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COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference
The advent of RNA-sequencing and microarray technologies has led to rapid growth of transcriptome data generated for a wide range of organisms, under various cellular, organ and individual conditions. Since the number of possible combinations of intercellular and extracellular conditions is almost u...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324053/ https://www.ncbi.nlm.nih.gov/pubmed/30462320 http://dx.doi.org/10.1093/nar/gky1155 |
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author | Obayashi, Takeshi Kagaya, Yuki Aoki, Yuichi Tadaka, Shu Kinoshita, Kengo |
author_facet | Obayashi, Takeshi Kagaya, Yuki Aoki, Yuichi Tadaka, Shu Kinoshita, Kengo |
author_sort | Obayashi, Takeshi |
collection | PubMed |
description | The advent of RNA-sequencing and microarray technologies has led to rapid growth of transcriptome data generated for a wide range of organisms, under various cellular, organ and individual conditions. Since the number of possible combinations of intercellular and extracellular conditions is almost unlimited, cataloging all transcriptome conditions would be an immeasurable challenge. Gene coexpression refers to the similarity of gene expression patterns under various conditions, such as disease states, tissue types, and developmental stages. Since the quality of gene coexpression data depends on the quality and quantity of transcriptome data, timely usage of the growing data is key to promoting individual research in molecular biology. COXPRESdb (http://coxpresdb.jp) is a database providing coexpression information for 11 animal species. One characteristic feature of COXPRESdb is its ability to compare multiple coexpression data derived from different transcriptomics technologies and different species, which strongly reduces false positive relationships in individual gene coexpression data. Here, we summarized the current version of this database, including 23 coexpression platforms with the highest-level quality till date. Using various functionalities in COXPRESdb, the new coexpression data would support a broader area of research from molecular biology to medical sciences. |
format | Online Article Text |
id | pubmed-6324053 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-63240532019-01-10 COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference Obayashi, Takeshi Kagaya, Yuki Aoki, Yuichi Tadaka, Shu Kinoshita, Kengo Nucleic Acids Res Database Issue The advent of RNA-sequencing and microarray technologies has led to rapid growth of transcriptome data generated for a wide range of organisms, under various cellular, organ and individual conditions. Since the number of possible combinations of intercellular and extracellular conditions is almost unlimited, cataloging all transcriptome conditions would be an immeasurable challenge. Gene coexpression refers to the similarity of gene expression patterns under various conditions, such as disease states, tissue types, and developmental stages. Since the quality of gene coexpression data depends on the quality and quantity of transcriptome data, timely usage of the growing data is key to promoting individual research in molecular biology. COXPRESdb (http://coxpresdb.jp) is a database providing coexpression information for 11 animal species. One characteristic feature of COXPRESdb is its ability to compare multiple coexpression data derived from different transcriptomics technologies and different species, which strongly reduces false positive relationships in individual gene coexpression data. Here, we summarized the current version of this database, including 23 coexpression platforms with the highest-level quality till date. Using various functionalities in COXPRESdb, the new coexpression data would support a broader area of research from molecular biology to medical sciences. Oxford University Press 2019-01-08 2018-11-20 /pmc/articles/PMC6324053/ /pubmed/30462320 http://dx.doi.org/10.1093/nar/gky1155 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Database Issue Obayashi, Takeshi Kagaya, Yuki Aoki, Yuichi Tadaka, Shu Kinoshita, Kengo COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference |
title | COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference |
title_full | COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference |
title_fullStr | COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference |
title_full_unstemmed | COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference |
title_short | COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference |
title_sort | coxpresdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324053/ https://www.ncbi.nlm.nih.gov/pubmed/30462320 http://dx.doi.org/10.1093/nar/gky1155 |
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