Cargando…
Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states
Distinguishing cell states based only on gene expression data remains a challenging task. This is true even for analyses within a species. In cross-species comparisons, the results obtained by different groups have varied widely. Here, we integrate RNA-seq data from more than 40 cell and tissue type...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389511/ https://www.ncbi.nlm.nih.gov/pubmed/27980097 http://dx.doi.org/10.1093/nar/gkw1256 |
_version_ | 1782521281322680320 |
---|---|
author | Yang, Yang Yang, Yu-Cheng T. Yuan, Jiapei Lu, Zhi John Li, Jingyi Jessica |
author_facet | Yang, Yang Yang, Yu-Cheng T. Yuan, Jiapei Lu, Zhi John Li, Jingyi Jessica |
author_sort | Yang, Yang |
collection | PubMed |
description | Distinguishing cell states based only on gene expression data remains a challenging task. This is true even for analyses within a species. In cross-species comparisons, the results obtained by different groups have varied widely. Here, we integrate RNA-seq data from more than 40 cell and tissue types of four mammalian species to identify sets of associated genes as indicators for specific cell states in each species. We employ a statistical method, TROM, to identify both protein-coding and non-coding indicators. Next, we map the cell states within each species and also between species using these indicator genes. We recapitulate known phenotypic similarity between related cell and tissue types and reveal molecular basis for their similarity. We also report novel associations between several tissues and cell types with functional support. Moreover, our identified conserved associated genes are found to be a good resource for studying cell differentiation and reprogramming. Lastly, long non-coding RNAs can serve well as associated genes to indicate cell states. We further infer the biological functions of those non-coding associated genes based on their co-expressed protein-coding genes. This study demonstrates that combining statistical modeling with public RNA-seq data can be powerful for improving our understanding of cell identity control. |
format | Online Article Text |
id | pubmed-5389511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-53895112017-04-24 Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states Yang, Yang Yang, Yu-Cheng T. Yuan, Jiapei Lu, Zhi John Li, Jingyi Jessica Nucleic Acids Res Computational Biology Distinguishing cell states based only on gene expression data remains a challenging task. This is true even for analyses within a species. In cross-species comparisons, the results obtained by different groups have varied widely. Here, we integrate RNA-seq data from more than 40 cell and tissue types of four mammalian species to identify sets of associated genes as indicators for specific cell states in each species. We employ a statistical method, TROM, to identify both protein-coding and non-coding indicators. Next, we map the cell states within each species and also between species using these indicator genes. We recapitulate known phenotypic similarity between related cell and tissue types and reveal molecular basis for their similarity. We also report novel associations between several tissues and cell types with functional support. Moreover, our identified conserved associated genes are found to be a good resource for studying cell differentiation and reprogramming. Lastly, long non-coding RNAs can serve well as associated genes to indicate cell states. We further infer the biological functions of those non-coding associated genes based on their co-expressed protein-coding genes. This study demonstrates that combining statistical modeling with public RNA-seq data can be powerful for improving our understanding of cell identity control. Oxford University Press 2017-02-28 2016-12-14 /pmc/articles/PMC5389511/ /pubmed/27980097 http://dx.doi.org/10.1093/nar/gkw1256 Text en © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Computational Biology Yang, Yang Yang, Yu-Cheng T. Yuan, Jiapei Lu, Zhi John Li, Jingyi Jessica Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states |
title | Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states |
title_full | Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states |
title_fullStr | Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states |
title_full_unstemmed | Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states |
title_short | Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states |
title_sort | large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389511/ https://www.ncbi.nlm.nih.gov/pubmed/27980097 http://dx.doi.org/10.1093/nar/gkw1256 |
work_keys_str_mv | AT yangyang largescalemappingofmammaliantranscriptomesidentifiesconservedgenesassociatedwithdifferentcellstates AT yangyuchengt largescalemappingofmammaliantranscriptomesidentifiesconservedgenesassociatedwithdifferentcellstates AT yuanjiapei largescalemappingofmammaliantranscriptomesidentifiesconservedgenesassociatedwithdifferentcellstates AT luzhijohn largescalemappingofmammaliantranscriptomesidentifiesconservedgenesassociatedwithdifferentcellstates AT lijingyijessica largescalemappingofmammaliantranscriptomesidentifiesconservedgenesassociatedwithdifferentcellstates |