Which are the state-of-the-art computational tools for the prediction of gene fusion events?
Gene fusion detection - also known as the 'Rosetta Stone' method - involves the identification of fused composite genes in a set of reference genomes, which indicates potential interactions between its un-fused counterpart genes in query genomes. A few methods/tools and computational pipelines for the detection of gene fusion events have been introduced. The basic steps followed in these approaches consist of (i) all-against-all sequence comparison, (ii) detection of non-overlapping similarities of two genes/proteins (components) to a single gene/protein (composite), and optionally (iii) elimination of putative spurious hits (e.g. due to promiscuous domains) achieves via clustering based on sequence similarity and examining dense regions of the resulting graph or by querying the PFAM database. An advantage of gene fusion analysis is that functional associations can be predicted even in cases of genes of unknown function. Due to the computationally intense nature of these approaches, precompiled data of this type are often organized in specialized databases. Tools and databases developed for this purpose include (in alphabetical order): fdfBLAST, FusionDB, InPrePPI, (Integrated method for Prediction of Protein-Protein Interactions), MosaicFinder, Phydbac2, PLEX, Predictome, Rosetta Stone method, STRING.