Linked Life Data

16448051

Source:http://linkedlifedata.com/resource/pubmed/id/16448051

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2006-2-1
pubmed:abstractText
Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0003-2700
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
78
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
779-87
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.
pubmed:affiliation
Scripps Center for Mass Spectrometry and Department of Molecular Biology, Scripps Research Institute, La Jolla, California 92037, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural