Title |
Synergistic use of plant-prokaryote comparative genomics for functional annotations
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Published in |
BMC Genomics, June 2011
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DOI | 10.1186/1471-2164-12-s1-s2 |
Pubmed ID | |
Authors |
Svetlana Gerdes, Basma El Yacoubi, Marc Bailly, Ian K Blaby, Crysten E Blaby-Haas, Linda Jeanguenin, Aurora Lara-Núñez, Anne Pribat, Jeffrey C Waller, Andreas Wilke, Ross Overbeek, Andrew D Hanson, Valérie de Crécy-Lagard |
Abstract |
Identifying functions for all gene products in all sequenced organisms is a central challenge of the post-genomic era. However, at least 30-50% of the proteins encoded by any given genome are of unknown or vaguely known function, and a large number are wrongly annotated. Many of these 'unknown' proteins are common to prokaryotes and plants. We set out to predict and experimentally test the functions of such proteins. Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants. This approach bridges the gap between automated homology-based annotations and the classical gene discovery efforts of experimentalists, and is more powerful than purely computational approaches to identifying gene-function associations. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 2% |
Mexico | 1 | 2% |
Belgium | 1 | 2% |
Romania | 1 | 2% |
Spain | 1 | 2% |
United States | 1 | 2% |
Unknown | 59 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 16 | 25% |
Student > Ph. D. Student | 14 | 22% |
Professor > Associate Professor | 9 | 14% |
Student > Bachelor | 5 | 8% |
Student > Postgraduate | 5 | 8% |
Other | 12 | 18% |
Unknown | 4 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 33 | 51% |
Biochemistry, Genetics and Molecular Biology | 13 | 20% |
Computer Science | 3 | 5% |
Engineering | 3 | 5% |
Medicine and Dentistry | 3 | 5% |
Other | 4 | 6% |
Unknown | 6 | 9% |