| Autor: |
Kimmen Sjölander, Jyotsna Guleria, Donald J. Ferguson, Giovanni Gadda, John F. Hunt, Almaz Maksad, Maria Jesus Martin, Revonda M. Pokrzywa, Charles DeLisi, Linda Columbus, David Horn, John Tate, Dieter Söll, Rajeswari Swaminathan, Jeffrey H Miller, Lina L. Faller, Alexander F. Yakunin, Bernhard O. Palsson, Martin Steffen, Granger G. Sutton, Daniel Segrè, Kenneth E. Rudd, Krista Rochussen, Peter D. Karp, Mark G. McGettrick, Alexey Fomenkov, Han-Pil Choi, Ramana Madupu, Robert Blumenthal, Manuel Ferrer, Jim C. Spain, Claire O'Donovan, Russell Greiner, J. Martin Bollinger, Ami Levy-Moonshine, Richard J. Roberts, William Klimke, Shuang-yong Xu, Kevin R. Tao, Yi Chien Chang, Caitlin Monahan, Julien Gobeill, Germán Plata, Varun Mazumdar, Aaron T. Setterdahl, Dmitri Tchigvintsev, Genevieve Housman, Jie Hu, John Rachlin, Woo Suk Chang, Ashok S. Bhagwat, Michael Y. Galperin, Irina A. Rodionova, Zhenjun Hu, Lais Osmani, Carsten Krebs, Dennis Vitkup, Brian P. Anton, Daniel H. Haft, Iddo Friedberg, Simon Kasif, Steven E. Brenner, Steven L. Salzberg, Stanley Letovsky, Niels Klitgord, Dana Macelis, Alex Bateman, Richard D. Morgan, Peter Brown, Valérie de Crécy-Lagard, Andrei L. Osterman, Benjamin L. Allen, Dmitry A. Rodionov, Patrick Ruch |
| Přispěvatelé: |
National Institute of General Medical Sciences (US) |
| Jazyk: |
angličtina |
| Rok vydání: |
2014 |
| Předmět: |
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| Zdroj: |
PLoS Biology
Digital.CSIC. Repositorio Institucional del CSIC
instname
PLoS Biology, Vol 11, Iss 8, p e1001638 (2013) |
| Popis: |
© 2013 Brian P. et al.
Prior to the “genomic era,” when the acquisition of DNA sequence involved significant labor and expense, the sequencing of genes was strongly linked to the experimental characterization of their products. Sequencing at that time directly resulted from the need to understand an experimentally determined phenotype or biochemical activity. Now that DNA sequencing has become orders of magnitude faster and less expensive, focus has shifted to sequencing entire genomes. Since biochemistry and genetics have not, by and large, enjoyed the same improvement of scale, public sequence repositories now predominantly contain putative protein sequences for which there is no direct experimental evidence of function. Computational approaches attempt to leverage evidence associated with the ever-smaller fraction of experimentally analyzed proteins to predict function for these putative proteins. Maximizing our understanding of function over the universe of proteins in toto requires not only robust computational methods of inference but also a judicious allocation of experimental resources, focusing on proteins whose experimental characterization will maximize the number and accuracy of follow-on predictions.
COMBREX is funded by a GO grant from the National Institute of General Medical Sciences (NIGMS) (1RC2GM092602-01). |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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