title: PepSeeker: mining information from proteomic data.
abstract: Driven by advances in mass spectrometry and analytical chemistry, coupled with the expanding number of completely sequenced genomes, proteomics is becoming a widely exploited technology for characterizing the proteins found in living systems. As proteomics becomes increasingly more high-throughput there is a parallel need for storage of the large quantities of data generated, to support data exchange and allow further analyses. The capture and storage of such data, along with subsequent release and dissemination, not only aid in sharing of the data throughout the proteomics community but also provide scientific insights into the observations between different laboratories, instruments, and software. Growing numbers of resources offer a range of approaches for the capture, storage, and dissemination of proteomic experimental data reflecting the fact that proteomics has now come of age in the postgenomic era and is delivering large, complex datasets that are rich in information. This chapter demonstrates how one such resource, PepSeeker, can be used to mine useful information from proteomic data, which can then be exploited for peptide identification algorithms via a better understanding of how peptides fragment inside mass spectrometers.
authors.affiliation: Faculty of Life Sciences, The University of Manchester, Manchester, UK, Michael Smith Building.
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Siepen
authors.forename: Julian N
authors.initials: JN
authors.lastname: Selley
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Hubbard
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods; D010455:Peptides / Q000032:analysis / Q000235:genetics; D040901:Proteomics; D014584:User-Computer Interface
chemical_list: D010455:Peptides