Send to

Choose Destination
J Proteome Res. 2007 Sep;6(9):3549-57. Epub 2007 Aug 4.

Proteomic parsimony through bipartite graph analysis improves accuracy and transparency.

Author information

Department of Biomedical Informatics, Mass Spectrometry Research Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-8575, USA.


Assembling peptides identified from LC-MS/MS spectra into a list of proteins is a critical step in analyzing shotgun proteomics data. As one peptide sequence can be mapped to multiple proteins in a database, naïve protein assembly can substantially overstate the number of proteins found in samples. We model the peptide-protein relationships in a bipartite graph and use efficient graph algorithms to identify protein clusters with shared peptides and to derive the minimal list of proteins. We test the effects of this parsimony analysis approach using MS/MS data sets generated from a defined human protein mixture, a yeast whole cell extract, and a human serum proteome after MARS column depletion. The results demonstrate that the bipartite parsimony technique not only simplifies protein lists but also improves the accuracy of protein identification. We use bipartite graphs for the visualization of the protein assembly results to render the parsimony analysis process transparent to users. Our approach also groups functionally related proteins together and improves the comprehensibility of the results. We have implemented the tool in the IDPicker package. The source code and binaries for this protein assembly pipeline are available under Mozilla Public License at the following URL:

[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for American Chemical Society Icon for PubMed Central
Loading ...
Support Center