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ChimeRScope: a novel alignment-free algorithm for fusion transcript prediction using paired-end RNA-Seq data.

Abstract：

:The RNA-Seq technology has revolutionized transcriptome characterization not only by accurately quantifying gene expression, but also by the identification of novel transcripts like chimeric fusion transcripts. The 'fusion' or 'chimeric' transcripts have improved the diagnosis and prognosis of several tumors, and have led to the development of novel therapeutic regimen. The fusion transcript detection is currently accomplished by several software packages, primarily relying on sequence alignment algorithms. The alignment of sequencing reads from fusion transcript loci in cancer genomes can be highly challenging due to the incorrect mapping induced by genomic alterations, thereby limiting the performance of alignment-based fusion transcript detection methods. Here, we developed a novel alignment-free method, ChimeRScope that accurately predicts fusion transcripts based on the gene fingerprint (as k-mers) profiles of the RNA-Seq paired-end reads. Results on published datasets and in-house cancer cell line datasets followed by experimental validations demonstrate that ChimeRScope consistently outperforms other popular methods irrespective of the read lengths and sequencing depth. More importantly, results on our in-house datasets show that ChimeRScope is a better tool that is capable of identifying novel fusion transcripts with potential oncogenic functions. ChimeRScope is accessible as a standalone software at (https://github.com/ChimeRScope/ChimeRScope/wiki) or via the Galaxy web-interface at (https://galaxy.unmc.edu/).

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

Li Y,Heavican TB,Vellichirammal NN,Iqbal J,Guda C

doi

10.1093/nar/gkx315

subject

Has Abstract

pub_date

2017-07-27 00:00:00

pages

e120

issue

13

eissn

0305-1048

issn

1362-4962

pii

3787810

journal_volume

45

pub_type

杂志文章

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