Bellerophontes: an RNA-Seq data analysis framework for chimeric transcripts discovery based on accurate fusion model


Motivation: Next-generation sequencing technology allows the detection of genomic structural variations, novel genes and transcript isoforms from the analysis of high-throughput data. In this work, we propose a new framework for the detection of fusion transcripts through short paired-end reads which integrates splicing-driven alignment and abundance estimation analysis, producing a more accurate set of reads supporting the junction discovery and taking into account also not annotated transcripts. Bellerophontes performs a selection of putative junctions on the basis of a match to an accurate gene fusion model.

Results: We report the fusion genes discovered by the proposed framework on experimentally validated biological samples of chronic myelogenous leukemia (CML) and on public NCBI datasets, for which Bellerophontes is able to detect the exact junction sequence. With respect to state-of-art approaches, Bellerophontes detects the same experimentally validated fusions, however, it is more selective on the total number of detected fusions and provides a more accurate set of spanning reads supporting the junctions. We finally report the fusions involving non-annotated transcripts found in CML samples.

Availability and implementation: Bellerophontes JAVA/Perl/Bash software implementation is free and available at


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I am an experienced Bio/Cheminformatics Scientist with over 10 years of experience in management and statistical analysis of large datasets and implementations of machine learning approaches. In addition, I am a skilled programmer with affinity with several programming languages including C/C++, Java, and Python.