FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data

Deep sequencing approaches applied to transcriptome profiling (RNA-Seq) are dramatically impacting our understanding of the extent and complexity of eukaryotic transcription [1-4]. RNA-Seq provides a more accurate measurement of expression levels of genes and more information about alternative splicing of their isoforms compared to other chip-based methods [1,4-10].Large international consortia, such as the ENCODE project [11] and the modENCODE project [12], are exploiting this technology to obtain a better picture of the transcriptome. More recently, RNA-Seq was applied to the identification of fusion transcripts, where mRNAs from two different genes are joined together [13-17]. Although the role of these chimeric transcripts is not fully understood, some studies have shown that they might be implicated in cancer [18,19]. Also, a fusion transcript may indicate an underlying genomic rearrangement between the two genes. Such gene fusions are thought to drive molecular events, such as in chronic myelogenous leukemia, which is defined by the reciprocal translocation between chromosome 9 and 22 leading to a chimeric fusion oncogene (BCR-ABL1) encoding a tyrosine kinase that is constitutively active.Most gene fusions reported in the past have been attributed to hematological cancers [20-22]. Recently, recurrent fusions between the transmembrane protease serine 2 (TMPRSS2) gene and members of the ETS family of transcription factors (mainly the v-ets erythroblastosis virus E26 oncogene homolog (avian), ERG, and the ets variant 1, ETV1) were reported in prostate cancer [23]. Other epithelial tumors, such as lung and breast cancer, also harbor translocations [24-26].Compared to DNA sequencing, RNA-Seq seems to have less requirements in terms of overall coverage, since it aims at sequencing only the regions of the genome that are transcribed and spliced into mature mRNA, which current estimates set at about 2 to 6%. However, this apparent advantage of RNA-Seq in practice is n