Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer.

TitlePegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer.
Publication TypeJournal Article
Year of Publication2014
AuthorsAbate F, Zairis S, Ficarra E, Acquaviva A, Wiggins CH, Frattini V, Lasorella A, Iavarone A, Inghirami G, Rabadan R
JournalBMC Syst Biol
Volume8
Pagination97
Date Published2014 Sep 04
ISSN1752-0509
KeywordsComputational Biology, Databases, Genetic, Decision Trees, Gene Fusion, Humans, Molecular Sequence Annotation, Neoplasms, Software
Abstract

BACKGROUND: The extraordinary success of imatinib in the treatment of BCR-ABL1 associated cancers underscores the need to identify novel functional gene fusions in cancer. RNA sequencing offers a genome-wide view of expressed transcripts, uncovering biologically functional gene fusions. Although several bioinformatics tools are already available for the detection of putative fusion transcripts, candidate event lists are plagued with non-functional read-through events, reverse transcriptase template switching events, incorrect mapping, and other systematic errors. Such lists lack any indication of oncogenic relevance, and they are too large for exhaustive experimental validation.

RESULTS: We have designed and implemented a pipeline, Pegasus, for the annotation and prediction of biologically functional gene fusion candidates. Pegasus provides a common interface for various gene fusion detection tools, reconstruction of novel fusion proteins, reading-frame-aware annotation of preserved/lost functional domains, and data-driven classification of oncogenic potential. Pegasus dramatically streamlines the search for oncogenic gene fusions, bridging the gap between raw RNA-Seq data and a final, tractable list of candidates for experimental validation.

CONCLUSION: We show the effectiveness of Pegasus in predicting new driver fusions in 176 RNA-Seq samples of glioblastoma multiforme (GBM) and 23 cases of anaplastic large cell lymphoma (ALCL).

DOI10.1186/s12918-014-0097-z
Alternate JournalBMC Syst Biol
PubMed ID25183062
Grant ListR01 CA164152-01 / CA / NCI NIH HHS / United States
R01 CA179044-01A1 / CA / NCI NIH HHS / United States
U54 CA121852 / CA / NCI NIH HHS / United States
Related Faculty: 
Giorgio Inghirami, M.D.

Pathology & Laboratory Medicine 1300 York Avenue New York, NY 10065 Phone: (212) 746-6464
Surgical Pathology: (212) 746-2700