Linked Life Data

17397212

Source:http://linkedlifedata.com/resource/pubmed/id/17397212

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2007-5-4
pubmed:abstractText
This paper described a simple heuristic method for determining the merit of a set of peptide sequence assignments made using tandem mass spectra. The method involved comparing a prediction based on the known stochastic behavior of a sequence assignment algorithm with the assignments generated from a particular data set. A particular formulation of this comparison was defined through the construction of a plot of the data, the rho-diagram, as well as a parameter derived from this plot, the rho-score. This plot and parameter were shown to be able to readily characterize the relative quality of a set of peptide sequence assignments and to allow the straightforward determination of probability threshold values for the interpretation of proteomics data. This plot is independent of the algorithm or scoring scheme used to estimate the statistical significance of a set of experimental results; rather, it can be used as an objective test of the correctness of those estimates. The rho-score can also be used as a parameter to evaluate the relative merit of protein identifications, such as those made across proteome species taxonomic categories.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1535-3893
pubmed:author
pubmed:issnType
Print
pubmed:volume
6
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1997-2004
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Determining the overall merit of protein identification data sets: rho-diagrams and rho-scores.
pubmed:affiliation
Rockefeller University, New York, New York 10019, University of California Davis, Davis, California 95616, USA.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural