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Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks
BACKGROUND: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functiona...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685597/ https://www.ncbi.nlm.nih.gov/pubmed/38030973 http://dx.doi.org/10.1186/s12859-023-05574-9 |
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author | Li, Dong-Xu Zhou, Peng Zhao, Bo-Wei Su, Xiao-Rui Li, Guo-Dong Zhang, Jun Hu, Peng-Wei Hu, Lun |
author_facet | Li, Dong-Xu Zhou, Peng Zhao, Bo-Wei Su, Xiao-Rui Li, Guo-Dong Zhang, Jun Hu, Peng-Wei Hu, Lun |
author_sort | Li, Dong-Xu |
collection | PubMed |
description | BACKGROUND: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters. RESULTS: Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment. CONCLUSIONS: BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/. |
format | Online Article Text |
id | pubmed-10685597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106855972023-11-30 Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks Li, Dong-Xu Zhou, Peng Zhao, Bo-Wei Su, Xiao-Rui Li, Guo-Dong Zhang, Jun Hu, Peng-Wei Hu, Lun BMC Bioinformatics Software BACKGROUND: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters. RESULTS: Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment. CONCLUSIONS: BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/. BioMed Central 2023-11-29 /pmc/articles/PMC10685597/ /pubmed/38030973 http://dx.doi.org/10.1186/s12859-023-05574-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Li, Dong-Xu Zhou, Peng Zhao, Bo-Wei Su, Xiao-Rui Li, Guo-Dong Zhang, Jun Hu, Peng-Wei Hu, Lun Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks |
title | Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks |
title_full | Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks |
title_fullStr | Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks |
title_full_unstemmed | Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks |
title_short | Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks |
title_sort | biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685597/ https://www.ncbi.nlm.nih.gov/pubmed/38030973 http://dx.doi.org/10.1186/s12859-023-05574-9 |
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