Title | Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer. |
Publication Type | Journal Article |
Year of Publication | 2014 |
Authors | Abate F, Zairis S, Ficarra E, Acquaviva A, Wiggins CH, Frattini V, Lasorella A, Iavarone A, Inghirami G, Rabadan R |
Journal | BMC Syst Biol |
Volume | 8 |
Pagination | 97 |
Date Published | 2014 Sep 04 |
ISSN | 1752-0509 |
Keywords | Computational 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). |
DOI | 10.1186/s12918-014-0097-z |
Alternate Journal | BMC Syst Biol |
PubMed ID | 25183062 |
Grant List | R01 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.