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Article Abstract
Motivation: Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules.
Results: We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance.
Availabilityand Implementation: FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504624 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btab206 | DOI Listing |
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