MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.
MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.
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DOI:
10.1093/nar/gkab382
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发表时间:
2021-07-02
影响因子:
14.9
通讯作者:
Xia J
中科院分区:
文献类型:
--
作者:
Pang Z;Chong J;Zhou G;de Lima Morais DA;Chang L;Barrette M;Gauthier C;Jacques PÉ;Li S;Xia J
Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC–MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca. From raw data to statistical and functional insights using MetaboAnalyst 5.0.
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影响因子:
14.9
作者:
Kanehisa M;Furumichi M;Sato Y;Ishiguro-Watanabe M;Tanabe M
通讯作者:
Tanabe M
影响因子:
--
作者:
Kuo TC;Tian TF;Tseng YJ
通讯作者:
Tseng YJ
DOI:
10.1007/978-1-0716-0239-3_3
发表时间:
2020
期刊:
Methods in molecular biology (Clifton, N.J.)
影响因子:
--
作者:
Du X;Smirnov A;Pluskal T;Jia W;Sumner S
通讯作者:
Sumner S
影响因子:
4.1
作者:
Chong J;Xia J
通讯作者:
Xia J
影响因子:
4.1
作者:
Chong, Jasmine;Yamamoto, Mai;Xia, Jianguo
通讯作者:
Xia, Jianguo