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

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

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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.

Original language	English (US)
Title of host publication	Methods in Molecular Biology
Publisher	Humana Press Inc.
Pages	13-25
Number of pages	13
DOIs	https://doi.org/10.1007/978-1-4939-9904-0_2
State	Published - 2020

Publication series

Name	Methods in Molecular Biology
Volume	2079
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Keywords

ChimeRScope
Fusion detection algorithm
Fusion transcripts
Oncogenic fusions
RNA-seq
k-mers

ASJC Scopus subject areas

Molecular Biology
Genetics

Access to Document

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

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title = "Identification of Fusion Transcripts from Unaligned RNA-Seq Reads Using ChimeRScope",

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.",

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author = "Vellichirammal, {Neetha Nanoth} and Abrar Albahrani and You Li and Chittibabu Guda",

note = "Funding Information: This work was supported by the development funds to CG from UNMC and partial support from National Institutes of Health grants [P20GM103427, 1P30GM110768, P30CA036727, and 2P01AG029531].",

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doi = "10.1007/978-1-4939-9904-0_2",

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AU - Albahrani, Abrar

AU - Li, You

AU - Guda, Chittibabu

N1 - Funding Information: This work was supported by the development funds to CG from UNMC and partial support from National Institutes of Health grants [P20GM103427, 1P30GM110768, P30CA036727, and 2P01AG029531].

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - ChimeRScope

KW - Fusion detection algorithm

KW - Fusion transcripts

KW - Oncogenic fusions

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

Abstract

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Keywords

ASJC Scopus subject areas

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