Journal
NATURE METHODS
Volume 10, Issue 3, Pages 221-227Publisher
NATURE PORTFOLIO
DOI: 10.1038/NMETH.2340
Keywords
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Categories
Funding
- US National Institutes of Health (NIH) [R13 HG006079-01A1]
- Office of Science (Biological and Environmental Research), US Department of Energy (DOE BER) [DE-SC0006807TDD]
- US National Science Foundation (NSF) [DBI-0644017, ABI-0965768, DMS0800568, CCF-0905536, DBI-1062455, DBI-0965768, ABI-1146960]
- Marie Curie International Outgoing Fellowship [PIOF-QA-2009-237751]
- PRIN Italian Ministry for University and Research MIUR [009WXT45Y]
- NIH [GM093123, GM075004, GM097528, GM079656, GM066099, LM00945102, RO1 GM071749, LM009722, HG004028]
- FP7 Infrastructures project TransPLANT Award [283496]
- UK Biotechnology and Biological Sciences Research Council (BBSRC) [BB/G022771/1, BB/K004131/1, BB/F020481/1]
- BBSRC
- Marie Curie Intra European Fellowship Award [PIEF-GA-2009-237292]
- Department of Information Technology, Government of India
- EU
- Natural Sciences and Engineering Research Council of Canada [298292-2009, 380478-2009]
- Canada Foundation for Innovation New Opportunities Award [10437]
- Ontario's Early Researcher Award [ER07-04-085]
- Netherlands Genomics Initiative
- National Information and Communication Technology Australia
- National Natural Science Foundation of China [31071113, 30971643]
- DOE [BER KP110201]
- Alexander von Humboldt Foundation
- NSF [CNS-0723054]
- BBSRC [BB/G022771/1, BB/F00964X/1, BB/K004131/1, BB/H02364X/1, BB/F020481/1, BB/J002925/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/J002925/1, BB/K004131/1, BB/H02364X/1, BB/F020481/1, BB/F00964X/1, BB/G022771/1] Funding Source: researchfish
- Direct For Biological Sciences
- Div Of Biological Infrastructure [0965768, 0965616] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1062455] Funding Source: National Science Foundation
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Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first Large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.
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