Literature DB >> 32603093

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies: MeTaQuaC.

Mathias Kuhring1,2, Alina Eisenberger1,2, Vanessa Schmidt2, Nicolle Kränkel3,4, David M Leistner1,3,4, Jennifer Kirwan1,2, Dieter Beule1,2,3.   

Abstract

Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data are essential for confidence in analytical results and are particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory's routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire data set alongside standard analyses of quality control samples can be used to identify outliers and quantify systematic trends to improve downstream analysis. Finally, we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and other target data sets and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.

Year:  2020        PMID: 32603093     DOI: 10.1021/acs.analchem.0c00136

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  7 in total

Review 1.  Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives.

Authors:  Fatemeh Dehghani; Saeed Yousefinejad; Douglas I Walker; Fariborz Omidi
Journal:  Metabolomics       Date:  2022-09-09       Impact factor: 4.747

Review 2.  New software tools, databases, and resources in metabolomics: updates from 2020.

Authors:  Biswapriya B Misra
Journal:  Metabolomics       Date:  2021-05-11       Impact factor: 4.290

Review 3.  Synthetic gut microbiome: Advances and challenges.

Authors:  Humphrey A Mabwi; Eunjung Kim; Dae-Geun Song; Hyo Shin Yoon; Cheol-Ho Pan; Erick V G Komba; GwangPyo Ko; Kwang Hyun Cha
Journal:  Comput Struct Biotechnol J       Date:  2020-12-24       Impact factor: 7.271

4.  MatrixQCvis: shiny-based interactive data quality exploration for omics data.

Authors:  Thomas Naake; Wolfgang Huber
Journal:  Bioinformatics       Date:  2021-11-12       Impact factor: 6.937

5.  Analysis of adherent cell culture lysates with low metabolite concentrations using the Biocrates AbsoluteIDQ p400 HR kit.

Authors:  Raphaela Fritsche-Guenther; Yoann Gloaguen; Alina Eisenberger; Jennifer A Kirwan
Journal:  Sci Rep       Date:  2022-05-13       Impact factor: 4.379

6.  Ten quick tips for biomarker discovery and validation analyses using machine learning.

Authors:  Ramon Diaz-Uriarte; Elisa Gómez de Lope; Rosalba Giugno; Holger Fröhlich; Petr V Nazarov; Isabel A Nepomuceno-Chamorro; Armin Rauschenberger; Enrico Glaab
Journal:  PLoS Comput Biol       Date:  2022-08-11       Impact factor: 4.779

Review 7.  Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches.

Authors:  Li Chen; Fanyi Zhong; Jiangjiang Zhu
Journal:  Metabolites       Date:  2020-08-27
  7 in total

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