Orthology Assignment Research Articles

Large-scale assignment of orthology: back to phylogenetics?

Automated use of phylogenetic trees to deduce orthology relationships in proteins.

OPTIC: orthologous and paralogous transcripts in clades.

The genome sequences of a large number of metazoan species are now known. As multiple closely related genomes are sequenced, comparative studies that previously focussed on only pairs of genomes can now be extended over whole clades. The orthologous and paralogous transcripts in clades (OPTIC) database currently provides sets of gene predictions and orthology assignments for three clades: (i) amniotes, including human, dog, mouse, opossum, platypus and chicken (17 443 orthologous groups); (ii) a Drosophila clade of 12 species (12 889 orthologous groups) and (iii) a nematode clade of four species (13 626 orthologous groups). Gene predictions, multiple alignments and phylogenetic trees are freely available to browse and download from http://genserv.anat.ox.ac.uk/clades. Further genomes and clades will be added in the future.

Evolutionary rate analyses of orthologs and paralogs from 12 Drosophila genomes

The newly sequenced genome sequences of 11 Drosophila species provide the first opportunity to investigate variations in evolutionary rates across a clade of closely related species. Protein-coding genes were predicted using established Drosophila melanogaster genes as templates, with recovery rates ranging from 81%-97% depending on species divergence and on genome assembly quality. Orthology and paralogy assignments were shown to be self-consistent among the different Drosophila species and to be consistent with regions of conserved gene order (synteny blocks). Next, we investigated the rates of diversification among these species' gene repertoires with respect to amino acid substitutions and to gene duplications. Constraints on amino acid sequences appear to have been most pronounced on D. ananassae and least pronounced on D. simulans and D. erecta terminal lineages. Codons predicted to have been subject to positive selection were found to be significantly over-represented among genes with roles in immune response and RNA metabolism, with the latter category including each subunit of the Dicer-2/r2d2 heterodimer. The vast majority of gene duplications (96.5%) and synteny rearrangements were found to occur, as expected, within single Müller elements. We show that the rate of ancient gene duplications was relatively uniform. However, gene duplications in terminal lineages are strongly skewed toward very recent events, consistent with either a rapid-birth and rapid-death model or the presence of large proportions of copy number variable genes in these Drosophila populations. Duplications were significantly more frequent among trypsin-like proteases and DM8 putative lipid-binding domain proteins.

MSOAR: A High-Throughput Ortholog Assignment System Based on Genome Rearrangement

The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics, since many computational methods for solving various biological problems critically rely on bona fide orthologs as input. While it is usually done using sequence similarity search, we recently proposed a new combinatorial approach that combines sequence similarity and genome rearrangement. This paper continues the development of the approach and unites genome rearrangement events and (post-speciation) duplication events in a single framework under the parsimony principle. In this framework, orthologous genes are assumed to correspond to each other in the most parsimonious evolutionary scenario involving both genome rearrangement and (post-speciation) gene duplication. Besides several original algorithmic contributions, the enhanced method allows for the detection of inparalogs. Following this approach, we have implemented a high-throughput system for ortholog assignment on a genome scale, called MSOAR, and applied it to human and mouse genomes. As the result will show, MSOAR is able to find 99 more true orthologs than the INPARANOID program did. In comparison to the iterated exemplar algorithm on simulated data, MSOAR performed favorably in terms of assignment accuracy. We also validated our predicted main ortholog pairs between human and mouse using public ortholog assignment datasets, synteny information, and gene function classification. These test results indicate that our approach is very promising for genome-wide ortholog assignment. Supplemental material and MSOAR program are available at http://msoar.cs.ucr.edu.

An analysis of the gene complement of a marsupial,Monodelphis domestica: Evolution of lineage-specific genes and giant chromosomes

The newly sequenced genome of Monodelphis domestica not only provides the out-group necessary to better understand our own eutherian lineage, but it enables insights into the innovative biology of metatherians. Here, we compare Monodelphis with Homo sequences from alignments of single nucleotides, genes, and whole chromosomes. Using PhyOP, we have established orthologs in Homo for 82% (15,250) of Monodelphis gene predictions. Those with single orthologs in each species exhibited a high median synonymous substitution rate (d(S) = 1.02), thereby explaining the relative paucity of aligned regions outside of coding sequences. Orthology assignments were used to construct a synteny map that illustrates the considerable fragmentation of Monodelphis and Homo karyotypes since their therian last common ancestor. Fifteen percent of Monodelphis genes are predicted, from their low divergence at synonymous sites, to have been duplicated in the metatherian lineage. The majority of Monodelphis-specific genes possess predicted roles in chemosensation, reproduction, adaptation to specific diets, and immunity. Using alignments of Monodelphis genes to sequences from either Homo or Trichosurus vulpecula (an Australian marsupial), we show that metatherian X chromosomes have elevated silent substitution rates and high G+C contents in comparison with both metatherian autosomes and eutherian chromosomes. Each of these elevations is also a feature of subtelomeric chromosomal regions. We attribute these observations to high rates of female-specific recombination near the chromosomal ends and within the X chromosome, which act to sustain or increase G+C levels by biased gene conversion. In particular, we propose that the higher G+C content of the Monodelphis X chromosome is a direct consequence of its small size relative to the giant autosomes.

Orthology prediction at scalable resolution by phylogenetic tree analysis.

BackgroundOrthology is one of the cornerstones of gene function prediction. Dividing the phylogenetic relations between genes into either orthologs or paralogs is however an oversimplification. Already in two-species gene-phylogenies, the complicated, non-transitive nature of phylogenetic relations results in inparalogs and outparalogs. For situations with more than two species we lack semantics to specifically describe the phylogenetic relations, let alone to exploit them. Published procedures to extract orthologous groups from phylogenetic trees do not allow identification of orthology at various levels of resolution, nor do they document the relations between the orthologous groups.ResultsWe introduce "levels of orthology" to describe the multi-level nature of gene relations. This is implemented in a program LOFT (Levels of Orthology From Trees) that assigns hierarchical orthology numbers to genes based on a phylogenetic tree. To decide upon speciation and gene duplication events in a tree LOFT can be instructed either to perform classical species-tree reconciliation or to use the species overlap between partitions in the tree. The hierarchical orthology numbers assigned by LOFT effectively summarize the phylogenetic relations between genes. The resulting high-resolution orthologous groups are depicted in colour, facilitating visual inspection of (large) trees. A benchmark for orthology prediction, that takes into account the varying levels of orthology between genes, shows that the phylogeny-based high-resolution orthology assignments made by LOFT are reliable.ConclusionThe "levels of orthology" concept offers high resolution, reliable orthology, while preserving the relations between orthologous groups. A Windows as well as a preliminary Java version of LOFT is available from the LOFT website .

A network flow approach to the Minimum Common Integer Partition Problem

In the k-Minimum Common Integer Partition Problem, abbreviated as k-MCIP, we are given k multisets X 1 , … , X k of positive integers, the goal is to find an integer multiset T of the minimum size such that for every i, we can partition each of the integers in X i so that the disjoint (multiset) union of their partitions equals T. This problem has applications in computational molecular biology, in particular, ortholog assignment and DNA hybridization fingerprint assembly. The problem is known to be NP-hard for any k ⩾ 2 . In this article, we improve the approximation ratio for k-MCIP by viewing this problem as a flow decomposition problem in some flow network. We show an efficient 0.5625 k -approximation algorithm, improving upon the previously best known 0.6139 k -approximation algorithm for this problem.

Detecting non-orthology in the COGs database and other approaches grouping orthologs using genome-specific best hits

Correct orthology assignment is a critical prerequisite of numerous comparative genomics procedures, such as function prediction, construction of phylogenetic species trees and genome rearrangement analysis. We present an algorithm for the detection of non-orthologs that arise by mistake in current orthology classification methods based on genome-specific best hits, such as the COGs database. The algorithm works with pairwise distance estimates, rather than computationally expensive and error-prone tree-building methods. The accuracy of the algorithm is evaluated through verification of the distribution of predicted cases, case-by-case phylogenetic analysis and comparisons with predictions from other projects using independent methods. Our results show that a very significant fraction of the COG groups include non-orthologs: using conservative parameters, the algorithm detects non-orthology in a third of all COG groups. Consequently, sequence analysis sensitive to correct orthology assignments will greatly benefit from these findings.

Systematic identification of functional orthologs based on protein network comparison

Annotating protein function across species is an important task that is often complicated by the presence of large paralogous gene families. Here, we report a novel strategy for identifying functionally related proteins that supplements sequence-based comparisons with information on conserved protein-protein interactions. First, the protein interaction networks of two species are aligned by assigning proteins to sequence homology clusters using the Inparanoid algorithm. Next, probabilistic inference is performed on the aligned networks to identify pairs of proteins, one from each species, that are likely to retain the same function based on conservation of their interacting partners. Applying this method to Drosophila melanogaster and Saccharomyces cerevisiae, we analyze 121 cases for which functional orthology assignment is ambiguous when sequence similarity is used alone. In 61 of these cases, the network supports a different protein pair than that favored by sequence comparisons. These results suggest that network analysis can be used to provide a key source of information for refining sequence-based homology searches.

Stable evolutionary signal in a Yeast protein interaction network

BackgroundThe recently emerged protein interaction network paradigm can provide novel and important insights into the innerworkings of a cell. Yet, the heavy burden of both false positive and false negative protein-protein interaction data casts doubt on the broader usefulness of these interaction sets. Approaches focusing on one-protein-at-a-time have been powerfully employed to demonstrate the high degree of conservation of proteins participating in numerous interactions; here, we expand his 'node' focused paradigm to investigate the relative persistence of 'link' based evolutionary signals in a protein interaction network of S. cerevisiae and point out the value of this relatively untapped source of information.ResultsThe trend for highly connected proteins to be preferably conserved in evolution is stable, even in the context of tremendous noise in the underlying protein interactions as well as in the assignment of orthology among five higher eukaryotes. We find that local clustering around interactions correlates with preferred evolutionary conservation of the participating proteins; furthermore the correlation between high local clustering and evolutionary conservation is accompanied by a stable elevated degree of coexpression of the interacting proteins. We use this conserved interaction data, combined with P. falciparum /Yeast orthologs, as proof-of-principle that high-order network topology can be used comparatively to deduce local network structure in non-model organisms.ConclusionHigh local clustering is a criterion for the reliability of an interaction and coincides with preferred evolutionary conservation and significant coexpression. These strong and stable correlations indicate that evolutionary units go beyond a single protein to include the interactions among them. In particular, the stability of these signals in the face of extreme noise suggests that empirical protein interaction data can be integrated with orthologous clustering around these protein interactions to reliably infer local network structures in non-model organisms.

Positional Homology in Bacterial Genomes

In comparative genomic studies, syntenic groups of homologous sequence in the same order have been used as supplementary information that can be used in helping to determine the orthology of the compared sequences. The assumption is that orthologous gene copies are more likely to share the same genome positions and share the same gene neighbors. In this study we have defined positional homologs as those that also have homologous neighboring genes and we investigated the usefulness of this distinction for bacterial comparative genomics. We considered the identification of positionaly homologous gene pairs in bacterial genomes using protein and DNA sequence level alignments and found that the positional homologs had on average relatively lower rates of substitution at the DNA level (synonymous substitutions) than duplicate homologs in different genomic locations, regardless of the level of protein sequence divergence (measured with non-synonymous substitution rate). Since gene order conservation can indicate accuracy of orthology assignments, we also considered the effect of imposing certain alignment quality requirements on the sensitivity and specificity of identification of protein pairs by BLAST and FASTA when neighboring information is not available and in comparisons where gene order is not conserved. We found that the addition of a stringency filter based on the second best hits was an efficient way to remove dubious ortholog identifications in BLAST and FASTA analyses. Gene order conservation and DNA sequence homology are useful to consider in comparative genomic studies as they may indicate different orthology assignments than protein sequence homology alone.

TreeFam: a curated database of phylogenetic trees of animal gene families.

TreeFam is a database of phylogenetic trees of gene families found in animals. It aims to develop a curated resource that presents the accurate evolutionary history of all animal gene families, as well as reliable ortholog and paralog assignments. Curated families are being added progressively, based on seed alignments and trees in a similar fashion to Pfam. Release 1.1 of TreeFam contains curated trees for 690 families and automatically generated trees for another 11 646 families. These represent over 128 000 genes from nine fully sequenced animal genomes and over 45 000 other animal proteins from UniProt; ∼40–85% of proteins encoded in the fully sequenced animal genomes are included in TreeFam. TreeFam is freely available at and .

HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms

BackgroundThe application of high throughput approaches to the identification of protein interactions has offered for the first time a glimpse of the global interactome of some model organisms. Until now, however, such genome-wide approaches have not been applied to the human proteome.ResultsIn order to fill this gap we have assembled an inferred human protein interaction network where interactions discovered in model organisms are mapped onto the corresponding human orthologs. In addition to a stringent assignment to orthology classes based on the InParanoid algorithm, we have implemented a string matching algorithm to filter out orthology assignments of proteins whose global domain organization is not conserved. Finally, we have assessed the accuracy of our own, and related, inferred networks by benchmarking them against i) an assembled experimental interactome, ii) a network derived by mining of the scientific literature and iii) by measuring the enrichment of interacting protein pairs sharing common Gene Ontology annotation.ConclusionThe resulting networks are named HomoMINT and HomoMINT_filtered, the latter being based on the orthology table filtered by the domain architecture matching algorithm. They contains 9749 and 5203 interactions respectively and can be analyzed and viewed in the context of the experimentally verified interactions between human proteins stored in the MINT database. HomoMINT is constantly updated to take into account the growing information in the MINT database.

Assignment of Orthologous Genes via Genome Rearrangement

The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics. Existing methods that assign orthologs based on the similarity between DNA or protein sequences may make erroneous assignments when sequence similarity does not clearly delineate the evolutionary relationship among genes of the same families. In this paper, we present a new approach to ortholog assignment that takes into account both sequence similarity and evolutionary events at a genome level, where orthologous genes are assumed to correspond to each other in the most parsimonious evolving scenario under genome rearrangement. First, the problem is formulated as that of computing the signed reversal distance with duplicates between the two genomes of interest. Then, the problem is decomposed into two new optimization problems, called minimum common partition and maximum cycle decomposition, for which efficient heuristic algorithms are given. Following this approach, we have implemented a high-throughput system for assigning orthologs on a genome scale, called SOAR, and tested it on both simulated data and real genome sequence data. Compared to a recent ortholog assignment method based entirely on homology search (called INPARANOID), SOAR shows a marginally better performance in terms of sensitivity on the real data set because it is able to identify several correct orthologous pairs that are missed by INPARANOID. The simulation results demonstrate that SOAR, in general, performs better than the iterated exemplar algorithm in terms of computing the reversal distance and assigning correct orthologs.

WFleaBase: the Daphnia genome database

BackgroundwFleaBase is a database with the necessary infrastructure to curate, archive and share genetic, molecular and functional genomic data and protocols for an emerging model organism, the microcrustacean Daphnia. Commonly known as the water-flea, Daphnia's ecological merit is unequaled among metazoans, largely because of its sentinel role within freshwater ecosystems and over 200 years of biological investigations. By consequence, the Daphnia Genomics Consortium (DGC) has launched an interdisciplinary research program to create the resources needed to study genes that affect ecological and evolutionary success in natural environments.DiscussionThese tools include the genome database wFleaBase, which currently contains functions to search and extract information from expressed sequenced tags, genome survey sequences and full genome sequencing projects. This new database is built primarily from core components of the Generic Model Organism Database project, and related bioinformatics tools.SummaryOver the coming year, preliminary genetic maps and the nearly complete genomic sequence of Daphnia pulex will be integrated into wFleaBase, including gene predictions and ortholog assignments based on sequence similarities with eukaryote genes of known function. wFleaBase aims to serve a large ecological and evolutionary research community. Our challenge is to rapidly expand its content and to ultimately integrate genetic and functional genomic information with population-level responses to environmental challenges. URL: .

WormBase: a multi-species resource for nematode biology and genomics

WormBase (http://www.wormbase.org/) is the central data repository for information about Caenorhabditis elegans and related nematodes. As a model organism database, WormBase extends beyond the genomic sequence, integrating experimental results with extensively annotated views of the genome. The WormBase Consortium continues to expand the biological scope and utility of WormBase with the inclusion of large-scale genomic analyses, through active data and literature curation, through new analysis and visualization tools, and through refinement of the user interface. Over the past year, the nearly complete genomic sequence and comparative analyses of the closely related species Caenorhabditis briggsae have been integrated into WormBase, including gene predictions, ortholog assignments and a new synteny viewer to display the relationships between the two species. Extensive site-wide refinement of the user interface now provides quick access to the most frequently accessed resources and a consistent browsing experience across the site. Unified single-page views now provide complete summaries of commonly accessed entries like genes. These advances continue to increase the utility of WormBase for C.elegans researchers, as well as for those researchers exploring problems in functional and comparative genomics in the context of a powerful genetic system.

Mining gene expression data for functional annotation of genomes

The ability of DNA microarrays to monitor the expression of thousands of genes simultaneously has made it possible to examine all the transcripts in cells or tissues, the ‘transcriptome’, in a high-throughput fashion. Using this technology, it will soon be possible to analyze the entire transcriptome on a single chip. So far, DNA microarrays have largely been used to study differential expression of genes in two or more states or to identify single nucleotide polymorphisms (SNPs). Discovering groups of genes that are specifically expressed in different tumors compared with normal tissue has been the focus of several studies. Such efforts have already led to the identification of distinct transcript ‘signatures’, which can sometimes be used to classify tumors into different subtypes.Global transcript analysis can also be put to several other uses, as elegantly demonstrated in a recent study by Su et al. [1xLarge-scale analysis of the human and mouse transcriptomes. Su, A.I. et al. Proc. Natl. Acad. Sci. U. S. A. 2002; 99: 4465–4470Crossref | PubMed | Scopus (1089)See all References][1]. They combined the information obtained from expression patterns of thousands of human and mouse genes in several tissues, organs and cell lines with sequence analysis to provide a more complete description of gene function. They obtained the expression profiles by hybridizing 46 human and 45 mouse-tissue RNA samples to human or mouse oligonucleotide arrays and found that ∼5% of the genes examined were ubiquitously expressed and any individual tissue expressed ∼30–40% of the genes. Using stringent criteria, they identified 311 known human tissue restricted genes along with 76 others of which functions have not yet been established. Examining the domain structures of the previously uncharacterized proteins allowed them to establish potential enzyme–substrate relationships and to assign candidate protein–protein interaction partners.The expression data could also be used to confirm sequence-based ortholog assignment because it would be expected that true orthologs have similar patterns of tissue-specific expression in humans and mice. Expression patterns of ∼800 ortholog pairs across 16 tissues, as defined by a common LocusLink symbol, revealed that almost 50% of them were highly correlated in their expression profiles. Surprisingly, a large number of genes were either poorly correlated or even had a negative correlation in their expression patterns between these two species. This indicates that the function of some of these genes might have diverged significantly; a fact not obvious on comparison of their primary amino acid sequences. Overall, the approach of categorizing proteins and orthologs on the basis of tissue specificity and patterns of expression is very promising. This could help in selecting therapeutic targets based on defined criteria such as expression in a defined tissue or coexpression with another gene of interest. Because a large number of genes have no obvious function that can be predicted from their protein sequence, studies such as this can not only help in their annotation but also help direct specific experiments to address their functions.

Automated ortholog inference from phylogenetic trees and calculation of orthology reliability.

Orthologous proteins in different species are likely to have similar biochemical function and biological role. When annotating a newly sequenced genome by sequence homology, the most precise and reliable functional information can thus be derived from orthologs in other species. A standard method of finding orthologs is to compare the sequence tree with the species tree. However, since the topology of phylogenetic tree is not always reliable one might get incorrect assignments. Here we present a novel method that resolves this problem by analyzing a set of bootstrap trees instead of the optimal tree. The frequency of orthology assignments in the bootstrap trees can be interpreted as a support value for the possible orthology of the sequences. Our method is efficient enough to analyze data in the scale of whole genomes. It is implemented in Java and calculates orthology support levels for all pairwise combinations of homologous sequences of two species. The method was tested on simulated datasets and on real data of homologous proteins.

Automatic clustering of orthologs and in-paralogs from pairwise species comparisons

Orthologs are genes in different species that originate from a single gene in the last common ancestor of these species. Such genes have often retained identical biological roles in the present-day organisms. It is hence important to identify orthologs for transferring functional information between genes in different organisms with a high degree of reliability. For example, orthologs of human proteins are often functionally characterized in model organisms. Unfortunately, orthology analysis between human and e.g. invertebrates is often complex because of large numbers of paralogs within protein families. Paralogs that predate the species split, which we call out-paralogs, can easily be confused with true orthologs. Paralogs that arose after the species split, which we call in-paralogs, however, are bona fide orthologs by definition. Orthologs and in-paralogs are typically detected with phylogenetic methods, but these are slow and difficult to automate. Automatic clustering methods based on two-way best genome-wide matches on the other hand, have so far not separated in-paralogs from out-paralogs effectively. We present a fully automatic method for finding orthologs and in-paralogs from two species. Ortholog clusters are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for both orthologs and in-paralogs. The program, called INPARANOID, was tested on all completely sequenced eukaryotic genomes. To assess the quality of INPARANOID results, ortholog clusters were generated from a dataset of worm and mammalian transmembrane proteins, and were compared to clusters derived by manual tree-based ortholog detection methods. This study led to the identification with a high degree of confidence of over a dozen novel worm-mammalian ortholog assignments that were previously undetected because of shortcomings of phylogenetic methods. A WWW server that allows searching for orthologs between human and several fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/. This is the first comprehensive resource with orthologs of all fully sequenced eukaryotic genomes. Programs and tables of orthology assignments are available from the same location.