Novornabreak: Local Assembly for Novel Splice Junction and Fusion Transcript Detection from RNA-Seq Data

Authors

Yukun Tan
Vakul Mohanty
Shaoheng Liang
Jinzhuang Dou
Jun Ma
Kun Hee Kim
Marc Jan Bonder
Xinghua Shi
Charles Lee
Zechen Chong
Ken Chen

Publication Date

1-1-2023

Journal

Human Genome Structural Variation Consortium

Abstract

We present novoRNABreak, a unified framework for cancer specific novel splice junction and fusion transcript detection in RNA-seq data obtained from human cancer samples. novoRNABreak is based on a local assembly model, which offers a tradeoff between the alignment-based and de novo whole transcriptome assembly (WTA) methods. This approach is accurate and sensitive in assembling novel junctions that are difficult to directly align or have multiple alignments. Additionally, it is more efficient due to the strategy that focuses on junctions rather than full length transcripts. The performance of novoRNABreak is demonstrated by a comprehensive set of experiments using synthetic data generated based on genome reference, as well as real RNA-seq data from breast cancer and prostate cancer samples. The results show that our tool has a better performance by fully utilizing unmapped reads and precisely identifying the junctions where short reads or small exons have multiple alignments. novoRNABreak is a fully-fledged program available on GitHub (https://github.com/KChen-lab/novoRNABreak).

Keywords

novoRNABreak, unified framework, local assembly model, cancer specific, novel splice junctions, fusion transcripts

DOI

10.26502/jbsb.5107050

PMID

39301431

PMCID

PMC11412692

PubMedCentral® Posted Date

9-19-2024

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes