Fcirc: A comprehensive pipeline for the exploration of fusion linear and circular RNAs.
Fcirc
fusion circRNA
fusion linear RNA
performance benchmarks
Journal
GigaScience
ISSN: 2047-217X
Titre abrégé: Gigascience
Pays: United States
ID NLM: 101596872
Informations de publication
Date de publication:
01 06 2020
01 06 2020
Historique:
received:
06
11
2019
revised:
01
03
2020
accepted:
29
04
2020
entrez:
30
5
2020
pubmed:
30
5
2020
medline:
5
10
2021
Statut:
ppublish
Résumé
In cancer cells, fusion genes can produce linear and chimeric fusion-circular RNAs (f-circRNAs), which are functional in gene expression regulation and implicated in malignant transformation, cancer progression, and therapeutic resistance. For specific cancers, proteins encoded by fusion transcripts have been identified as innovative therapeutic targets (e.g., EML4-ALK). Even though RNA sequencing (RNA-Seq) technologies combined with existing bioinformatics approaches have enabled researchers to systematically identify fusion transcripts, specifically detecting f-circRNAs in cells remains challenging owing to their general sparsity and low abundance in cancer cells but also owing to imperfect computational methods. We developed the Python-based workflow "Fcirc" to identify fusion linear and f-circRNAs from RNA-Seq data with high specificity. We applied Fcirc to 3 different types of RNA-Seq data scenarios: (i) actual synthetic spike-in RNA-Seq data, (ii) simulated RNA-Seq data, and (iii) actual cancer cell-derived RNA-Seq data. Fcirc showed significant advantages over existing methods regarding both detection accuracy (i.e., precision, recall, F-measure) and computing performance (i.e., lower runtimes). Fcirc is a powerful and comprehensive Python-based pipeline to identify linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computing times compared with previously published algorithms. Fcirc empowers the research community to study the biology of fusion RNAs in cancer more effectively.
Sections du résumé
BACKGROUND
In cancer cells, fusion genes can produce linear and chimeric fusion-circular RNAs (f-circRNAs), which are functional in gene expression regulation and implicated in malignant transformation, cancer progression, and therapeutic resistance. For specific cancers, proteins encoded by fusion transcripts have been identified as innovative therapeutic targets (e.g., EML4-ALK). Even though RNA sequencing (RNA-Seq) technologies combined with existing bioinformatics approaches have enabled researchers to systematically identify fusion transcripts, specifically detecting f-circRNAs in cells remains challenging owing to their general sparsity and low abundance in cancer cells but also owing to imperfect computational methods.
RESULTS
We developed the Python-based workflow "Fcirc" to identify fusion linear and f-circRNAs from RNA-Seq data with high specificity. We applied Fcirc to 3 different types of RNA-Seq data scenarios: (i) actual synthetic spike-in RNA-Seq data, (ii) simulated RNA-Seq data, and (iii) actual cancer cell-derived RNA-Seq data. Fcirc showed significant advantages over existing methods regarding both detection accuracy (i.e., precision, recall, F-measure) and computing performance (i.e., lower runtimes).
CONCLUSION
Fcirc is a powerful and comprehensive Python-based pipeline to identify linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computing times compared with previously published algorithms. Fcirc empowers the research community to study the biology of fusion RNAs in cancer more effectively.
Identifiants
pubmed: 32470133
pii: 5848590
doi: 10.1093/gigascience/giaa054
pmc: PMC7259471
pii:
doi:
Substances chimiques
RNA, Circular
0
RNA
63231-63-0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
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