Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector

Brian J Haas; Alexander Dobin; Mahmoud Ghandi; Anne Van Arsdale; Timothy Tickle; James T Robinson; Riaz Gillani; Simon Kasif; Aviv Regev

doi:10.1016/j.crmeth.2023.100467

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector

Cell Rep Methods. 2023 May 8;3(5):100467. doi: 10.1016/j.crmeth.2023.100467. eCollection 2023 May 22.

Authors

Brian J Haas^{1

2}, Alexander Dobin³, Mahmoud Ghandi⁴, Anne Van Arsdale^{5

6}, Timothy Tickle¹, James T Robinson⁷, Riaz Gillani^{8

9

10

11}, Simon Kasif^{2

12}, Aviv Regev^{1

13

14}

Affiliations

¹ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
² Graduate Program in Bioinformatics, Boston University, Boston, MA 02215, USA.
³ Cold Spring Harbor Laboratory, New York, NY 11724, USA.
⁴ Monte Rosa Therapeutics, Boston, MA 02210, USA.
⁵ Department of Obstetrics and Gynecology and Women's Health, Albert Einstein Montefiore Medical Center, Bronx, NY 10461, USA.
⁶ Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
⁷ School of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
⁸ Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
⁹ Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
¹⁰ Department of Pediatrics, Harvard Medical School, Boston, MA 02215, USA.
¹¹ Boston Children's Hospital, Boston, MA 02115, USA.
¹² Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
¹³ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
¹⁴ Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.

Abstract

Here, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA sequencing (RNA-seq) and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.

Keywords: FusionInspector; RNA-seq; STAR-Fusion; Trinity; cancer; fusion.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

High-Throughput Nucleotide Sequencing*
Humans
Neoplasms* / genetics
Sequence Analysis, RNA
Transcriptome / genetics

Abstract

Publication types

MeSH terms

Grants and funding