Title |
MetaPhOrs: orthology and paralogy predictions from multiple phylogenetic evidence using a consistency-based confidence score
|
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Published in |
Nucleic Acids Research, December 2010
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DOI | 10.1093/nar/gkq953 |
Pubmed ID | |
Authors |
Leszek P. Pryszcz, Jaime Huerta-Cepas, Toni Gabaldón |
Abstract |
Reliable prediction of orthology is central to comparative genomics. Approaches based on phylogenetic analyses closely resemble the original definition of orthology and paralogy and are known to be highly accurate. However, the large computational cost associated to these analyses is a limiting factor that often prevents its use at genomic scales. Recently, several projects have addressed the reconstruction of large collections of high-quality phylogenetic trees from which orthology and paralogy relationships can be inferred. This provides us with the opportunity to infer the evolutionary relationships of genes from multiple, independent, phylogenetic trees. Using such strategy, we combine phylogenetic information derived from different databases, to predict orthology and paralogy relationships for 4.1 million proteins in 829 fully sequenced genomes. We show that the number of independent sources from which a prediction is made, as well as the level of consistency across predictions, can be used as reliable confidence scores. A webserver has been developed to easily access these data (http://orthology.phylomedb.org), which provides users with a global repository of phylogeny-based orthology and paralogy predictions. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Spain | 4 | 57% |
Ecuador | 1 | 14% |
Sweden | 1 | 14% |
Unknown | 1 | 14% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 71% |
Scientists | 2 | 29% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 6 | 4% |
United States | 4 | 2% |
Germany | 3 | 2% |
United Kingdom | 3 | 2% |
Brazil | 2 | 1% |
Sweden | 1 | <1% |
Mexico | 1 | <1% |
France | 1 | <1% |
Romania | 1 | <1% |
Other | 3 | 2% |
Unknown | 139 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 44 | 27% |
Researcher | 42 | 26% |
Student > Master | 21 | 13% |
Professor > Associate Professor | 10 | 6% |
Professor | 10 | 6% |
Other | 28 | 17% |
Unknown | 9 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 101 | 62% |
Biochemistry, Genetics and Molecular Biology | 25 | 15% |
Computer Science | 15 | 9% |
Medicine and Dentistry | 3 | 2% |
Environmental Science | 2 | 1% |
Other | 5 | 3% |
Unknown | 13 | 8% |