Linked Life Data

15858974

Source:http://linkedlifedata.com/resource/pubmed/id/15858974

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2005-4-29
pubmed:abstractText
We present a novel scoring method for de novo interpretation of peptides from tandem mass spectrometry data. Our scoring method uses a probabilistic network whose structure reflects the chemical and physical rules that govern the peptide fragmentation. We use a likelihood ratio hypothesis test to determine whether the peaks observed in the mass spectrum are more likely to have been produced under our fragmentation model than under a model that treats peaks as random events. We tested our de novo algorithm PepNovo on ion trap data and achieved results that are superior to popular de novo peptide sequencing algorithms. PepNovo can be accessed via the URL http://www-cse.ucsd.edu/groups/bioinformatics/software.html.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0003-2700
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
77
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
964-73
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
PepNovo: de novo peptide sequencing via probabilistic network modeling.
pubmed:affiliation
Department of Computer Science & Engineering, University of California, San Diego, La Jolla, California 92093-0114, USA. arf@cs.ucsd.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural