Proteomic parsimony through bipartite graph analysis improves accuracy and transparency.

By Bingwu Zhang, Matthew Chambers, David L. Tabb. Journal of Proteome Research, 2007

381 citations recorded by the source corpus.

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: http://www.mc.vanderbilt.edu/msrc/bioinformatics/.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/6cb565e5c9f467ec2663421b82e0d4e4f61bfbb2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Spectroscopy

Citations in the corpus

Spectral Probabilities and Generating Functions of Tandem Mass Spectra: a Strike Against Decoy Databases (2008, background): “…remark that many one-hit-wonders with SpectralProbability below 1.55 × 10 - 8 are actually more statistically signiﬁcant than some proteins with multiple peptide hits but larger SpectralProbability values (see refs 44, 48–50 for combining peptide signiﬁcance scores into protein signiﬁcance scores).”