Linked Life Data

16013882

Source:http://linkedlifedata.com/resource/pubmed/id/16013882

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
14
pubmed:dateCreated
2005-7-14
pubmed:abstractText
Reliable identification of posttranslational modifications is key to understanding various cellular regulatory processes. We describe a tool, InsPecT, to identify posttranslational modifications using tandem mass spectrometry data. InsPecT constructs database filters that proved to be very successful in genomics searches. Given an MS/MS spectrum S and a database D, a database filter selects a small fraction of database D that is guaranteed (with high probability) to contain a peptide that produced S. InsPecT uses peptide sequence tags as efficient filters that reduce the size of the database by a few orders of magnitude while retaining the correct peptide with very high probability. In addition to filtering, InsPecT also uses novel algorithms for scoring and validating in the presence of modifications, without explicit enumeration of all variants. InsPecT identifies modified peptides with better or equivalent accuracy than other database search tools while being 2 orders of magnitude faster than SEQUEST, and substantially faster than X!TANDEM on complex mixtures. The tool was used to identify a number of novel modifications in different data sets, including many phosphopeptides in data provided by Alliance for Cellular Signaling that were missed by other tools.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0003-2700
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
77
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4626-39
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
InsPecT: identification of posttranslationally modified peptides from tandem mass spectra.
pubmed:affiliation
Department of Bioengineering and Computer Science Department, APM 3832, University of California-San Diego, 9500 Gilman Drive, La Jolla, California 92093-0114, USA. stanner@ucsd.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural