FUNGI: FUsioN Gene Integration toolset.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
11 Oct 2021
11 Oct 2021
Historique:
received:
18
08
2020
revised:
27
02
2021
accepted:
25
03
2021
medline:
28
3
2021
pubmed:
28
3
2021
entrez:
27
3
2021
Statut:
ppublish
Résumé
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. 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. FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 33772596
pii: 6194566
doi: 10.1093/bioinformatics/btab206
pmc: PMC8504624
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3353-3355Subventions
Organisme : European Union's Horizon 2020 research and innovation programme
ID : 667403
Organisme : Academy of Finland
Organisme : Sigrid Jusélius Foundation
Organisme : Finnish Cultural Foundation
Organisme : Finnish Cancer Association
Organisme : NCI
Organisme : NHGRI
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press.