Work

IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets

Year: 2022

Type: article

Abstract: Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies ( n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package ( https://ipa.idsl.me ), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential 12 C and 13 C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m / z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA .

Source: Journal of Proteome Research

DOI: 10.1021/acs.jproteome.2c00120

Authors: Sadjad Fakouri Baygi, Yashwant Kumar, Dinesh Kumar Barupal

Institutions: Icahn School of Medicine at Mount Sinai, Translational Health Science and Technology Institute

Language: English

Topic: Metabolomics and Mass Spectrometry Studies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of Environmental Health Sciences, National Center for Advancing Translational Sciences