By Cathy H. Wu, Chuming Chen
With the swift improvement of proteomic applied sciences within the existence sciences and in scientific purposes, many bioinformatics methodologies, databases, and software program instruments were built to help comparative proteomics research. In Bioinformatics for Comparative Proteomics, specialists within the box spotlight the present prestige, demanding situations, open difficulties, and destiny traits for constructing bioinformatics instruments and assets for comparative proteomics learn so as to convey a definitive reference offering either the breadth and intensity wanted at the topic. dependent in 3 significant sections, this distinctive quantity covers easy bioinformatics frameworks on the subject of comparative proteomics, bioinformatics databases and instruments for proteomics facts research, and built-in bioinformatics platforms and methods for learning comparative proteomics within the platforms biology context. Written for the hugely winning equipment in Molecular Biology™ sequence, the contributions during this e-book give you the meticulous, step by step description and implementation suggestion that's an important for buying optimum ends up in the lab. finished and easy-to-use, Bioinformatics for Comparative Proteomics serves all readers who desire to know about cutting-edge bioinformatics databases and instruments, novel computational tools and destiny developments in proteomics facts research, and comparative proteomics in structures biology.
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Extra info for Bioinformatics for Comparative Proteomics
76. , Cortens, J. , Beavis, R. C. (2006) Using annotated peptide mass spectrum libraries for protein identification. J. Proteome Res. 5, 1843–1849. 77. Deutsch, E. , Aebersold, R. (2008) PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434. 78. Slotta, D. , Edgar, R. (2009) NCBI peptidome: a new public repository for mass spectrometry peptide identifications. Nat. Biotechnol. 27, 600–601. Chapter 2 A Guide to UniProt for Protein Scientists Claire O’Donovan and Rolf Apweiler Abstract One of the essential requirements of the proteomics community is a high quality annotated nonredundant protein sequence database with stable identifiers and an archival service to enable protein identification and characterization.
Biotechnol. 25, 887–893. 64. Tatusov, R. , Fedorova, N. , Jackson, J. , Jacobs, A. , Koonin, E. , Krylov, D. , Mekhedov, S. , Nikolskaya, A. , Rao, B. , Sverdlov, A. , Wolf, Y. , Yin, J. , Natale, D. A. (2003) The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41–54. 65. , Linial, M. 0: a hierarchical classification of one million protein sequences. Nucleic Acids Res. 33, D216–D218. 66. , Anderson, J. , Derbyshire, M. , Fong, J. , Geer, L. , Geer, R. , Gonzales, N. , Hurwitz, D.
InterPro integrates protein signatures from 11 major signature databases (CATH-Gene3D, HAMAP, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY, and TIGRFAMs) into a single resource, taking advantage of the different areas of specialization of each to produce a resource that provides protein classification on multiple levels: protein families, structural superfamilies and functionally close subfamilies, as well as functional domains, repeats and important sites. By linking related signatures together, InterPro places them in a hierarchical classification scheme, reflecting their underlying evolutionary relationships.