Evidence map›Paper›PMID 29494574›Full record

ArticleNature protocols2018

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online.

Erica M Forsberg, Tao Huan, Duane Rinehart, H Paul Benton, Benedikt Warth, Brian Hilmers, Gary Siuzdak

Abstract read
In one paragraph

Article in Nature protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers.

0numbers the graph read from it
0cells of the map it votes in
131citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

131 citing papers in PubMed.

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71 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Erica M ForsbergCenter for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California, USA.
Tao HuanCenter for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California, USA.
Duane RinehartCenter for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California, USA.
H Paul BentonCenter for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California, USA.
Benedikt WarthCenter for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California, USA.
Brian HilmersCenter for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California, USA.
Gary SiuzdakCenter for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California, USA.ORCID 0000-0002-4749-0014

Funding

Pharmacokinetics and Drug-Drug InteractionsP01DA028555 · NIDA · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI ALNOUTI, YAZEN · 2010 to 2019
$16.8M
XCMS Metabolomic Data TechnologyR01GM114368 · NIGMS · SCRIPPS RESEARCH INSTITUTE, THE · PI SIUZDAK, GARY E · 2015 to 2018
$1.5M
NIDA NIH HHS P01 DA028555NIGMS NIH HHS R01 GM114368
6 · The paper itself

Abstract

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes ∼30 min.

Indexed as

MetabolismComputational BiologyElectronic Data ProcessingInternetMetabolomicsSoftwareSystems Biology

Identifiers

PMID29494574
PMCPMC5937130

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.