detects possible drivers within transcriptomes of cancer samples derived from next-generation sequencing (NGS) analysis. Oncofuse consists of a Bayesian classifier which leans on data about known oncogenic fusions to discover novel genes to be considered as potential drivers. It can be used to assist users in determining driver mutations in cancer at the validation step. This software can also be queried from the FusionHub web platform.
Discovers cancer-driver genes showing an excess of somatic mutations at non-synonymous variants. WITER permits the creation of statistically valid p-values and the detection of multiple significant driver-genes. It enables the recognition of potential driver genes in many cancers which are ignored by widely-used alternative methods. This tool belongs to the unsupervised category and therefore does not suffer from training bias.
Prioritizes fusion drivers from hundreds or thousands of fusion candidates identified in diverse cancer types. Fusion centrality is based on a domain fusion model built on the theory of exon/domain shuffling. It leads to a hypothesis that a fusion is more likely to be an oncogenic driver if its partner genes act like hubs in a network because the fusion mutation can deregulate normal functions of many other genes and their pathways.
A pipeline for the annotation and prediction of biologically functional gene fusion candidates. Pegasus provides a common interface for various gene fusion detection tools, reconstruction of novel fusion proteins, reading-frame-aware annotation of preserved/lost functional domains, and data-driven classification of oncogenic potential. Pegasus dramatically streamlines the search for oncogenic gene fusions, bridging the gap between raw RNA-Seq data and a final, tractable list of candidates for experimental validation.
Allows users to manipulate, analyze and filter fusion events detected by a variety of fusion detection tools. Chimera performs a first step of validation, which can be useful given the high number of false-positive results reported, in most cases, by fusion detection tools. It is able to integrate and compare the data produced by different fusion detection tools. This tool provides a validation procedure based on de novo assembly.
Deduces the molecular interactions and pathways associated with a fusion. FusionPathway also serves for the investigation of potential therapeutic targets in these pathways. It can assist researchers for developing therapeutic strategies for patients who harbor undruggable fusions. This tool enables the prediction of novel molecular interactions of proteins on the basis of multiple domain combinations.