File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Application of Meta-Mesh on the analysis of microbial communities from human associated-habitats

TitleApplication of Meta-Mesh on the analysis of microbial communities from human associated-habitats
Authors
Keywordsdata mining
metagenome
microbial community
Issue Date2015
Citation
Quantitative Biology, 2015, v. 3, n. 1, p. 4-18 How to Cite?
AbstractWith the current fast accumulation of microbial community samples and related metagenomic sequencing data, data integration and analysis system is urgently needed for in-depth analysis of large number of metagenomic samples (also referred to as “microbial communities”) of interest. Although several existing databases have collected a large number of metagenomic samples, they mostly serve as data repositories with crude annotations, and offer limited functionality for analysis. Moreover, the few available tools for comparative analysis in the literature could only support the comparison of a few pre-defined set of metagenomic samples. To facilitate comprehensive comparative analysis on large amount of diverse microbial community samples, we have designed a Meta-Mesh system for a variety of analyses including quantitative analysis of similarities among microbial communities and computation of the correlation between the meta-information of these samples. We have used Meta-Mesh for systematically and efficiently analyses on diverse sets of human associate-habitat microbial community samples. Results have shown that Meta-Mesh could serve well as an efficient data analysis platform for discovery of clusters, biomarker and other valuable biological information from a large pool of human microbial samples.
Persistent Identifierhttp://hdl.handle.net/10722/311416
ISSN
2023 Impact Factor: 0.6
2023 SCImago Journal Rankings: 0.631

 

DC FieldValueLanguage
dc.contributor.authorSu, Xiaoquan-
dc.contributor.authorWang, Xiaojun-
dc.contributor.authorJing, Gongchao-
dc.contributor.authorHuang, Shi-
dc.contributor.authorXu, Jian-
dc.contributor.authorNing, Kang-
dc.date.accessioned2022-03-22T11:53:53Z-
dc.date.available2022-03-22T11:53:53Z-
dc.date.issued2015-
dc.identifier.citationQuantitative Biology, 2015, v. 3, n. 1, p. 4-18-
dc.identifier.issn2095-4689-
dc.identifier.urihttp://hdl.handle.net/10722/311416-
dc.description.abstractWith the current fast accumulation of microbial community samples and related metagenomic sequencing data, data integration and analysis system is urgently needed for in-depth analysis of large number of metagenomic samples (also referred to as “microbial communities”) of interest. Although several existing databases have collected a large number of metagenomic samples, they mostly serve as data repositories with crude annotations, and offer limited functionality for analysis. Moreover, the few available tools for comparative analysis in the literature could only support the comparison of a few pre-defined set of metagenomic samples. To facilitate comprehensive comparative analysis on large amount of diverse microbial community samples, we have designed a Meta-Mesh system for a variety of analyses including quantitative analysis of similarities among microbial communities and computation of the correlation between the meta-information of these samples. We have used Meta-Mesh for systematically and efficiently analyses on diverse sets of human associate-habitat microbial community samples. Results have shown that Meta-Mesh could serve well as an efficient data analysis platform for discovery of clusters, biomarker and other valuable biological information from a large pool of human microbial samples.-
dc.languageeng-
dc.relation.ispartofQuantitative Biology-
dc.subjectdata mining-
dc.subjectmetagenome-
dc.subjectmicrobial community-
dc.titleApplication of Meta-Mesh on the analysis of microbial communities from human associated-habitats-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s40484-015-0040-3-
dc.identifier.scopuseid_2-s2.0-84994566520-
dc.identifier.volume3-
dc.identifier.issue1-
dc.identifier.spage4-
dc.identifier.epage18-
dc.identifier.eissn2095-4697-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats