MetID: an R package for automatable compound annotation for LC-MS-based data

Xiaotao Shen, Si Wu, Liang Liang, Songjie Chen, Kévin Contrepois, Zheng Jiang Zhu, Michael Snyder

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Summary: Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials.

Original languageEnglish
Pages (from-to)568-569
Number of pages2
JournalBioinformatics
Volume38
Issue number2
DOIs
StatePublished - 15 Jan 2022
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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