title: Computational analysis of shotgun proteomics data.
abstract: Proteomics technology is progressing at an incredible rate. The latest generation of tandem mass spectrometers can now acquire tens of thousands of fragmentation spectra in a matter of hours. Furthermore, quantitative proteomics methods have been developed that incorporate a stable isotope-labeled internal standard for every peptide within a complex protein mixture for the measurement of relative protein abundances. These developments have opened the doors for 'shotgun' proteomics, yet have also placed a burden on the computational approaches that manage the data. With each new method that is developed, the quantity of data that can be derived from a single experiment increases. To deal with this increase, new computational approaches are being developed to manage the data and assess false positives. This review discusses current approaches for analyzing proteomics data by mass spectrometry and identifies present computational limitations and bottlenecks.
authors.affiliation: Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, K307, Box 357730, Seattle, WA 98195-7730, USA. maccoss@gs.washington.edu
authors.forename: Michael J
authors.initials: MJ
authors.lastname: MacCoss
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019992:Databases as Topic; D013058:Mass Spectrometry / Q000379:methods; D010446:Peptide Fragments / Q000737:chemistry*; D040901:Proteomics; D012984:Software; D013997:Time Factors
chemical_list: D010446:Peptide Fragments