| Literature DB >> 32807955 |
Alan K Jarmusch1,2, Mingxun Wang1,2, Christine M Aceves1,2, Rohit S Advani1,2, Shaden Aguirre1,2, Alexander A Aksenov1,2, Gajender Aleti3, Allegra T Aron1,2, Anelize Bauermeister1,4, Sanjana Bolleddu1,2, Amina Bouslimani1,2, Andres Mauricio Caraballo Rodriguez1,2, Rama Chaar1,2, Roxana Coras5, Emmanuel O Elijah1,2, Madeleine Ernst1,2,6, Julia M Gauglitz1,2, Emily C Gentry1,2, Makhai Husband1,2, Scott A Jarmusch7, Kenneth L Jones1,2, Zdenek Kamenik8, Audrey Le Gouellec9, Aileen Lu1,2, Laura-Isobel McCall10, Kerry L McPhail11, Michael J Meehan1,2, Alexey V Melnik1,2, Riya C Menezes12, Yessica Alejandra Montoya Giraldo13, Ngoc Hung Nguyen1,2, Louis Felix Nothias1,2, Mélissa Nothias-Esposito1,2, Morgan Panitchpakdi1,2, Daniel Petras1,2,14, Robert A Quinn15, Nicole Sikora1,2, Justin J J van der Hooft1,16, Fernando Vargas1,2,17, Alison Vrbanac18, Kelly C Weldon1,2,19, Rob Knight18,19,20,21, Nuno Bandeira2,19,20, Pieter C Dorrestein22,23,24,25.
Abstract
We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.Entities:
Mesh：
Year: 2020 PMID： 32807955 PMCID： PMC7968862 DOI： 10.1038/s41592-020-0916-7
Source DB: PubMed Journal: Nat Methods ISSN： 1548-7091 Impact factor: 28.547