Orthology Prediction Research Articles

Ortholog identification in the presence of domain architecture rearrangement

Ortholog identification is used in gene functional annotation, species phylogeny estimation, phylogenetic profile construction and many other analyses. Bioinformatics methods for ortholog identification are commonly based on pairwise protein sequence comparisons between whole genomes. Phylogenetic methods of ortholog identification have also been developed; these methods can be applied to protein data sets sharing a common domain architecture or which share a single functional domain but differ outside this region of homology. While promiscuous domains represent a challenge to all orthology prediction methods, overall structural similarity is highly correlated with proximity in a phylogenetic tree, conferring a degree of robustness to phylogenetic methods. In this article, we review the issues involved in orthology prediction when data sets include sequences with structurally heterogeneous domain architectures, with particular attention to automated methods designed for high-throughput application, and present a case study to illustrate the challenges in this area.

Positional orthology: putting genomic evolutionary relationships into context

Orthology is a powerful refinement of homology that allows us to describe more precisely the evolution of genomes and understand the function of the genes they contain. However, because orthology is not concerned with genomic position, it is limited in its ability to describe genes that are likely to have equivalent roles in different genomes. Because of this limitation, the concept of ‘positional orthology’ has emerged, which describes the relation between orthologous genes that retain their ancestral genomic positions. In this review, we formally define this concept, for which we introduce the shorter term ‘toporthology’, with respect to the evolutionary events experienced by a gene’s ancestors. Through a discussion of recent studies on the role of genomic context in gene evolution, we show that the distinction between orthology and toporthology is biologically significant. We then review a number of orthology prediction methods that take genomic context into account and thus that may be used to infer the important relation of toporthology.

Letter to the Editor: SeqXML and OrthoXML: standards for sequence and orthology information

There is a great need for standards in the orthology field. Users must contend with different ortholog data representations from each provider, and the providers themselves must independently gather and parse the input sequence data. These burdensome and redundant procedures make data comparison and integration difficult. We have designed two XML-based formats, SeqXML and OrthoXML, to solve these problems. SeqXML is a lightweight format for sequence records-the input for orthology prediction. It stores the same sequence and metadata as typical FASTA format records, but overcomes common problems such as unstructured metadata in the header and erroneous sequence content. XML provides validation to prevent data integrity problems that are frequent in FASTA files. The range of applications for SeqXML is broad and not limited to ortholog prediction. We provide read/write functions for BioJava, BioPerl, and Biopython. OrthoXML was designed to represent ortholog assignments from any source in a consistent and structured way, yet cater to specific needs such as scoring schemes or meta-information. A unified format is particularly valuable for ortholog consumers that want to integrate data from numerous resources, e.g. for gene annotation projects. Reference proteomes for 61 organisms are already available in SeqXML, and 10 orthology databases have signed on to OrthoXML. Adoption by the entire field would substantially facilitate exchange and quality control of sequence and orthology information.

OrthoList: a compendium of C. elegans genes with human orthologs.

Background C. elegans is an important model for genetic studies relevant to human biology and disease. We sought to assess the orthology between C. elegans and human genes to understand better the relationship between their genomes and to generate a compelling list of candidates to streamline RNAi-based screens in this model.ResultsWe performed a meta-analysis of results from four orthology prediction programs and generated a compendium, “OrthoList”, containing 7,663 C. elegans protein-coding genes. Various assessments indicate that OrthoList has extensive coverage with low false-positive and false-negative rates. Part of this evaluation examined the conservation of components of the receptor tyrosine kinase, Notch, Wnt, TGF-ß and insulin signaling pathways, and led us to update compendia of conserved C. elegans kinases, nuclear hormone receptors, F-box proteins, and transcription factors. Comparison with two published genome-wide RNAi screens indicated that virtually all of the conserved hits would have been obtained had just the OrthoList set (∼38% of the genome) been targeted. We compiled Ortholist by InterPro domains and Gene Ontology annotation, making it easy to identify C. elegans orthologs of human disease genes for potential functional analysis.ConclusionsWe anticipate that OrthoList will be of considerable utility to C. elegans researchers for streamlining RNAi screens, by focusing on genes with apparent human orthologs, thus reducing screening effort by ∼60%. Moreover, we find that OrthoList provides a useful basis for annotating orthology and reveals more C. elegans orthologs of human genes in various functional groups, such as transcription factors, than previously described.

QuartetS: a fast and accurate algorithm for large-scale orthology detection

The unparalleled growth in the availability of genomic data offers both a challenge to develop orthology detection methods that are simultaneously accurate and high throughput and an opportunity to improve orthology detection by leveraging evolutionary evidence in the accumulated sequenced genomes. Here, we report a novel orthology detection method, termed QuartetS, that exploits evolutionary evidence in a computationally efficient manner. Based on the well-established evolutionary concept that gene duplication events can be used to discriminate homologous genes, QuartetS uses an approximate phylogenetic analysis of quartet gene trees to infer the occurrence of duplication events and discriminate paralogous from orthologous genes. We used function- and phylogeny-based metrics to perform a large-scale, systematic comparison of the orthology predictions of QuartetS with those of four other methods [bi-directional best hit (BBH), outgroup, OMA and QuartetS-C (QuartetS followed by clustering)], involving 624 bacterial genomes and >2 million genes. We found that QuartetS slightly, but consistently, outperformed the highly specific OMA method and that, while consuming only 0.5% additional computational time, QuartetS predicted 50% more orthologs with a 50% lower false positive rate than the widely used BBH method. We conclude that, for large-scale phylogenetic and functional analysis, QuartetS and QuartetS-C should be preferred, respectively, in applications where high accuracy and high throughput are required.

Evaluating ortholog prediction algorithms in a yeast model clade.

BackgroundAccurate identification of orthologs is crucial for evolutionary studies and for functional annotation. Several algorithms have been developed for ortholog delineation, but so far, manually curated genome-scale biological databases of orthologous genes for algorithm evaluation have been lacking. We evaluated four popular ortholog prediction algorithms (MultiParanoid; and OrthoMCL; RBH: Reciprocal Best Hit; RSD: Reciprocal Smallest Distance; the last two extended into clustering algorithms cRBH and cRSD, respectively, so that they can predict orthologs across multiple taxa) against a set of 2,723 groups of high-quality curated orthologs from 6 Saccharomycete yeasts in the Yeast Gene Order Browser.ResultsExamination of sensitivity [TP/(TP+FN)], specificity [TN/(TN+FP)], and accuracy [(TP+TN)/(TP+TN+FP+FN)] across a broad parameter range showed that cRBH was the most accurate and specific algorithm, whereas OrthoMCL was the most sensitive. Evaluation of the algorithms across a varying number of species showed that cRBH had the highest accuracy and lowest false discovery rate [FP/(FP+TP)], followed by cRSD. Of the six species in our set, three descended from an ancestor that underwent whole genome duplication. Subsequent differential duplicate loss events in the three descendants resulted in distinct classes of gene loss patterns, including cases where the genes retained in the three descendants are paralogs, constituting ‘traps’ for ortholog prediction algorithms. We found that the false discovery rate of all algorithms dramatically increased in these traps.ConclusionsThese results suggest that simple algorithms, like cRBH, may be better ortholog predictors than more complex ones (e.g., OrthoMCL and MultiParanoid) for evolutionary and functional genomics studies where the objective is the accurate inference of single-copy orthologs (e.g., molecular phylogenetics), but that all algorithms fail to accurately predict orthologs when paralogy is rampant.

IsoBase: a database of functionally related proteins across PPI networks

We describe IsoBase, a database identifying functionally related proteins, across five major eukaryotic model organisms: Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus and Homo Sapiens. Nearly all existing algorithms for orthology detection are based on sequence comparison. Although these have been successful in orthology prediction to some extent, we seek to go beyond these methods by the integration of sequence data and protein–protein interaction (PPI) networks to help in identifying true functionally related proteins. With that motivation, we introduce IsoBase, the first publicly available ortholog database that focuses on functionally related proteins. The groupings were computed using the IsoRankN algorithm that uses spectral methods to combine sequence and PPI data and produce clusters of functionally related proteins. These clusters compare favorably with those from existing approaches: proteins within an IsoBase cluster are more likely to share similar Gene Ontology (GO) annotation. A total of 48 120 proteins were clustered into 12 693 functionally related groups. The IsoBase database may be browsed for functionally related proteins across two or more species and may also be queried by accession numbers, species-specific identifiers, gene name or keyword. The database is freely available for download at http://isobase.csail.mit.edu/.

MetaPhOrs: orthology and paralogy predictions from multiple phylogenetic evidence using a consistency-based confidence score

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.

PhylomeDB v3.0: an expanding repository of genome-wide collections of trees, alignments and phylogeny-based orthology and paralogy predictions

The growing availability of complete genomic sequences from diverse species has brought about the need to scale up phylogenomic analyses, including the reconstruction of large collections of phylogenetic trees. Here, we present the third version of PhylomeDB (http://phylomeDB.org), a public database for genome-wide collections of gene phylogenies (phylomes). Currently, PhylomeDB is the largest phylogenetic repository and hosts 17 phylomes, comprising 416 093 trees and 165 840 alignments. It is also a major source for phylogeny-based orthology and paralogy predictions, covering about 5 million proteins in 717 fully-sequenced genomes. For each protein-coding gene in a seed genome, the database provides original and processed alignments, phylogenetic trees derived from various methods and phylogeny-based predictions of orthology and paralogy relationships. The new version of phylomeDB has been extended with novel data access and visualization features, including the possibility of programmatic access. Available seed species include model organisms such as human, yeast, Escherichia coli or Arabidopsis thaliana, but also alternative model species such as the human pathogen Candida albicans, or the pea aphid Acyrtosiphon pisum. Finally, PhylomeDB is currently being used by several genome sequencing projects that couple the genome annotation process with the reconstruction of the corresponding phylome, a strategy that provides relevant evolutionary insights.

A statistical approach to high-throughput screening of predicted orthologs

Orthologs are genes in different species that have diverged from a common ancestral gene after speciation. In contrast, paralogs are genes that have diverged after a gene duplication event. For many comparative analyses, it is of interest to identify orthologs with similar functions. Such orthologs tend to support species divergence (ssd-orthologs) in the sense that they have diverged only due to speciation, to the same relative degree as their species. However, due to incomplete sequencing or gene loss in a species, predicted orthologs can sometimes be paralogs or other non-ssd-orthologs. To increase the specificity of ssd-ortholog prediction, Fulton et al. [Fulton, D., Li, Y., Laird, M., Horsman, B., Roche, F., Brinkman, F., 2006. Improving the specificity of high-throughput ortholog prediction. BMC Bioinformatics 7 (1), 270] developed Ortholuge, a bioinformatics tool that identifies predicted orthologs with atypical genetic divergence. However, when the initial list of putative orthologs contains a non-negligible number of non-ssd-orthologs, the cut-off values that Ortholuge generates for orthology classification are difficult to interpret and can be too high, leading to decreased specificity of ssd-ortholog prediction. Therefore, we propose a complementary statistical approach to determining cut-off values. A benefit of the proposed approach is that it gives the user an estimated conditional probability that a predicted ortholog pair is unusually diverged. This enables the interpretation and selection of cut-off values based on a direct measure of the relative composition of ssd-orthologs versus non-ssd-orthologs. In a simulation comparison of the two approaches, we find that the statistical approach provides more stable cut-off values and improves the specificity of ssd-ortholog prediction for low-quality data sets of predicted orthologs.

RegPredict: an integrated system for regulon inference in prokaryotes by comparative genomics approach

RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is freely accessible at http://regpredict.lbl.gov.

A Phylogenomic Approach to Resolve the Arthropod Tree of Life

Arthropods were the first animals to conquer land and air. They encompass more than three quarters of all described living species. This extraordinary evolutionary success is based on an astoundingly wide array of highly adaptive body organizations. A lack of robustly resolved phylogenetic relationships, however, currently impedes the reliable reconstruction of the underlying evolutionary processes. Here, we show that phylogenomic data can substantially advance our understanding of arthropod evolution and resolve several conflicts among existing hypotheses. We assembled a data set of 233 taxa and 775 genes from which an optimally informative data set of 117 taxa and 129 genes was finally selected using new heuristics and compared with the unreduced data set. We included novel expressed sequence tag (EST) data for 11 species and all published phylogenomic data augmented by recently published EST data on taxonomically important arthropod taxa. This thorough sampling reduces the chance of obtaining spurious results due to stochastic effects of undersampling taxa and genes. Orthology prediction of genes, alignment masking tools, and selection of most informative genes due to a balanced taxa-gene ratio using new heuristics were established. Our optimized data set robustly resolves major arthropod relationships. We received strong support for a sister group relationship of onychophorans and euarthropods and strong support for a close association of tardigrades and cycloneuralia. Within pancrustaceans, our analyses yielded paraphyletic crustaceans and monophyletic hexapods and robustly resolved monophyletic endopterygote insects. However, our analyses also showed for few deep splits that were recently thought to be resolved, for example, the position of myriapods, a remarkable sensitivity to methods of analyses.

Innate Immune Signaling Pathways in Reactome (94.13)

Abstract Toll-like receptors (TLR) play a key role in the innate immune system, providing vertebrates with a critical first line of defense against invading microbial pathogens. With the increasing body of molecular data relating to TLR function, organizing this information is useful to biologists and immunologists. We have used Reactome (www.reactome.org), a manually curated pathway database, to organize TLR data so that it can be viewed like an electronic textbook. TLR signaling cascades were authored by experts, maintained by the Reactome editorial staff and cross-referenced to publicly available bioinformatics resources. The Reactome data model generalizes the concept of a biochemical reaction to encompass any transformation of an input set of physical entities into an output set, and thus allows us to integrate TLR cascades with other innate immune processes, adaptive immunity, metabolic pathways, and other signaling processes. Since TLR-signaling molecules and pathways and the signaling pathways they initiate are highly conserved, Reactome provides pathway inference for 22 species based upon improved orthology prediction methods. A new entity-level pathway viewer and pathway analysis tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. All data content and software can be freely used and redistributed under open source terms.

ETE: a python Environment for Tree Exploration

BackgroundMany bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.ResultsHere we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE's programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations.ConclusionsETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from http://ete.cgenomics.org.

A New Approach to Find Orthologous Proteins Using Sequence and Protein-Protein Interaction Similarity

Developed proteome-scale ortholog and paralog prediction methods are mainly based on sequence similarity. However, it is known that even the closest BLAST hit often does not mean the closest neighbor. For this reason, we added conserved interaction information to find orthologs. We propose a genome-scale, automated ortholog prediction method, named OrthoInterBlast. The method is based on both sequence and interaction similarity. When we applied this method to fly and yeast, 17% of the ortholog candidates were different compared with the results of Inparanoid. By adding protein-protein interaction information, proteins that have low sequence similarity still can be selected as orthologs, which can not be easily detected by sequence homology alone.

Benchmarking Next-Generation Transcriptome Sequencing for Functional and Evolutionary Genomics

Next-generation sequencing has opened the door to genomic analysis of nonmodel organisms. Technologies generating long-sequence reads (200-400 bp) are increasingly used in evolutionary studies of nonmodel organisms, but the short-sequence reads (30-50 bp) that can be produced at lower cost are thought to be of limited utility for de novo sequencing applications. Here, we tested this assumption by short-read sequencing the transcriptomes of the tropical disease vectors Aedes aegypti and Anopheles gambiae, for which complete genome sequences are available. Comparison of our results to the reference genomes allowed us to accurately evaluate the quantity, quality, and functional and evolutionary information content of our "test" data. We produced more than 0.7 billion nucleotides of sequenced data per species that assembled into more than 21,000 test contigs larger than 100 bp per species and covered approximately 27% of the Aedes reference transcriptome. Remarkably, the substitution error rate in the test contigs was approximately 0.25% per site, with very few indels or assembly errors. Test contigs of both species were enriched for genes involved in energy production and protein synthesis and underrepresented in genes involved in transcription and differentiation. Ortholog prediction using the test contigs was accurate across hundreds of millions of years of evolution. Our results demonstrate the considerable utility of short-read transcriptome sequencing for genomic studies of nonmodel organisms and suggest an approach for assessing the information content of next-generation data for evolutionary studies.

Probabilistic Orthology Analysis

Orthology analysis aims at identifying orthologous genes and gene products from different organisms and, therefore, is a powerful tool in modern computational and experimental biology. Although reconciliation-based orthology methods are generally considered more accurate than distance-based ones, the traditional parsimony-based implementation of reconciliation-based orthology analysis (most parsimonious reconciliation [MPR]) suffers from a number of shortcomings. For example, 1) it is limited to orthology predictions from the reconciliation that minimizes the number of gene duplication and loss events, 2) it cannot evaluate the support of this reconciliation in relation to the other reconciliations, and 3) it cannot make use of prior knowledge (e.g., about species divergence times) that provides auxiliary information for orthology predictions. We present a probabilistic approach to reconciliation-based orthology analysis that addresses all these issues by estimating orthology probabilities. The method is based on the gene evolution model, an explicit evolutionary model for gene duplication and gene loss inside a species tree, that generalizes the standard birth-death process. We describe the probabilistic approach to orthology analysis using 2 experimental data sets and show that the use of orthology probabilities allows a more informative analysis than MPR and, in particular, that it is less sensitive to taxon sampling problems. We generalize these anecdotal observations and show, using data generated under biologically realistic conditions, that MPR give false orthology predictions at a substantial frequency. Last, we provide a new orthology prediction method that allows an orthology and paralogy classification with any chosen sensitivity/specificity combination from the spectra of achievable combinations. We conclude that probabilistic orthology analysis is a strong and more advanced alternative to traditional orthology analysis and that it provides a framework for sophisticated comparative studies of processes in genome evolution.

OrthoSelect: a protocol for selecting orthologous groups in phylogenomics

BackgroundPhylogenetic studies using expressed sequence tags (EST) are becoming a standard approach to answer evolutionary questions. Such studies are usually based on large sets of newly generated, unannotated, and error-prone EST sequences from different species. A first crucial step in EST-based phylogeny reconstruction is to identify groups of orthologous sequences. From these data sets, appropriate target genes are selected, and redundant sequences are eliminated to obtain suitable sequence sets as input data for tree-reconstruction software. Generating such data sets manually can be very time consuming. Thus, software tools are needed that carry out these steps automatically.ResultsWe developed a flexible and user-friendly software pipeline, running on desktop machines or computer clusters, that constructs data sets for phylogenomic analyses. It automatically searches assembled EST sequences against databases of orthologous groups (OG), assigns ESTs to these predefined OGs, translates the sequences into proteins, eliminates redundant sequences assigned to the same OG, creates multiple sequence alignments of identified orthologous sequences and offers the possibility to further process this alignment in a last step by excluding potentially homoplastic sites and selecting sufficiently conserved parts. Our software pipeline can be used as it is, but it can also be adapted by integrating additional external programs. This makes the pipeline useful for non-bioinformaticians as well as to bioinformatic experts. The software pipeline is especially designed for ESTs, but it can also handle protein sequences.ConclusionOrthoSelect is a tool that produces orthologous gene alignments from assembled ESTs. Our tests show that OrthoSelect detects orthologs in EST libraries with high accuracy. In the absence of a gold standard for orthology prediction, we compared predictions by OrthoSelect to a manually created and published phylogenomic data set. Our tool was not only able to rebuild the data set with a specificity of 98%, but it detected four percent more orthologous sequences. Furthermore, the results OrthoSelect produces are in absolut agreement with the results of other programs, but our tool offers a significant speedup and additional functionality, e.g. handling of ESTs, computing sequence alignments, and refining them. To our knowledge, there is currently no fully automated and freely available tool for this purpose. Thus, OrthoSelect is a valuable tool for researchers in the field of phylogenomics who deal with large quantities of EST sequences. OrthoSelect is written in Perl and runs on Linux/Mac OS X. The tool can be downloaded at

Reactome - a knowledgebase of human biological pathways

AbstractReactome ("http://www.reactome.org":http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2921 human proteins, 2871 reactions and 4167 literature citations. This curated dataset is integrated with a functional interaction network assembled computationally from non-curated sources of information including protein-protein interactions, gene co-expression, and gene ontology annotations, providing access. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms. Reactome instances are cross-referenced to corresponding ones in databases including EntrezGene, OMIM, Ensembl, UniProt, the UCSC Genome Browser, KEGG, ChEBI, and Gene Ontology.

The Tree versus the Forest: The Fungal Tree of Life and the Topological Diversity within the Yeast Phylome

A recurrent topic in phylogenomics is the combination of various sequence alignments to reconstruct a tree that describes the evolutionary relationships within a group of species. However, such approach has been criticized for not being able to properly represent the topological diversity found among gene trees. To evaluate the representativeness of species trees based on concatenated alignments, we reconstruct several fungal species trees and compare them with the complete collection of phylogenies of genes encoded in the Saccharomyces cerevisiae genome. We found that, despite high levels of among-gene topological variation, the species trees do represent widely supported phylogenetic relationships. Most topological discrepancies between gene and species trees are concentrated in certain conflicting nodes. We propose to map such information on the species tree so that it accounts for the levels of congruence across the genome. We identified the lack of sufficient accuracy of current alignment and phylogenetic methods as an important source for the topological diversity encountered among gene trees. Finally, we discuss the implications of the high levels of topological variation for phylogeny-based orthology prediction strategies.