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SQUID: transcriptomic structural variation detection from RNA-seq

Transcripts are frequently modified by structural variations, which lead to fused transcripts of either multiple genes, known as a fusion gene, or a gene and a previously non-transcribed sequence. Detecting these modifications, called transcriptomic structural variations (TSVs), especially in cancer...

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Detalles Bibliográficos
Autores principales: Ma, Cong, Shao, Mingfu, Kingsford, Carl
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896115/
https://www.ncbi.nlm.nih.gov/pubmed/29650026
http://dx.doi.org/10.1186/s13059-018-1421-5
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author Ma, Cong
Shao, Mingfu
Kingsford, Carl
author_facet Ma, Cong
Shao, Mingfu
Kingsford, Carl
author_sort Ma, Cong
collection PubMed
description Transcripts are frequently modified by structural variations, which lead to fused transcripts of either multiple genes, known as a fusion gene, or a gene and a previously non-transcribed sequence. Detecting these modifications, called transcriptomic structural variations (TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to predict both fusion-gene and non-fusion-gene TSVs accurately from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model and doubles the precision on simulation data compared to other approaches. Using SQUID, we identify novel non-fusion-gene TSVs on TCGA samples. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1421-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-58961152018-04-20 SQUID: transcriptomic structural variation detection from RNA-seq Ma, Cong Shao, Mingfu Kingsford, Carl Genome Biol Method Transcripts are frequently modified by structural variations, which lead to fused transcripts of either multiple genes, known as a fusion gene, or a gene and a previously non-transcribed sequence. Detecting these modifications, called transcriptomic structural variations (TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to predict both fusion-gene and non-fusion-gene TSVs accurately from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model and doubles the precision on simulation data compared to other approaches. Using SQUID, we identify novel non-fusion-gene TSVs on TCGA samples. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1421-5) contains supplementary material, which is available to authorized users. BioMed Central 2018-04-12 /pmc/articles/PMC5896115/ /pubmed/29650026 http://dx.doi.org/10.1186/s13059-018-1421-5 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Method
Ma, Cong
Shao, Mingfu
Kingsford, Carl
SQUID: transcriptomic structural variation detection from RNA-seq
title SQUID: transcriptomic structural variation detection from RNA-seq
title_full SQUID: transcriptomic structural variation detection from RNA-seq
title_fullStr SQUID: transcriptomic structural variation detection from RNA-seq
title_full_unstemmed SQUID: transcriptomic structural variation detection from RNA-seq
title_short SQUID: transcriptomic structural variation detection from RNA-seq
title_sort squid: transcriptomic structural variation detection from rna-seq
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896115/
https://www.ncbi.nlm.nih.gov/pubmed/29650026
http://dx.doi.org/10.1186/s13059-018-1421-5
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