Barnacle: Detecting and Characterizing Tandem Duplications and Fusions in Transcriptome Assemblies

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2013 Aug 15

PMID 23941359

Citations 5

Authors

Lucas Swanson

Gordon Robertson

Karen L Mungall

Yaron S Butterfield

Readman Chiu

Richard D Corbett

T Roderick Docking

Donna Hogge

Shaun D Jackman

Richard A Moore

Andrew J Mungall

Ka Ming Nip

Jeremy D K Parker

Jenny Qing Qian

Anthony Raymond

Sandy Sung

Angela Tam

Nina Thiessen

Richard Varhol

Sherry Wang

Deniz Yorukoglu

Yongjun Zhao

Pamela A Hoodless

S Cenk Sahinalp

Aly Karsan

Inanc Birol

Affiliations

Soon will be listed here.

Abstract

Background: Chimeric transcripts, including partial and internal tandem duplications (PTDs, ITDs) and gene fusions, are important in the detection, prognosis, and treatment of human cancers.

Results: We describe Barnacle, a production-grade analysis tool that detects such chimeras in de novo assemblies of RNA-seq data, and supports prioritizing them for review and validation by reporting the relative coverage of co-occurring chimeric and wild-type transcripts. We demonstrate applications in large-scale disease studies, by identifying PTDs in MLL, ITDs in FLT3, and reciprocal fusions between PML and RARA, in two deeply sequenced acute myeloid leukemia (AML) RNA-seq datasets.

Conclusions: Our analyses of real and simulated data sets show that, with appropriate filter settings, Barnacle makes highly specific predictions for three types of chimeric transcripts that are important in a range of cancers: PTDs, ITDs, and fusions. High specificity makes manual review and validation efficient, which is necessary in large-scale disease studies. Characterizing an extended range of chimera types will help generate insights into progression, treatment, and outcomes for complex diseases.

Citing Articles

MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data.

Cmero M, Schmidt B, Majewski I, Ekert P, Oshlack A, Davidson N Genome Biol. 2021; 22(1):296.

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SQUID: transcriptomic structural variation detection from RNA-seq.

Ma C, Shao M, Kingsford C Genome Biol. 2018; 19(1):52.

PMID: 29650026 PMC: 5896115. DOI: 10.1186/s13059-018-1421-5.

Allelic decomposition and exact genotyping of highly polymorphic and structurally variant genes.

Numanagic I, Malikic S, Ford M, Qin X, Toji L, Radovich M Nat Commun. 2018; 9(1):828.

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Computational identification of micro-structural variations and their proteogenomic consequences in cancer.

Lin Y, Gawronski A, Hach F, Li S, Numanagic I, Sarrafi I Bioinformatics. 2017; 34(10):1672-1681.

PMID: 29267878 PMC: 5946953. DOI: 10.1093/bioinformatics/btx807.

Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer.

Shlien A, Raine K, Fuligni F, Arnold R, Nik-Zainal S, Dronov S Cell Rep. 2016; 16(7):2032-46.

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