is a tool designed to
of fusion genes
. It is a post-processing step that tries to validate in-silico the predictions made by fusion detection software. Goal is to assign a functional prediction score (oncogenic potential, i.e. the probability of being 'driver' events) to fusion sequences identified by other software such as Tophat-fusion, fusioncatcher or STAR. Details can be found
. This module aids in running oncofuse for you.
Upload output from fusion detection softwares such as Tophat-fusion, fusioncatcher or STAR or simply upload coordinate based input format.
Note: This module is in development phase. so you might encounter few bugs. We will soon fix this. Please report us with any bugs you encounter.
If you find this module useful, please cite the following articles.
Panigrahi P, Jere A, Anamika K (2018) FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer. PLoS ONE 13(5): e0196588. https://doi.org/10.1371/journal.pone.0196588
Mikhail Shugay, Inigo Ortiz de Mendíbil, Jose L. Vizmanos and Francisco J. Novo. Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions. Bioinformatics. 16 Aug 2013. doi:10.1093/bioinformatics/btt445.
1. Upload input file
2. Choose the input file type (i.e. output of
or Coordinate based)
3. If coordinate based then make sure to input correct format (see below)
4. Choose correct genome assembly version
5. Choose a tissue type. If tissue source is not known, then select "Average"
Output of FusionCatcher
Output of RNASTAR
Output of StarFusion
Output of Tophat
Output of Tophat-Fusion-Post
Genome assembly version:
Average expression (If tissue source is unknown)
If input type is Coordinates-based, then format is tab separated file with following information
Data must not contain headers.
Tissue_type is the library argument, which tells Oncofuse to use its own pre-built gene expression libraries. There are four pre-built libraries, corresponding to the four supported tissue types: EPI (epithelial origin), HEM (hematological origin), MES (mesenchymal origin) and AVG (average expression, if tissue source is unknown).