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GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation
Gene co-expression network analysis has been widely used in gene function annotation, especially for long noncoding RNAs (lncRNAs). However, there is a lack of effective cross-platform analysis tools. For biologists to easily build a gene co-expression network and to predict gene function, we develo...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9164028/ https://www.ncbi.nlm.nih.gov/pubmed/35723358 http://dx.doi.org/10.3390/cimb44040100 |
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author | Chen, Wen Li, Jing Huang, Shulan Li, Xiaodeng Zhang, Xuan Hu, Xiang Xiang, Shuanglin Liu, Changning |
author_facet | Chen, Wen Li, Jing Huang, Shulan Li, Xiaodeng Zhang, Xuan Hu, Xiang Xiang, Shuanglin Liu, Changning |
author_sort | Chen, Wen |
collection | PubMed |
description | Gene co-expression network analysis has been widely used in gene function annotation, especially for long noncoding RNAs (lncRNAs). However, there is a lack of effective cross-platform analysis tools. For biologists to easily build a gene co-expression network and to predict gene function, we developed GCEN, a cross-platform command-line toolkit developed with C++. It is an efficient and easy-to-use solution that will allow everyone to perform gene co-expression network analysis without the requirement of sophisticated programming skills, especially in cases of RNA-Seq research and lncRNAs function annotation. Because of its modular design, GCEN can be easily integrated into other pipelines. |
format | Online Article Text |
id | pubmed-9164028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91640282022-06-04 GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation Chen, Wen Li, Jing Huang, Shulan Li, Xiaodeng Zhang, Xuan Hu, Xiang Xiang, Shuanglin Liu, Changning Curr Issues Mol Biol Article Gene co-expression network analysis has been widely used in gene function annotation, especially for long noncoding RNAs (lncRNAs). However, there is a lack of effective cross-platform analysis tools. For biologists to easily build a gene co-expression network and to predict gene function, we developed GCEN, a cross-platform command-line toolkit developed with C++. It is an efficient and easy-to-use solution that will allow everyone to perform gene co-expression network analysis without the requirement of sophisticated programming skills, especially in cases of RNA-Seq research and lncRNAs function annotation. Because of its modular design, GCEN can be easily integrated into other pipelines. MDPI 2022-03-25 /pmc/articles/PMC9164028/ /pubmed/35723358 http://dx.doi.org/10.3390/cimb44040100 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Wen Li, Jing Huang, Shulan Li, Xiaodeng Zhang, Xuan Hu, Xiang Xiang, Shuanglin Liu, Changning GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation |
title | GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation |
title_full | GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation |
title_fullStr | GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation |
title_full_unstemmed | GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation |
title_short | GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation |
title_sort | gcen: an easy-to-use toolkit for gene co-expression network analysis and lncrnas annotation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9164028/ https://www.ncbi.nlm.nih.gov/pubmed/35723358 http://dx.doi.org/10.3390/cimb44040100 |
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