Nucleic Acids Research Advance Access originally published online on December 7, 2006
Nucleic Acids Research 2007 35(1):e7; doi:10.1093/nar/gkl792
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Nucleic Acids Research, 2007, Vol. 35, No. 1 e7
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Methods Online |
Gene loss rate: a probabilistic measure for the conservation of eukaryotic genes
1 School of Computer Science, Tel-Aviv University Tel-Aviv 69978, Israel 2 School of Medicine, Tel-Aviv University Tel-Aviv 69978, Israel
*To whom correspondence should be addressed. Tel: +972 3 640 5378; Fax: +972 3 640 9357; Email: borens{at}post.tau.ac.il
Received July 23, 2006. Revised September 12, 2006. Accepted October 2, 2006.
The rate of conservation of a gene in evolution is believed to be correlated with its biological importance. Recent studies have devised various conservation measures for genes and have shown that they are correlated with several biological characteristics of functional importance. Specifically, the state-of-the-art propensity for gene loss (PGL) measure was shown to be strongly correlated with gene essentiality and its number of protein–protein interactions (PPIs). The observed correlation between conservation and functional importance varies however between conservation measures, underscoring the need for accurate and general measures for the rate of gene conservation. Here we develop a novel maximum-likelihood approach to computing the rate in which a gene is lost in evolution, motivated by the same principles as those underlying PGL. However, in difference to PGL which considers only the most parsimonious ancestral states of the internal nodes of the phylogenetic tree relating the species, our approach weighs in a probabilistic manner all possible ancestral states, and includes the branch length information as part of the probabilistic model. In application to data of 16 eukaryotic genomes, our approach shows higher correlations with experimental data than PGL, including data on gene lethality, level of connectivity in a PPI network and coherence within functionally related genes.
*Correspondence may also be addressed to Tomer Shlomi. Tel: +972 3 640 5378; Fax: +972 3 640 9357; Email: shlomito{at}post.tau.ac.il
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
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