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Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression...

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Autores principales: Saha, Ashis, Kim, Yungil, Gewirtz, Ariel D.H., Jo, Brian, Gao, Chuan, McDowell, Ian C., Engelhardt, Barbara E., Battle, Alexis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668942/
https://www.ncbi.nlm.nih.gov/pubmed/29021288
http://dx.doi.org/10.1101/gr.216721.116
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author Saha, Ashis
Kim, Yungil
Gewirtz, Ariel D.H.
Jo, Brian
Gao, Chuan
McDowell, Ian C.
Engelhardt, Barbara E.
Battle, Alexis
author_facet Saha, Ashis
Kim, Yungil
Gewirtz, Ariel D.H.
Jo, Brian
Gao, Chuan
McDowell, Ian C.
Engelhardt, Barbara E.
Battle, Alexis
author_sort Saha, Ashis
collection PubMed
description Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.
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spelling pubmed-56689422017-11-13 Co-expression networks reveal the tissue-specific regulation of transcription and splicing Saha, Ashis Kim, Yungil Gewirtz, Ariel D.H. Jo, Brian Gao, Chuan McDowell, Ian C. Engelhardt, Barbara E. Battle, Alexis Genome Res Method Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. Cold Spring Harbor Laboratory Press 2017-11 /pmc/articles/PMC5668942/ /pubmed/29021288 http://dx.doi.org/10.1101/gr.216721.116 Text en © 2017 Saha et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by/4.0/ This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
spellingShingle Method
Saha, Ashis
Kim, Yungil
Gewirtz, Ariel D.H.
Jo, Brian
Gao, Chuan
McDowell, Ian C.
Engelhardt, Barbara E.
Battle, Alexis
Co-expression networks reveal the tissue-specific regulation of transcription and splicing
title Co-expression networks reveal the tissue-specific regulation of transcription and splicing
title_full Co-expression networks reveal the tissue-specific regulation of transcription and splicing
title_fullStr Co-expression networks reveal the tissue-specific regulation of transcription and splicing
title_full_unstemmed Co-expression networks reveal the tissue-specific regulation of transcription and splicing
title_short Co-expression networks reveal the tissue-specific regulation of transcription and splicing
title_sort co-expression networks reveal the tissue-specific regulation of transcription and splicing
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668942/
https://www.ncbi.nlm.nih.gov/pubmed/29021288
http://dx.doi.org/10.1101/gr.216721.116
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