InParanoid 6: eukaryotic ortholog clusters with inparalogs.

The InParanoid eukaryotic ortholog database (http://InParanoid.sbc.su.se/) has been updated to version 6 and is now based on 35 species. We collected all available 'complete' eukaryotic proteomes and Escherichia coli, and calculated ortholog groups for all 595 species pairs using the InParanoid program. This resulted in 2 642 187 pairwise ortholog groups in total. The orthology-based species relations are presented in an orthophylogram. InParanoid clusters contain one or more orthologs from each of the two species. Multiple orthologs in the same species, i.e. inparalogs, result from gene duplications after the species divergence. A new InParanoid website has been developed which is optimized for speed both for users and for updating the system. The XML output format has been improved for efficient processing of the InParanoid ortholog clusters.

Tertiary windowing to detect positive diversifying selection.

As a protein-encoding gene evolves, different selective pressures act on the gene temporally and spatially. An examination of the ratio of nonsynonymous-to-synonymous nucleotide substitution rate ratios (K(a)/K(s)) has proven to be a valuable method to examine selective pressures on protein encoding genes, including detecting positive diversifying selection. To gain power over averaging all sites in a gene together, examination of sites in primary sequence windows has frequently been employed. However, selection acts on folded proteins and sites that are close in tertiary space may not be close in primary sequence. A new method for the examination of K(a)/K(s) ratios based upon windows in tertiary structure is introduced and applied to the leptin gene family in mammals. Tertiary sequence windowing detects new sites under positive diversifying selection and detects positive diversifying selection with a more significant signal along various branches of the leptin gene family tree.

Myostatin rapid sequence evolution in ruminants predates domestication.

Myostatin (GDF-8) is a negative regulator of skeletal muscle development. This gene has previously been implicated in the double muscling phenotype in mice and cattle. A systematic analysis of myostatin sequence evolution in ruminants was performed in a phylogenetic context. The myostatin coding sequence was determined from duiker (Sylvicapra grimmia caffra), eland (Taurotragus derbianus), gaur (Bos gaurus), ibex (Capra ibex), impala (Aepyceros melampus rednilis), pronghorn (Antilocapra americana), and tahr (Hemitragus jemlahicus). Analysis of nonsynonymous to synonymous nucleotide substitution rate ratios (Ka/Ks) indicates that positive selection may have been operating on this gene during the time of divergence of Bovinae and Antilopinae, starting from approximately 23 million years ago, a period that appears to account for most of the sequence difference between myostatin in these groups. These periods of positive selective pressure on myostatin may correlate with changes in skeletal muscle mass during the same period.

Bayesian gene/species tree reconciliation and orthology analysis using MCMC.

MOTIVATION: Comparative genomics in general and orthology analysis in particular are becoming increasingly important parts of gene function prediction. Previously, orthology analysis and reconciliation has been performed only with respect to the parsimony model. This discards many plausible solutions and sometimes precludes finding the correct one. In many other areas in bioinformatics probabilistic models have proven to be both more realistic and powerful than parsimony models. For instance, they allow for assessing solution reliability and consideration of alternative solutions in a uniform way. There is also an added benefit in making model assumptions explicit and therefore making model comparisons possible. For orthology analysis, uncertainty has recently been addressed using parsimonious reconciliation combined with bootstrap techniques. However, until now no probabilistic methods have been available. RESULTS: We introduce a probabilistic gene evolution model based on a birth-death process in which a gene tree evolves 'inside' a species tree. Based on this model, we develop a tool with the capacity to perform practical orthology analysis, based on Fitch's original definition, and more generally for reconciling pairs of gene and species trees. Our gene evolution model is biologically sound (Nei et al., 1997) and intuitively attractive. We develop a Bayesian analysis based on MCMC which facilitates approximation of an a posteriori distribution for reconciliations. That is, we can find the most probable reconciliations and estimate the probability of any reconciliation, given the observed gene tree. This also gives a way to estimate the probability that a pair of genes are orthologs. The main algorithmic contribution presented here consists of an algorithm for computing the likelihood of a given reconciliation. To the best of our knowledge, this is the first successful introduction of this type of probabilistic methods, which flourish in phylogeny analysis, into reconciliation and orthology analysis. The MCMC algorithm has been implemented and, although not yet being in its final form, tests show that it performs very well on synthetic as well as biological data. Using standard correspondences, our results carry over to allele trees as well as biogeography.