Linked Life Data

17165823

Source:http://linkedlifedata.com/resource/pubmed/id/17165823

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
24
pubmed:dateCreated
2006-12-14
pubmed:abstractText
New software tools for quantitative analysis of mass spectrometric lipidome data have been developed. The LIMSA tool finds and integrates peaks in a mass spectrum, matches the peaks with a user-supplied list of expected lipids, corrects for overlap in their isotopic patterns, and quantifies the identified lipid species according to internal standards. Three different algorithms for isotopic correction (deconvolution) were implemented and compared. LIMSA has a convenient user interface and can be applied on any type of MS spectrum. Typically, analysis of one spectrum takes only a few seconds. The SECD tool, designed for analysis of LC-MS data sets, provides an intuitive and informative display of MS chromatograms as two-dimensional "maps" for visual inspection of the data and allows the user to extract mass spectra, to be further analyzed with LIMSA, from arbitrary regions of these maps. More reliable analysis of complex lipidome data with improved signal-to-noise ratio is obtained when compared to standard time-range averaged spectra. The functionality of these tools is demonstrated by analysis of standard mixtures as well as complex biological samples. The tools described here make accurate, high-throughput analysis of extensive sample sets feasible and are made available to the scientific community free of charge.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0003-2700
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
78
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
8324-31
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Software tools for analysis of mass spectrometric lipidome data.
pubmed:affiliation
Institute of Biomedicine, Department of Biochemistry, University of Helsinki, Haartmaninkatu 8, PL 8, 00014 Helsinki, Finland.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't