Linked Life Data

21204564

Source:http://linkedlifedata.com/resource/pubmed/id/21204564

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2011-4-1
pubmed:abstractText
To interpret LC-MS/MS data in proteomics, most popular protein identification algorithms primarily use predicted fragment m/z values to assign peptide sequences to fragmentation spectra. The intensity information is often undervalued, because it is not as easy to predict and incorporate into algorithms. Nevertheless, the use of intensity to assist peptide identification is an attractive prospect and can potentially improve the confidence of matches and generate more identifications. On the basis of our previously reported study of fragmentation intensity patterns, we developed a protein identification algorithm, SeQuence IDentfication (SQID), that makes use of the coarse intensity from a statistical analysis. The scoring scheme was validated by comparing with Sequest and X!Tandem using three data sets, and the results indicate an improvement in the number of identified peptides, including unique peptides that are not identified by Sequest or X!Tandem. The software and source code are available under the GNU GPL license at http://quiz2.chem.arizona.edu/wysocki/bioinformatics.htm.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1535-3907
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
10
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1593-602
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
SQID: an intensity-incorporated protein identification algorithm for tandem mass spectrometry.
pubmed:affiliation
Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona 85721, United States.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural