CPVA: A web-based metabolomic tool for chromatographic peak visualization and annotation

Hemi Luan, Xingen Jiang, Fenfen Ji, Zhangzhang Lan, Zongwei CAI, Wenyong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Motivation: Liquid chromatography-mass spectrometry-based non-targeted metabolomics is routinely performed to qualitatively and quantitatively analyze a tremendous amount of metabolite signals in complex biological samples. However, false-positive peaks in the datasets are commonly detected as metabolite signals by using many popular software, resulting in non-reliable measurement. Results: To reduce false-positive calling, we developed an interactive web tool, termed CPVA, for visualization and accurate annotation of the detected peaks in non-targeted metabolomics data. We used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies.

Original languageEnglish
Pages (from-to)3913-3915
Number of pages3
JournalBioinformatics
Volume36
Issue number12
DOIs
Publication statusPublished - 31 Mar 2020

Scopus Subject Areas

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

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