arrow

From NeuroLex

Jump to: navigation, search



Resource:Percolator: Semi-supervised learning for peptide identification from shotgun proteomics datasets

Name: Resource:Percolator: Semi-supervised learning for peptide identification from shotgun proteomics datasets
Description: Percolator post-processes the results of a shotgun proteomics database search program, re-ranking peptide-spectrum matches so that the top of the list is enriched for correct matches. Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic dataset and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.


The yeast-01 data is available in tab delimetered format. The SEQUEST parameter file and target database for the yeast and worm data are also available.
Other Name(s): Percolator
Parent Organization: University of Washington; Washington; USA
Resource Type(s): Software resource, Downloadable database
Resource: Resource
URL: http://noble.gs.washington.edu/proj/percolator/
Id: nlx_98814
PMID: PMID 17952086
Keywords: worm, yeast
Link to OWL / RDF: Download this content as OWL/RDF

Curation status: Uncurated

This resource will be curated within 7 days.

For Resource Owners:
After the resource is curated, you may create a sitemap, which will help keep your registry description up-to-date and inform search engines about your resource.

Note: For a new resource, the website's URL must first be verified by a NIF curator before you may proceed.

Learn more about what NIF can do for your resource.
Proudly proclaim your inclusion in NIF by displaying the "Registered with NIF" button on your site. Please login to create the sitemap. (top right)

Contributors

Aarnaud, Ccdbuser, Zaidaziz



bookmark
Facts about Resource:Percolator: Semi-supervised learning for peptide identification from shotgun proteomics datasetsRDF feed
CurationStatuscurated  +
DefiningCitationhttp://noble.gs.washington.edu/proj/percolator/  +
DefinitionPercolator post-processes the results of a Percolator post-processes the results of a shotgun proteomics database search program, re-ranking peptide-spectrum matches so that the top of the list is enriched for correct matches. Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic dataset and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.


The yeast-01 data is available in tab delimetered format. The SEQUEST parameter file and target database for the yeast and worm data are also available.
he yeast and worm data are also available.
Has default formThis property is a special property in this wiki.Resource  +
Has roleSoftware resource  +, and Downloadable database  +
Idnlx_98814  +
Is part ofUniversity of Washington; Washington; USA  +
KeywordsWorm  +, and Yeast  +
LabelResource:Percolator: Semi-supervised learning for peptide identification from shotgun proteomics datasets  +
ModifiedDate9 April 2011  +
PMID17952086  +
SuperCategoryResource  +
SynonymPercolator  +