Literature DB >> 26673825

A roadmap for the XCMS family of software solutions in metabolomics.

Nathaniel G Mahieu1, Jessica Lloyd Genenbacher1, Gary J Patti2.   

Abstract

Global profiling of metabolites in biological samples by liquid chromatography/mass spectrometry results in datasets too large to evaluate manually. Fortunately, a variety of software programs are now available to automate the data analysis. Selection of the appropriate processing solution is dependent upon experimental design. Most metabolomic studies a decade ago had a relatively simple experimental design in which the intensities of compounds were compared between only two sample groups. More recently, however, increasingly sophisticated applications have been pursued. Examples include comparing compound intensities between multiple sample groups and unbiasedly tracking the fate of specific isotopic labels. The latter types of applications have necessitated the development of new software programs, which have introduced additional functionalities that facilitate data analysis. The objective of this review is to provide an overview of the freely available bioinformatic solutions that are either based upon or are compatible with the algorithms in XCMS, which we broadly refer to here as the 'XCMS family' of software. These include CAMERA, credentialing, Warpgroup, metaXCMS, X(13)CMS, and XCMS Online. Together, these informatic technologies can accommodate most cutting-edge metabolomic applications and offer unique advantages when compared to the original XCMS program.
Copyright © 2015 Elsevier Ltd. All rights reserved.

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Year:  2015        PMID: 26673825      PMCID: PMC4831061          DOI: 10.1016/j.cbpa.2015.11.009

Source DB:  PubMed          Journal:  Curr Opin Chem Biol        ISSN: 1367-5931            Impact factor:   8.822


  43 in total

Review 1.  Defining the metabolome: size, flux, and regulation.

Authors:  Nicola Zamboni; Alan Saghatelian; Gary J Patti
Journal:  Mol Cell       Date:  2015-05-21       Impact factor: 17.970

2.  Meta-analysis of untargeted metabolomic data from multiple profiling experiments.

Authors:  Gary J Patti; Ralf Tautenhahn; Gary Siuzdak
Journal:  Nat Protoc       Date:  2012-02-16       Impact factor: 13.491

3.  metaXCMS: second-order analysis of untargeted metabolomics data.

Authors:  Ralf Tautenhahn; Gary J Patti; Ewa Kalisiak; Takashi Miyamoto; Manuela Schmidt; Fang Yin Lo; Joshua McBee; Nitin S Baliga; Gary Siuzdak
Journal:  Anal Chem       Date:  2010-12-21       Impact factor: 6.986

4.  Meta-analysis of global metabolomic data identifies metabolites associated with life-span extension.

Authors:  Gary J Patti; Ralf Tautenhahn; Darcy Johannsen; Ewa Kalisiak; Eric Ravussin; Jens C Brüning; Andrew Dillin; Gary Siuzdak
Journal:  Metabolomics       Date:  2014-08-01       Impact factor: 4.290

5.  An accelerated workflow for untargeted metabolomics using the METLIN database.

Authors:  Ralf Tautenhahn; Kevin Cho; Winnie Uritboonthai; Zhengjiang Zhu; Gary J Patti; Gary Siuzdak
Journal:  Nat Biotechnol       Date:  2012-09       Impact factor: 54.908

6.  Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database.

Authors:  Zheng-Jiang Zhu; Andrew W Schultz; Junhua Wang; Caroline H Johnson; Steven M Yannone; Gary J Patti; Gary Siuzdak
Journal:  Nat Protoc       Date:  2013-02-07       Impact factor: 13.491

7.  Processing methods for differential analysis of LC/MS profile data.

Authors:  Mikko Katajamaa; Matej Oresic
Journal:  BMC Bioinformatics       Date:  2005-07-18       Impact factor: 3.169

8.  MetaboAnalyst 3.0--making metabolomics more meaningful.

Authors:  Jianguo Xia; Igor V Sinelnikov; Beomsoo Han; David S Wishart
Journal:  Nucleic Acids Res       Date:  2015-04-20       Impact factor: 16.971

9.  Estimating relative changes of metabolic fluxes.

Authors:  Lei Huang; Dongsung Kim; Xiaojing Liu; Christopher R Myers; Jason W Locasale
Journal:  PLoS Comput Biol       Date:  2014-11-20       Impact factor: 4.475

10.  Highly sensitive feature detection for high resolution LC/MS.

Authors:  Ralf Tautenhahn; Christoph Böttcher; Steffen Neumann
Journal:  BMC Bioinformatics       Date:  2008-11-28       Impact factor: 3.169

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  34 in total

1.  Perspectives on Data Analysis in Metabolomics: Points of Agreement and Disagreement from the 2018 ASMS Fall Workshop.

Authors:  Erin S Baker; Gary J Patti
Journal:  J Am Soc Mass Spectrom       Date:  2019-08-22       Impact factor: 3.109

Review 2.  Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites.

Authors:  Brett C Covington; John A McLean; Brian O Bachmann
Journal:  Nat Prod Rep       Date:  2017-01-04       Impact factor: 13.423

Review 3.  Chemical Discovery in the Era of Metabolomics.

Authors:  Miriam Sindelar; Gary J Patti
Journal:  J Am Chem Soc       Date:  2020-05-11       Impact factor: 15.419

4.  A Protocol to Compare Methods for Untargeted Metabolomics.

Authors:  Lingjue Wang; Fuad J Naser; Jonathan L Spalding; Gary J Patti
Journal:  Methods Mol Biol       Date:  2019

5.  Hepatocyte-Macrophage Acetoacetate Shuttle Protects against Tissue Fibrosis.

Authors:  Patrycja Puchalska; Shannon E Martin; Xiaojing Huang; Justin E Lengfeld; Bence Daniel; Mark J Graham; Xianlin Han; Laszlo Nagy; Gary J Patti; Peter A Crawford
Journal:  Cell Metab       Date:  2018-11-15       Impact factor: 27.287

6.  Systems-level analysis of isotopic labeling in untargeted metabolomic data by X13CMS.

Authors:  Elizabeth M Llufrio; Kevin Cho; Gary J Patti
Journal:  Nat Protoc       Date:  2019-06-05       Impact factor: 13.491

7.  Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software.

Authors:  Cong-Hui Yao; Lingjue Wang; Ethan Stancliffe; Miriam Sindelar; Kevin Cho; Weitong Yin; Yahui Wang; Gary J Patti
Journal:  Anal Chem       Date:  2020-01-07       Impact factor: 6.986

Review 8.  Metabolite Measurement: Pitfalls to Avoid and Practices to Follow.

Authors:  Wenyun Lu; Xiaoyang Su; Matthias S Klein; Ian A Lewis; Oliver Fiehn; Joshua D Rabinowitz
Journal:  Annu Rev Biochem       Date:  2017-06-20       Impact factor: 23.643

9.  Autologous Exosome Transfer: A New Personalised Treatment Concept to Prevent Colitis in a Murine Model.

Authors:  Chunhua Yang; Mingzhen Zhang; Junsik Sung; Lixin Wang; Yunjin Jung; Didier Merlin
Journal:  J Crohns Colitis       Date:  2020-07-09       Impact factor: 9.071

10.  Trace Phosphate Improves ZIC-pHILIC Peak Shape, Sensitivity, and Coverage for Untargeted Metabolomics.

Authors:  Jonathan L Spalding; Fuad J Naser; Nathaniel G Mahieu; Stephen L Johnson; Gary J Patti
Journal:  J Proteome Res       Date:  2018-09-25       Impact factor: 4.466

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