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

BMC Systems Biology

Raul Rabadan²  Giorgio Inghirami⁵  Antonio Iavarone⁴  Anna Lasorella⁴  Veronique Frattini⁴  Chris H Wiggins¹  Andrea Acquaviva³  Elisa Ficarra³  Sakellarios Zairis²  Francesco Abate³ 

[1] Institute for Data Sciences and Engineering, Columbia University, 500 W. 120th Street, Mudd 524, New York 10027, New York, USA;Center for Computational Biology and Bioinformatics, Columbia University, 1130 St. Nicholas Ave, New York 10032, NY, USA;Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy;Institute for Cancer Genetics, Columbia University Medical Center, New York, New York, USA;Department of Pathology, Center for Experimental Research and Medical Studies, Laboratory of Functional Genomics, University of Torino, Torino, Italy

关键词: Machine learning;    Next-generation sequencing;    Gene fusion;   

Others  :  1127082
DOI  :  10.1186/s12918-014-0097-z

 received in 2014-02-06, accepted in 2014-08-05,  发布年份 2014

【 摘 要 】

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). Contact: [e-mail].

【 授权许可】

   
2014 Abate et al.; licensee BioMed Central Ltd.

【 图 表 】

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