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Nucleic Acids Res. 2017 Jul 27;45(13):e120. doi: 10.1093/nar/gkx315.

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

Author information

1
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA.
2
The Sichuan Key Laboratory for Human Disease Gene Study, Clinical Laboratory Department, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, Sichuan 610072, China.
3
School of Medicine, University of Electronic Science and Technology, Chengdu, Sichuan 610054, China.
4
Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE 68198, USA.
5
Bioinformatics and System Biology Core, University of Nebraska Medical Center, Omaha, NE 68198, USA.

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/).

PMID:
28472320
PMCID:
PMC5737728
DOI:
10.1093/nar/gkx315
[Indexed for MEDLINE]
Free PMC Article

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