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

You Li, Tayla B. Heavican, Neetha N. Vellichirammal, Javeed Iqbal, Chittibabu Guda

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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 alignmentfree 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 ( or via the Galaxy web-interface at (

Original languageEnglish (US)
Article numbere120
JournalNucleic acids research
Issue number13
StatePublished - Jul 1 2017

ASJC Scopus subject areas

  • Genetics


Dive into the research topics of 'ChimeRScope: A novel alignment-free algorithm for fusion transcript prediction using paired-end RNA-Seq data'. Together they form a unique fingerprint.

Cite this