Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.

Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requir...

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Autores principales:	Ridvan Eksi, Hong-Dong Li, Rajasree Menon, Yuchen Wen, Gilbert S Omenn, Matthias Kretzler, Yuanfang Guan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/44d3897abd1d4400a87a1f45085e984e
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spelling	oai:doaj.org-article:44d3897abd1d4400a87a1f45085e984e2021-11-18T05:53:26ZSystematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.1553-734X1553-735810.1371/journal.pcbi.1003314https://doaj.org/article/44d3897abd1d4400a87a1f45085e984e2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24244129/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires 'ground-truth' functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the 'responsible' isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the 'responsible' isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current gene-centered function prediction to isoform-level predictions.Ridvan EksiHong-Dong LiRajasree MenonYuchen WenGilbert S OmennMatthias KretzlerYuanfang GuanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 11, p e1003314 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ridvan Eksi Hong-Dong Li Rajasree Menon Yuchen Wen Gilbert S Omenn Matthias Kretzler Yuanfang Guan Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.
description	Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires 'ground-truth' functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the 'responsible' isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the 'responsible' isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current gene-centered function prediction to isoform-level predictions.
format	article
author	Ridvan Eksi Hong-Dong Li Rajasree Menon Yuchen Wen Gilbert S Omenn Matthias Kretzler Yuanfang Guan
author_facet	Ridvan Eksi Hong-Dong Li Rajasree Menon Yuchen Wen Gilbert S Omenn Matthias Kretzler Yuanfang Guan
author_sort	Ridvan Eksi
title	Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.
title_short	Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.
title_full	Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.
title_fullStr	Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.
title_full_unstemmed	Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.
title_sort	systematically differentiating functions for alternatively spliced isoforms through integrating rna-seq data.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/44d3897abd1d4400a87a1f45085e984e
work_keys_str_mv	AT ridvaneksi systematicallydifferentiatingfunctionsforalternativelysplicedisoformsthroughintegratingrnaseqdata AT hongdongli systematicallydifferentiatingfunctionsforalternativelysplicedisoformsthroughintegratingrnaseqdata AT rajasreemenon systematicallydifferentiatingfunctionsforalternativelysplicedisoformsthroughintegratingrnaseqdata AT yuchenwen systematicallydifferentiatingfunctionsforalternativelysplicedisoformsthroughintegratingrnaseqdata AT gilbertsomenn systematicallydifferentiatingfunctionsforalternativelysplicedisoformsthroughintegratingrnaseqdata AT matthiaskretzler systematicallydifferentiatingfunctionsforalternativelysplicedisoformsthroughintegratingrnaseqdata AT yuanfangguan systematicallydifferentiatingfunctionsforalternativelysplicedisoformsthroughintegratingrnaseqdata
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Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.

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