title: Current trends in computational inference from mass spectrometry-based proteomics.
abstract: Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.
authors.affiliation: Department of Computational Biology & Bioinformatics, Pacific Northwest National Laboratory, P.O. BOX 999, Richland, WA 99352, USA. bj@pnl.gov
authors.forename: Bobbie-Jo M
authors.initials: BJ
authors.lastname: Webb-Robertson
authors.forename: William R
authors.initials: WR
authors.lastname: Cannon
mesh_terms: D019295:Computational Biology / Q000639:trends*; D005544:Forecasting; D020869:Gene Expression Profiling / Q000639:trends; D013058:Mass Spectrometry / Q000639:trends*; D010449:Peptide Mapping / Q000639:trends*; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism*; D040901:Proteomics / Q000639:trends*; D020539:Sequence Analysis, Protein / Q000639:trends
chemical_list: D020543:Proteome