Back to Search
Start Over
Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer
- Source :
- BMC Systems Biology
- Publication Year :
- 2014
-
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). Contact: fa2306@columbia.edu.
- Subjects :
- Systems biology
Biology
DNA sequencing
Fusion gene
03 medical and health sciences
Annotation
0302 clinical medicine
Structural Biology
Neoplasms
Modelling and Simulation
Machine learning
Databases, Genetic
medicine
Humans
Anaplastic large-cell lymphoma
Gene
Molecular Biology
030304 developmental biology
Genetics
0303 health sciences
Gene fusions
Next generation sequecing
Machine Learning
Applied Mathematics
Decision Trees
Computational Biology
Molecular Sequence Annotation
medicine.disease
Fusion protein
3. Good health
Computer Science Applications
030220 oncology & carcinogenesis
Modeling and Simulation
Next-generation sequencing
Gene Fusion
Software
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- BMC Systems Biology
- Accession number :
- edsair.doi.dedup.....e434120a31492db4e77501890e421919