For the past 20 years, the Clusters of Orthologous Genes (COG) database had been a popular tool for microbial genome annotation and comparative genomics. Initially created for the purpose of evolutionary classification of protein families, the COG have been used, apart from straightforward functional annotation of sequenced genomes, for such tasks as (i) unification of genome annotation in groups of related organisms; (ii) identification of missing and/or undetected genes in complete microbial genomes; (iii) analysis of genomic neighborhoods, in many cases allowing prediction of novel functional systems; (iv) analysis of metabolic pathways and prediction of alternative forms of enzymes; (v) comparison of organisms by COG functional categories; and (vi) prioritization of targets for structural and functional characterization. Here we review the principles of the COG approach and discuss its key advantages and drawbacks in microbial genome analysis.

BACKGROUND: Viruses are the most abundant and genetically diverse biological entities on earth, yet the repertoire of viral proteins remains poorly explored. As the number of sequenced virus genomes grows into the thousands, and the number of viral proteins into the hundreds of thousands, we report a systematic computational analysis of the point of first-contact between viruses and their hosts, namely viral transmembrane (TM) proteins. RESULTS: The complement of α-helical TM proteins in double-stranded DNA viruses infecting bacteria and archaea reveals large-scale trends that differ from those of their hosts. Viruses typically encode a substantially lower fraction of TM proteins than archaea or bacteria, with the notable exception of viruses with virions containing a lipid component such as a lipid envelope, internal lipid core, or inner membrane vesicle. Compared to bacteriophages, archaeal viruses are substantially enriched in membrane proteins. However, this feature is not always stable throughout the evolution of a viral lineage; for example, TM proteins are not part of the common heritage shared between Lipothrixviridae and Rudiviridae. In contrast to bacteria and archaea, viruses almost completely lack proteins with complicated membrane topologies composed of more than 4 TM segments, with the few detected exceptions being obvious cases of relatively recent horizontal transfer from the host. CONCLUSIONS: The dramatic differences between the membrane proteomes of cells and viruses stem from the fact that viruses do not depend on essential membranes for energy transformation, ion homeostasis, nutrient transport and signaling.

We compare the sets of experimentally validated long intergenic non-coding (linc)RNAs from human and mouse and apply a maximum likelihood approach to estimate the total number of lincRNA genes as well as the size of the conserved part of the lincRNome. Under the assumption that the sets of experimentally validated lincRNAs are random samples of the lincRNomes of the corresponding species, we estimate the total lincRNome size at approximately 40,000 to 50,000 species, at least twice the number of protein-coding genes. We further estimate that the fraction of the human and mouse euchromatic genomes encoding lincRNAs is more than twofold greater than the fraction of protein-coding sequences. Although the sequences of most lincRNAs are much less strongly conserved than protein sequences, the extent of orthology between the lincRNomes is unexpectedly high, with 60 to 70% of the lincRNA genes shared between human and mouse. The orthologous mammalian lincRNAs can be predicted to perform equivalent functions; accordingly, it appears likely that thousands of evolutionarily conserved functional roles of lincRNAs remain to be characterized.

The serotonin 2C receptor (5-HT(2C)R)-a key regulator of diverse neurological processes-exhibits functional variability derived from editing of its pre-mRNA by site-specific adenosine deamination (A-to-I pre-mRNA editing) in five distinct sites. Here we describe a statistical technique that was developed for analysis of the dependencies among the editing states of the five sites. The statistical significance of the observed correlations was estimated by comparing editing patterns in multiple individuals. For both human and rat 5-HT(2C)R, the editing states of the physically proximal sites A and B were found to be strongly dependent. In contrast, the editing states of sites C and D, which are also physically close, seem not to be directly dependent but instead are linked through the dependencies on sites A and B, respectively. We observed pronounced differences between the editing patterns in humans and rats: in humans site A is the key determinant of the editing state of the other sites, whereas in rats this role belongs to site B. The structure of the dependencies among the editing sites is notably simpler in rats than it is in humans implying more complex regulation of 5-HT(2C)R editing and, by inference, function in the human brain. Thus, exhaustive statistical analysis of the 5-HT(2C)R editing patterns indicates that the editing state of sites A and B is the primary determinant of the editing states of the other three sites, and hence the overall editing pattern. Taken together, these findings allow us to propose a mechanistic model of concerted action of ADAR1 and ADAR2 in 5-HT(2C)R editing. Statistical approach developed here can be applied to other cases of interdependencies among modification sites in RNA and proteins.

A fundamental observation of comparative genomics is that the distribution of evolution rates across the complete sets of orthologous genes in pairs of related genomes remains virtually unchanged throughout the evolution of life, from bacteria to mammals. The most straightforward explanation for the conservation of this distribution appears to be that the relative evolution rates of all genes remain nearly constant, or in other words, that evolutionary rates of different genes are strongly correlated within each evolving genome. This correlation could be explained by a model that we denoted Universal PaceMaker (UPM) of genome evolution. The UPM model posits that the rate of evolution changes synchronously across genome-wide sets of genes in all evolving lineages. Alternatively, however, the correlation between the evolutionary rates of genes could be a simple consequence of molecular clock (MC). We sought to differentiate between the MC and UPM models by fitting thousands of phylogenetic trees for bacterial and archaeal genes to supertrees that reflect the dominant trend of vertical descent in the evolution of archaea and bacteria and that were constrained according to the two models. The goodness of fit for the UPM model was better than the fit for the MC model, with overwhelming statistical significance, although similarly to the MC, the UPM is strongly overdispersed. Thus, the results of this analysis reveal a universal, genome-wide pacemaker of evolution that could have been in operation throughout the history of life.

Walter M. Fitch, a towering giant of molecular evolution, passed away on 10 March 2011. His pivotal contributions to the field are manifold, diverse and profound. He developed some of the first algorithms and practical methods for phylogenetic tree construction; pioneered statistical approaches in sequence comparison and phylogenetic analysis; introduced the covarion approach in the study of gene evolution, a simple but powerful concept the full potential of which is not yet realized; and in the final years of his long and enormously productive career, laid the foundation of viral phylogenetics that has become essential for the most practical purpose of predicting the dominant influenza strain in the next epidemics. Fitch's many fundamental achievements have been succinctly but clearly outlined in a recent Science Retrospective [1]. For the special issue of Briefings in Bioinformatics on orthology and orthologs, it seems most appropriate to focus on a single paper of Fitch in which he introduced the concepts and definitions of orthology and paralogy. Appearing in 1970 in a well-respected but relatively low key journal Systematic Zoology, under the unassuming (at least, at the time) title ‘Distinguishing homologous from analogous proteins’ [2], this paper hardly attracted much attention immediately upon its publication and was quite poorly cited over the next 25 years. This has changed when comparison of gene sets from completely sequenced genomes became the core of comparative and evolutionary genomics (Figure 1). All of a sudden, it became apparent that the straightforward classification of homologs introduced by Fitch in that article was absolutely essential for genomics to make any sense of the pouring genomes, so the citations have been accumulating at a good pace ever since (Figure 1). I would go as far as to submit that Fitch's paper is the single most important conceptual cornerstone of modern genomics. Indeed, distinguishing orthologs from paralogs is critical for at least three key tasks [3]: Between these and related tasks, analysis and classification of homologous relationships among genes is at the center of almost every comparative genomic study. Not only is discrimination of orthologs and paralogs vitally important, it is also decidedly nontrivial both at the fundamental and at the technical level as evidenced by the multitude of developed methods and resources for this purpose many of which are described in the articles of this special issue of Briefings in Bioinformatics. Moreover, even the conceptual foundations of the orthology paradigm remain open to debate: a recent bioinformatics study has challenged the ‘ortholog conjecture’, namely the common (and crucial to genome annotation) assumption that orthologs are generally more functionally similar than paralogs [4]. So orthology and paralogy are not fixed textbook definitions but evolving concepts, and this is the typical fate of Walter Fitch's ideas and research: he usually addressed only deep problems that remain active research subjects for many decades if not indefinitely. reconstruction of genome evolution including genes losses, horizontal gene transfer and lineage-specific duplication; study of major aspects of the evolutionary process such as the distribution of selection pressure across genes; and transfer of functional information from functionally characterized genes to uncharacterized homologs from other organisms which is the basis of genome annotation. Cumulative dynamics of the citation of Fitch's 1970 article. The citation data were from the ISI Web of Science (http://apps.isiknowledge.com). Interestingly, Fitch's 1970 article was not dedicated to the classification of homologous relationships. As follows from the title, the bulk of the paper is about distinguishing homology from analogy, or more precisely, estimating the likelihood of a given level of sequence similarity between proteins emerging due to convergence (another subject that remains pertinent to this day). Only in the final paragraph of the discussion, Fitch notes: ‘It is not sufficient, for example, when reconstructing a phylogeny from amino acid sequences that the proteins be homologous. … there should be two subclasses of homology. Where the homology is the result of gene duplication so that both copies have descended side by side during the history of an organism (for example, a and b hemoglobin) the genes should be called paralogous (para = in parallel). Where the homology is the result of speciation so that the history of the gene reflects the history of the species (for example a hemoglobin in man and mouse) the genes should be called orthologous (ortho = exact). Phylogenies require orthologous, not paralogous, genes’ [2]. I hope readers appreciate the crystal clarity of this description given in the days when the idea of comparing complete genomes was still alien to biologists. For the 30th anniversary of his landmark paper, Fitch revisited the subject and published an equally lucid and succinct discussion of various aspects of homology that arguably remains the best reading on this subject [5]. Fitch's 1970 article has duly become a citation classic, but not a record-breaking one: according to ISI Web of Science, by the end of 2010, it has been cited 554 times (Figure 1), a highly respectable but by no account astonishing number. This could seem surprising given the obvious importance of Fitch's concept but another little scientometric exercise provides a clue. According to PubMed, the term ‘ortholog’ alone (not counting ‘orthologous’ or ‘orthology’) has been used in the title or abstract of 4947 publications (Figure 2). Obviously, almost 90% of articles in PubMed that use the term do not cite Fitch. The classification of homologs he proposed 40 years ago has become part of the research fabric in evolutionary biology and genomics—so much so that it is used mostly without attribution. It is hard to think of a higher recognition and a better tribute to a great scientist. The usage of the term ‘ortholog’ in the title or abstract of scientific publications. The usage data were from PubMed (http://www.ncbi.nlm.nih.gov/pubmed/). The author is funded through the intramural program of US Department of Health and Human Services (National Library of Medicine, NIH).

Accurate inference of orthologous genes is a pre-requisite for most comparative genomics studies, and is also important for functional annotation of new genomes. Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches. The most direct tree-based methods typically rely on the comparison of an individual gene tree with a species tree. Once the two trees are accurately constructed, orthologs are straightforwardly identified by the definition of orthology as those homologs that are related by speciation, rather than gene duplication, at their most recent point of origin. Although ideal for the purpose of orthology identification in principle, phylogenetic trees are computationally expensive to construct for large numbers of genes and genomes, and they often contain errors, especially at large evolutionary distances. Moreover, in many organisms, in particular prokaryotes and viruses, evolution does not appear to have followed a simple 'tree-like' mode, which makes conventional tree reconciliation inapplicable. Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms. These approaches are faster and easier to automate than tree-based methods, with efficient implementations provided by graph-theoretical algorithms enabling comparisons of thousands of genomes. Comparisons of these two approaches show that, despite conceptual differences, they produce similar sets of orthologs, especially at short evolutionary distances. Synteny also can aid in identification of orthologs. Often, tree-based, sequence similarity- and synteny-based approaches can be combined into flexible hybrid methods.

Protein-coding genes in eukaryotes are interrupted by introns, but intron densities widely differ between eukaryotic lineages. Vertebrates, some invertebrates and green plants have intron-rich genes, with 6-7 introns per kilobase of coding sequence, whereas most of the other eukaryotes have intron-poor genes. We reconstructed the history of intron gain and loss using a probabilistic Markov model (Markov Chain Monte Carlo, MCMC) on 245 orthologous genes from 99 genomes representing the three of the five supergroups of eukaryotes for which multiple genome sequences are available. Intron-rich ancestors are confidently reconstructed for each major group, with 53 to 74% of the human intron density inferred with 95% confidence for the Last Eukaryotic Common Ancestor (LECA). The results of the MCMC reconstruction are compared with the reconstructions obtained using Maximum Likelihood (ML) and Dollo parsimony methods. An excellent agreement between the MCMC and ML inferences is demonstrated whereas Dollo parsimony introduces a noticeable bias in the estimations, typically yielding lower ancestral intron densities than MCMC and ML. Evolution of eukaryotic genes was dominated by intron loss, with substantial gain only at the bases of several major branches including plants and animals. The highest intron density, 120 to 130% of the human value, is inferred for the last common ancestor of animals. The reconstruction shows that the entire line of descent from LECA to mammals was intron-rich, a state conducive to the evolution of alternative splicing.

Research in quantitative evolutionary genomics and systems biology led to the discovery of several universal regularities connecting genomic and molecular phenomic variables. These universals include the log-normal distribution of the evolutionary rates of orthologous genes; the power law-like distributions of paralogous family size and node degree in various biological networks; the negative correlation between a gene's sequence evolution rate and expression level; and differential scaling of functional classes of genes with genome size. The universals of genome evolution can be accounted for by simple mathematical models similar to those used in statistical physics, such as the birth-death-innovation model. These models do not explicitly incorporate selection; therefore, the observed universal regularities do not appear to be shaped by selection but rather are emergent properties of gene ensembles. Although a complete physical theory of evolutionary biology is inconceivable, the universals of genome evolution might qualify as "laws of evolutionary genomics" in the same sense "law" is understood in modern physics.

Experimental studies on enzyme evolution show that only a small fraction of all possible mutation trajectories are accessible to evolution. However, these experiments deal with individual enzymes and explore a tiny part of the fitness landscape. We report an exhaustive analysis of fitness landscapes constructed with an off-lattice model of protein folding where fitness is equated with robustness to misfolding. This model mimics the essential features of the interactions between amino acids, is consistent with the key paradigms of protein folding and reproduces the universal distribution of evolutionary rates among orthologous proteins. We introduce mean path divergence as a quantitative measure of the degree to which the starting and ending points determine the path of evolution in fitness landscapes. Global measures of landscape roughness are good predictors of path divergence in all studied landscapes: the mean path divergence is greater in smooth landscapes than in rough ones. The model-derived and experimental landscapes are significantly smoother than random landscapes and resemble additive landscapes perturbed with moderate amounts of noise; thus, these landscapes are substantially robust to mutation. The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness. We suggest that smoothness and the substantial deficit of peaks in the fitness landscapes of protein evolution are fundamental consequences of the physics of protein folding.

The division of labor between template and catalyst is a fundamental property of all living systems: DNA stores genetic information whereas proteins function as catalysts. The RNA world hypothesis, however, posits that, at the earlier stages of evolution, RNA acted as both template and catalyst. Why would such division of labor evolve in the RNA world? We investigated the evolution of DNA-like molecules, i.e. molecules that can function only as template, in minimal computational models of RNA replicator systems. In the models, RNA can function as both template-directed polymerase and template, whereas DNA can function only as template. Two classes of models were explored. In the surface models, replicators are attached to surfaces with finite diffusion. In the compartment models, replicators are compartmentalized by vesicle-like boundaries. Both models displayed the evolution of DNA and the ensuing division of labor between templates and catalysts. In the surface model, DNA provides the advantage of greater resistance against parasitic templates. However, this advantage is at least partially offset by the disadvantage of slower multiplication due to the increased complexity of the replication cycle. In the compartment model, DNA can significantly delay the intra-compartment evolution of RNA towards catalytic deterioration. These results are explained in terms of the trade-off between template and catalyst that is inherent in RNA-only replication cycles: DNA releases RNA from this trade-off by making it unnecessary for RNA to serve as template and so rendering the system more resistant against evolving parasitism. Our analysis of these simple models suggests that the lack of catalytic activity in DNA by itself can generate a sufficient selective advantage for RNA replicator systems to produce DNA. Given the widespread notion that DNA evolved owing to its superior chemical properties as a template, this study offers a novel insight into the evolutionary origin of DNA.

MOTIVATION: Identifying orthologous genes in multiple genomes is a fundamental task in comparative genomics. Construction of intergenomic symmetrical best matches (SymBets) and joining them into clusters is a popular method of ortholog definition, embodied in several software programs. Despite their wide use, the computational complexity of these programs has not been thoroughly examined. RESULTS: In this work, we show that in the standard approach of iteration through all triangles of SymBets, the memory scales with at least the number of these triangles, O(g(3)) (where g = number of genomes), and construction time scales with the iteration through each pair, i.e. O(g(6)). We propose the EdgeSearch algorithm that iterates over edges in the SymBet graph rather than triangles of SymBets, and as a result has a worst-case complexity of only O(g(3)log g). Several optimizations reduce the run-time even further in realistically sparse graphs. In two real-world datasets of genomes from bacteriophages (POGs) and Mollicutes (MOGs), an implementation of the EdgeSearch algorithm runs about an order of magnitude faster than the original algorithm and scales much better with increasing number of genomes, with only minor differences in the final results, and up to 60 times faster than the popular OrthoMCL program with a 90% overlap between the identified groups of orthologs. AVAILABILITY AND IMPLEMENTATION: C++ source code freely available for download at ftp.ncbi.nih.gov/pub/wolf/COGs/COGsoft/. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

In this review, we discuss applications of the theory of birth-and-death processes to problems in biology, primarily, those of evolutionary genomics. The mathematical principles of the theory of these processes are briefly described. Birth-and-death processes, with some straightforward additions such as innovation, are a simple, natural and formal framework for modeling a vast variety of biological processes such as population dynamics, speciation, genome evolution, including growth of paralogous gene families and horizontal gene transfer and somatic evolution of cancers. We further describe how empirical data, e.g. distributions of paralogous gene family size, can be used to choose the model that best reflects the actual course of evolution among different versions of birth-death-and-innovation models. We conclude that birth-and-death processes, thanks to their mathematical transparency, flexibility and relevance to fundamental biological processes, are going to be an indispensable mathematical tool for the burgeoning field of systems biology.

Toxin-antitoxin systems (TAS) are abundant, diverse, horizontally mobile gene modules that encode powerful resistance mechanisms in prokaryotes. We use the comparative-genomic approach to predict a new TAS that consists of a two-gene cassette encoding uncharacterized HicA and HicB proteins. Numerous bacterial and archaeal genomes encode from one to eight HicAB modules which appear to be highly prone to horizontal gene transfer. The HicB protein (COG1598/COG4226) has a partially degraded RNAse H fold, whereas HicA (COG1724) contains a double-stranded RNA-binding domain. The stable combination of these two domains suggests a link to RNA metabolism, possibly, via an RNA interference-type mechanism. In most HicB proteins, the RNAse H-like domain is fused to a DNA-binding domain, either of the ribbon-helix-helix or of the helix-turn-helix class; in other TAS, proteins containing these DNA-binding domains function as antitoxins. Thus, the HicAB module is predicted to be a novel TAS whose mechanism involves RNA-binding and, possibly, cleavage.

The cyanobacterium Anabaena (Nostoc) PCC 7120 responds to starvation for nitrogen compounds by differentiating approximately every 10th cell in the filament into nitrogen-fixing cells called heterocysts. Heterocyst formation is subject to complex regulation, which involves an unusual response regulator PatA that contains a CheY-like phosphoacceptor (receiver, REC) domain at its C-terminus. PatA-like response regulators are widespread in cyanobacteria; one of them regulates phototaxis in Synechocystis PCC 6803. Sequence analysis of PatA revealed, in addition to the REC domain, a previously undetected, conserved domain, which we named PATAN (after PatA N-terminus), and a potential helix-turn-helix (HTH) domain. PATAN domains are encoded in a variety of environmental bacteria and archaea, often in several copies per genome, and are typically associated with REC, Roadblock and other signal transduction domains, or with DNA-binding HTH domains. Many PATAN domains contain insertions of a small additional domain, termed alpha-clip, which is predicted to form a four-helix bundle. PATAN domains appear to participate in protein-protein interactions that regulate gliding motility and processes of cell development and differentiation in cyanobacteria and some proteobacteria, such as Myxococcus xanthus and Geobacter sulfurreducens.

The availability of multiple, complete eukaryotic genome sequences allows one to address many fundamental evolutionary questions on genome scale. One such important, long-standing problem is evolution of exon-intron structure of eukaryotic genes. Analysis of orthologous genes from completely sequenced genomes revealed numerous shared intron positions in orthologous genes from animals and plants and even between animals, plants and protists. The data on shared and lineage-specific intron positions were used as the starting point for evolutionary reconstruction with parsimony and maximum-likelihood approaches. Parsimony methods produce reconstructions with intron-rich ancestors but also infer lineage-specific, in many cases, high levels of intron loss and gain. Different probabilistic models gave opposite results, apparently depending on model parameters and assumptions, from domination of intron loss, with extremely intron-rich ancestors, to dramatic excess of gains, to the point of denying any true conservation of intron positions among deep eukaryotic lineages. Development of models with adequate, realistic parameters and assumptions seems to be crucial for obtaining more definitive estimates of intron gain and loss in different eukaryotic lineages. Many shared intron positions were detected in ancestral eukaryotic paralogues which evolved by duplication prior to the divergence of extant eukaryotic lineages. These findings indicate that numerous introns were present in eukaryotic genes already at the earliest stages of evolution of eukaryotes and are compatible with the hypothesis that the original, catastrophic intron invasion accompanied the emergence of the eukaryotic cells. Comparison of various features of old and younger introns starts shedding light on probable mechanisms of intron insertion, indicating that propagation of old introns is unlikely to be a major mechanism for origin of new ones. The existence and structure of ancestral protosplice sites were addressed by examining the context of introns inserted within codons that encode amino acids conserved in all eukaryotes and, accordingly, are not subject to selection for splicing efficiency. It was shown that introns indeed predominantly insert into or are fixed in specific protosplice sites which have the consensus sequence (A/C)AG|Gt.

MOTIVATION: In our previous studies, we developed discrete-space birth, death and innovation models (BDIMs) of genome evolution. These models explain the origin of the characteristic Pareto distribution of paralogous gene family sizes in genomes, and model parameters that provide for the evolution of these distributions within a realistic time frame have been identified. However, extracting the temporal dynamics of genome evolution from discrete-space BDIM was not technically feasible. We were interested in obtaining dynamic portraits of the genome evolution process by developing a diffusion approximation of BDIM. RESULTS: The diffusion version of BDIM belongs to a class of continuous-state models whose dynamics is described by the Fokker-Plank equation and the stationary solution could be any specified Pareto function. The diffusion models have time-dependent solutions of a special kind, namely, generalized self-similar solutions, which describe the transition from one stationary distribution of the system to another; this provides for the possibility of examining the temporal dynamics of genome evolution. Analysis of the generalized self-similar solutions of the diffusion BDIM reveals a biphasic curve of genome growth in which the initial, relatively short, self-accelerating phase is followed by a prolonged phase of slow deceleration. This evolutionary dynamics was observed both when genome growth started from zero and proceeded via innovation (a potential model of primordial evolution), and when evolution proceeded from one stationary state to another. In biological terms, this regime of evolution can be tentatively interpreted as a punctuated-equilibrium-like phenomenon whereby evolutionary transitions are accompanied by rapid gene amplification and innovation, followed by slow relaxation to a new stationary state.

Gene order in prokaryotes is conserved to a much lesser extent than protein sequences. Only some operons, primarily those that encode physically interacting proteins, are conserved in all or most of the bacterial and archaeal genomes. Nevertheless, even the limited conservation of operon organisation that is observed provides valuable evolutionary and functional clues through multiple genome comparisons. With the rapid growth in the number and diversity of sequenced prokaryotic genomes, functional inferences for uncharacterized genes located in the same conserved gene neighborhood with well-studied genes are becoming increasingly important. In this review, we discuss various computational approaches for identification of conserved gene strings and construction of local alignments of gene orders in prokaryotic genomes.

MOTIVATION: The distributions of many genome-associated quantities, including the membership of paralogous gene families can be approximated with power laws. We are interested in developing mathematical models of genome evolution that adequately account for the shape of these distributions and describe the evolutionary dynamics of their formation. RESULTS: We show that simple stochastic models of genome evolution lead to power-law asymptotics of protein domain family size distribution. These models, called Birth, Death and Innovation Models (BDIM), represent a special class of balanced birth-and-death processes, in which domain duplication and deletion rates are asymptotically equal up to the second order. The simplest, linear BDIM shows an excellent fit to the observed distributions of domain family size in diverse prokaryotic and eukaryotic genomes. However, the stochastic version of the linear BDIM explored here predicts that the actual size of large paralogous families is reached on an unrealistically long timescale. We show that introduction of non-linearity, which might be interpreted as interaction of a particular order between individual family members, allows the model to achieve genome evolution rates that are much better compatible with the current estimates of the rates of individual duplication/loss events.

BACKGROUND: The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. RESULTS: We describe here a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or approximately 54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of approximately 20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (approximately 1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes. CONCLUSION: The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies.

Mark Borodovsky, Eugene Koonin, Chris Burge, Jim Fickett, John Logsdon, Andrej Sali, Gary Stormo, Igor Zhulin; THE THIRD GEORGIA TECH–EMORY INTERNATIONAL C

MOTIVATION: The context of the start codon (typically, AUG) and the features of the 5' Untranslated Regions (5' UTRs) are important for understanding translation regulation in eukaryotic mRNAs and for accurate prediction of the coding region in genomic and cDNA sequences. The presence of AUG triplets in 5' UTRs (upstream AUGs) might effect the initiation rate and, in the context of gene prediction, could reduce the accuracy of the identification of the authentic start. To reveal potential connections between the presence of upstream AUGs and other features of 5' UTRs, such as their length and the start codon context, we undertook a systematic analysis of the available eukaryotic 5' UTR sequences. RESULTS: We show that a large fraction of 5' UTRs in the available cDNA sequences, 15-53% depending on the organism, contain upstream ATGs. A negative correlation was observed between the information content of the translation start signal and the length of the 5' UTR. Similarly, a negative correlation exists between the 'strength' of the start context and the number of upstream ATGs. Typically, cDNAs containing long 5' UTRs with multiple upstream ATGs have a 'weak' start context, and in contrast, cDNAs containing short 5' UTRs without ATGs have 'strong' starts. These counter-intuitive results may be interpreted in terms of upstream AUGs having an important role in the regulation of translation efficiency by ensuring low basal translation level via double negative control and creating the potential for additional regulatory mechanisms. One of such mechanisms, supported by experimental studies of some mRNAs, includes removal of the AUG-containing portion of the 5' UTR by alternative splicing. AVAILABILITY: An ATG_ EVALUATOR program is available upon request or at www.itba.mi.cnr.it/webgene. CONTACT: [email protected], [email protected].

In database searches for sequence similarity, matches to a distinct sequence region (e.g. protein domain) are frequently obscured by numerous matches to another region of the same sequence.In order to cope with this problem, algorithms are developed to discard redundant matches.One model for this problem begins with a list of intervals, each with an associated score; each interval gives the range of positions in the query sequence that align to a database sequence, and the score is that of the alignment.If interval I is contained in interval J, and I's score is less than J's, then I is said to be dominated by J.The problem is then to identify each interval that is dominated by at least K other intervals, where K is a given level of "tolerable redundancy."An algorithm is developed to solve the problem in O(N log N) time and O(N*) space, where N is the number of intervals and N' is a precisely defined value that never exceeds N and is frequently much smaller.This criterion for discarding database hits has been implemented in the Blast program, as illustrated herein with examples.Several variations and extensions of this approach are also described.

Abstract Motivation: Many studies have shown that database searches using position-specific score matrices (PSSMs) or profiles as queries are more effective at identifying distant protein relationships than are searches that use simple sequences as queries. One popular program for constructing a PSSM and comparing it with a database of sequences is Position-Specific Iterated BLAST (PSI-BLAST). Results: This paper describes a new software package, IMPALA, designed for the complementary procedure of comparing a single query sequence with a database of PSI-BLAST-generated PSSMs. We illustrate the use of IMPALA to search a database of PSSMs for protein folds, and one for protein domains involved in signal transduction. IMPALA’s sensitivity to distant biological relationships is very similar to that of PSI-BLAST. However, IMPALA employs a more refined analysis of statistical significance and, unlike PSI-BLAST, guarantees the output of the optimal local alignment by using the rigorous Smith–Waterman algorithm. Also, it is considerably faster when run with a large database of PSSMs than is BLAST or PSI-BLAST when run against the complete non-redundant protein database. Availability: The IMPALA source code, the wolf1187 database, and the aravind105 database are freely available from the NCBI ftp site ncbi.nlm.nih.gov. The databases may be found in the subdirectory ftp://ncbi.nlm.nih.gov/pub/impala. The source code is in ftp://ncbi.nlm.nih.gov/toolbox/ncbi˙tools. Some IMPALA executables for different implementations of UNIX are in ftp://ncbi.nlm.nih.gov/blast/executables. IMPALA has been added as a search option on the Blocks Database Server (http://blocks.fhcrc.org/blocks/impala.html)using a library of PSSMs derived from the BLOCKS database. Contact: [email protected]

Article Free Access Share on Genome analysis using clusters of orthologous groups (COGs) Authors: Eugene V. Koonin National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MDView Profile , Roman L. Tatusov National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MDView Profile , Michael Y. Galperin National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MDView Profile , Michael N. Rosanov National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MDView Profile Authors Info & Claims RECOMB '98: Proceedings of the second annual international conference on Computational molecular biologyMarch 1998 Pages 135–139https://doi.org/10.1145/279069.279106Published:01 March 1998Publication History 1citation518DownloadsMetricsTotal Citations1Total Downloads518Last 12 Months27Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Roman L. Tatusov, Eugene V. Koonin; A simple tool to search for sequence motifs that are conserved in BLAST outputs, Bioinformatics, Volume 10, Issue 4, 1 July