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Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas
The domestic pig (Sus scrofa) is both an economically important livestock species and a model for biomedical research. Two highly contiguous pig reference genomes have recently been released. To support functional annotation of the pig genomes and comparative analysis with large human transcriptomic...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034361/ https://www.ncbi.nlm.nih.gov/pubmed/32117413 http://dx.doi.org/10.3389/fgene.2019.01355 |
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author | Summers, Kim M. Bush, Stephen J. Wu, Chunlei Su, Andrew I. Muriuki, Charity Clark, Emily L. Finlayson, Heather A. Eory, Lel Waddell, Lindsey A. Talbot, Richard Archibald, Alan L. Hume, David A. |
author_facet | Summers, Kim M. Bush, Stephen J. Wu, Chunlei Su, Andrew I. Muriuki, Charity Clark, Emily L. Finlayson, Heather A. Eory, Lel Waddell, Lindsey A. Talbot, Richard Archibald, Alan L. Hume, David A. |
author_sort | Summers, Kim M. |
collection | PubMed |
description | The domestic pig (Sus scrofa) is both an economically important livestock species and a model for biomedical research. Two highly contiguous pig reference genomes have recently been released. To support functional annotation of the pig genomes and comparative analysis with large human transcriptomic data sets, we aimed to create a pig gene expression atlas. To achieve this objective, we extended a previous approach developed for the chicken. We downloaded RNAseq data sets from public repositories, down-sampled to a common depth, and quantified expression against a reference transcriptome using the mRNA quantitation tool, Kallisto. We then used the network analysis tool Graphia to identify clusters of transcripts that were coexpressed across the merged data set. Consistent with the principle of guilt-by-association, we identified coexpression clusters that were highly tissue or cell-type restricted and contained transcription factors that have previously been implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes, such as the cell cycle and oxidative phosphorylation. The same approach was used to identify coexpression clusters within RNAseq data from multiple individual liver and brain samples, highlighting cell type, process, and region-specific gene expression. Evidence of conserved expression can add confidence to assignment of orthology between pig and human genes. Many transcripts currently identified as novel genes with ENSSSCG or LOC IDs were found to be coexpressed with annotated neighbouring transcripts in the same orientation, indicating they may be products of the same transcriptional unit. The meta-analytic approach to utilising public RNAseq data is extendable to include new data sets and new species and provides a framework to support the Functional Annotation of Animals Genomes (FAANG) initiative. |
format | Online Article Text |
id | pubmed-7034361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70343612020-02-28 Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas Summers, Kim M. Bush, Stephen J. Wu, Chunlei Su, Andrew I. Muriuki, Charity Clark, Emily L. Finlayson, Heather A. Eory, Lel Waddell, Lindsey A. Talbot, Richard Archibald, Alan L. Hume, David A. Front Genet Genetics The domestic pig (Sus scrofa) is both an economically important livestock species and a model for biomedical research. Two highly contiguous pig reference genomes have recently been released. To support functional annotation of the pig genomes and comparative analysis with large human transcriptomic data sets, we aimed to create a pig gene expression atlas. To achieve this objective, we extended a previous approach developed for the chicken. We downloaded RNAseq data sets from public repositories, down-sampled to a common depth, and quantified expression against a reference transcriptome using the mRNA quantitation tool, Kallisto. We then used the network analysis tool Graphia to identify clusters of transcripts that were coexpressed across the merged data set. Consistent with the principle of guilt-by-association, we identified coexpression clusters that were highly tissue or cell-type restricted and contained transcription factors that have previously been implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes, such as the cell cycle and oxidative phosphorylation. The same approach was used to identify coexpression clusters within RNAseq data from multiple individual liver and brain samples, highlighting cell type, process, and region-specific gene expression. Evidence of conserved expression can add confidence to assignment of orthology between pig and human genes. Many transcripts currently identified as novel genes with ENSSSCG or LOC IDs were found to be coexpressed with annotated neighbouring transcripts in the same orientation, indicating they may be products of the same transcriptional unit. The meta-analytic approach to utilising public RNAseq data is extendable to include new data sets and new species and provides a framework to support the Functional Annotation of Animals Genomes (FAANG) initiative. Frontiers Media S.A. 2020-02-14 /pmc/articles/PMC7034361/ /pubmed/32117413 http://dx.doi.org/10.3389/fgene.2019.01355 Text en Copyright © 2020 Summers, Bush, Wu, Su, Muriuki, Clark, Finlayson, Eory, Waddell, Talbot, Archibald and Hume http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Summers, Kim M. Bush, Stephen J. Wu, Chunlei Su, Andrew I. Muriuki, Charity Clark, Emily L. Finlayson, Heather A. Eory, Lel Waddell, Lindsey A. Talbot, Richard Archibald, Alan L. Hume, David A. Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas |
title | Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas |
title_full | Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas |
title_fullStr | Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas |
title_full_unstemmed | Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas |
title_short | Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas |
title_sort | functional annotation of the transcriptome of the pig, sus scrofa, based upon network analysis of an rnaseq transcriptional atlas |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034361/ https://www.ncbi.nlm.nih.gov/pubmed/32117413 http://dx.doi.org/10.3389/fgene.2019.01355 |
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