A1 Refereed original research article in a scientific journal
FUNGI: Fusion Gene Integration Toolset
Authors: Cervera Alejandra, Rausio Heidi, Kähkönen Tiia, Andersson Noora, Partel Gabriele, Rantanen Ville, Paciello Giulia, Ficarra Elisa, Hynninen Johanna, Hietanen Sakari, Carpén Olli, Lehtonen Rainer, Hautaniemi Sampsa, Huhtinen Kaisa
Publisher: Oxford University Press
Publication year: 2021
Journal: Bioinformatics
Journal acronym: Bioinformatics
Volume: 37
Issue: 19
First page : 3353
Last page: 3355
DOI: https://doi.org/10.1093/bioinformatics/btab206
Web address : https://academic.oup.com/bioinformatics/article/37/19/3353/6194566
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/68087663
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 FUNGI (FUsionN Gene Identification toolset) 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.
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