Identification of Fusion Transcripts from Unaligned RNA-Seq Reads Using ChimeRScope

Neetha Nanoth Vellichirammal; Abrar Albahrani; You Li; Chittibabu Guda

doi:10.1007/978-1-4939-9904-0_2

Identification of Fusion Transcripts from Unaligned RNA-Seq Reads Using ChimeRScope

Methods Mol Biol. 2020:2079:13-25. doi: 10.1007/978-1-4939-9904-0_2.

Authors

Neetha Nanoth Vellichirammal¹, Abrar Albahrani¹, You Li², Chittibabu Guda^{3

4}

Affiliations

¹ Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA.
² HitGen Ltd., Chengdu, China.
³ Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA. babu.guda@unmc.edu.
⁴ Bioinformatics and Systems Biology Core, Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA. babu.guda@unmc.edu.

PMID: 31728959
DOI: 10.1007/978-1-4939-9904-0_2

Abstract

Fusion transcripts that are frequent in cancer can be exploited to understand the mechanisms of malignancy and can serve as diagnostic or prognostic markers. Several algorithms have been developed to predict fusion transcripts from DNA or RNA data. The majority of these algorithms align sequencing reads to the reference transcriptome for predicting fusions; however, this results in several undetected fusions due to the highly perturbed nature of cancer genomes. Here, we describe a novel method that uses a k-mer based algorithm to predict fusion transcripts accurately using the unaligned reads from the regular RNA-seq data analysis pipelines.

Keywords: ChimeRScope; Fusion detection algorithm; Fusion transcripts; Oncogenic fusions; RNA-seq; k-mers.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Algorithms
Computational Biology / methods*
Gene Fusion*
Humans
RNA-Seq* / methods
Software*
Workflow

Abstract

Publication types

MeSH terms

Grants and funding