التفاصيل البيبلوغرافية
| العنوان: |
Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification. |
| المؤلفون: |
Gautam, Anupam1,2,3 (AUTHOR), Bhowmik, Debaleena4,5 (AUTHOR), Basu, Sayantani6 (AUTHOR), Zeng, Wenhuan1,7 (AUTHOR), Lahiri, Abhishake5,8,9 (AUTHOR), Huson, Daniel H1,2,3 (AUTHOR), Paul, Sandip9 (AUTHOR) sandipp@jisiasr.org |
| المصدر: |
Briefings in Bioinformatics. Nov2023, Vol. 24 Issue 6, p1-9. 9p. |
| مصطلحات موضوعية: |
*INTERNET servers, *DATA integration, *BIOMARKERS, *ECOSYSTEM dynamics, *MICROBIAL communities, *MICROBIAL diversity, *METABOLOMICS |
| مستخلص: |
A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Academic Search Premier |
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