Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism

Hdl Handle:
http://hdl.handle.net/10149/621399
Title:
Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism
Authors:
Angione, C. (Claudio) ( 0000-0002-3140-7909 )
Affiliation:
Teesside University. Digital Futures Institute
Citation:
Angione, C. (2017) 'Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism' Bioinformatics; Online first 08 Sept 2017
Publisher:
Oxford University Press
Journal:
Bioinformatics
Issue Date:
8-Sep-2017
URI:
http://hdl.handle.net/10149/621399
Additional Links:
https://academic.oup.com/bioinformatics/article-abstract/doi/10.1093/bioinformatics/btx562/4107935/Integrating-splice-isoform-expression-into-genome?redirectedFrom=fulltext
Abstract:
Motivation: Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict cellular phenotype. During the process of gene expression, 95% of human genes can code for multiple proteins due to alternative splicing. While most splice variants of a gene carry the same function, variants within some key genes can have remarkably different roles. To bridge the gap between genotype and phenotype, condition- and tissue-specific models of metabolism have been constructed. However, current metabolic models only include information at the gene level. Consequently, as recently acknowledged by the scientific community, common situations where changes in splice-isoformexpression levels alter the metabolic outcome cannot be modeled. Results: We here propose GEMsplice, the first method for the incorporation of splice-isoform expression data into genome-scale metabolic models. Using GEMsplice, we make full use of RNA-Seq quantitative expression profiles to predict, for the first time, the effects of splice isoform-level changes in the metabolism of 1455 patients with 31 different breast cancer types. We validate GEMsplice by generating cancerversus- normal predictions on metabolic pathways, and by comparing with gene-level approaches and available literature on pathways affected by breast cancer. GEMsplice is freely available for academic use at https://github.com/GEMsplice/GEMsplice_code. Compared to state-of-the-art methods, we anticipate that GEMsplice will enable for the first time computational analyses at transcript level with splice-isoform resolution.
Type:
Article
Language:
en
ISSN:
1367-4803
EISSN:
1460-2059
Rights:
Following a 12 month embargo author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/issn/1367-4803/ [Accessed: 07/09/2017]

Full metadata record

DC FieldValue Language
dc.contributor.authorAngione, C. (Claudio)en
dc.date.accessioned2017-09-07T11:47:52Z-
dc.date.available2017-09-07T11:47:52Z-
dc.date.issued2017-09-08-
dc.identifier.citationBioinformatics; Online first 08 Sept 2017en
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10149/621399-
dc.description.abstractMotivation: Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict cellular phenotype. During the process of gene expression, 95% of human genes can code for multiple proteins due to alternative splicing. While most splice variants of a gene carry the same function, variants within some key genes can have remarkably different roles. To bridge the gap between genotype and phenotype, condition- and tissue-specific models of metabolism have been constructed. However, current metabolic models only include information at the gene level. Consequently, as recently acknowledged by the scientific community, common situations where changes in splice-isoformexpression levels alter the metabolic outcome cannot be modeled. Results: We here propose GEMsplice, the first method for the incorporation of splice-isoform expression data into genome-scale metabolic models. Using GEMsplice, we make full use of RNA-Seq quantitative expression profiles to predict, for the first time, the effects of splice isoform-level changes in the metabolism of 1455 patients with 31 different breast cancer types. We validate GEMsplice by generating cancerversus- normal predictions on metabolic pathways, and by comparing with gene-level approaches and available literature on pathways affected by breast cancer. GEMsplice is freely available for academic use at https://github.com/GEMsplice/GEMsplice_code. Compared to state-of-the-art methods, we anticipate that GEMsplice will enable for the first time computational analyses at transcript level with splice-isoform resolution.en
dc.language.isoenen
dc.publisherOxford University Pressen
dc.relation.urlhttps://academic.oup.com/bioinformatics/article-abstract/doi/10.1093/bioinformatics/btx562/4107935/Integrating-splice-isoform-expression-into-genome?redirectedFrom=fulltext-
dc.rightsFollowing a 12 month embargo author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/issn/1367-4803/ [Accessed: 07/09/2017]en
dc.titleIntegrating splice-isoform expression into genome-scale models characterizes breast cancer metabolismen
dc.typeArticleen
dc.identifier.eissn1460-2059-
dc.contributor.departmentTeesside University. Digital Futures Instituteen
dc.identifier.journalBioinformaticsen
or.citation.harvardAngione, C. (2017) 'Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism' Bioinformatics; Online first 08 Sept 2017-
dc.eprint.versionPost-printen
dc.embargo12 monthsen
dc.date.accepted2017-09-03-
All Items in TeesRep are protected by copyright, with all rights reserved, unless otherwise indicated.