A Python-Based Pipeline for Preprocessing LC-MS Data for Untargeted Metabolomics Workflows.

ABSTRACT: Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography-mass spectrometry (LC-MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC-MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC-MS measurements, and it allows preprocessing m

SUBMITTER: Riquelme G 

PROVIDER: S-EPMC7602939 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature