MultiMSOAR 2.0: an accurate tool to identify ortholog groups among multiple genomes

The identification of orthologous genes shared by multiple genomes plays an important role in evolutionary studies and gene functional analyses. Based on a recently developed accurate tool, called MSOAR 2.0, for ortholog assignment between a pair of closely related genomes based on genome rearrangement, we present a new system MultiMSOAR 2.0, to identify ortholog groups among multiple genomes in this paper. In the system, we construct gene families for all the genomes using sequence similarity search and clustering, run MSOAR 2.0 for all pairs of genomes to obtain the pairwise orthology relationship, and partition each gene family into a set of disjoint sets of orthologous genes (called super ortholog groups or SOGs) such that each SOG contains at most one gene from each genome. For each such SOG, we label the leaves of the species tree using 1 or 0 to indicate if the SOG contains a gene from the corresponding species or not. The resulting tree is called a tree of ortholog groups (or TOGs). We then label the internal nodes of each TOG based on the parsimony principle and some biological constraints. Ortholog groups are finally identified from each fully labeled TOG. In comparison with a popular tool MultiParanoid on simulated data, MultiMSOAR 2.0 shows significantly higher prediction accuracy. It also outperforms MultiParanoid, the Roundup multi-ortholog repository and the Ensembl ortholog database in real data experiments using gene symbols as a validation tool. In addition to ortholog group identification, MultiMSOAR 2.0 also provides information about gene births, duplications and losses in evolution, which may be of independent biological interest. Our experiments on simulated data demonstrate that MultiMSOAR 2.0 is able to infer these evolutionary events much more accurately than a well-known software tool Notung. The software MultiMSOAR 2.0 is available to the public for free.     

Clustering of main orthologs for multiple genomes

The identification of orthologous genes shared by multiple genomes is critical for both functional and evolutionary studies in comparative genomics. While it is usually done by sequence similarity search and reconciled tree construction in practice, recently a new combinatorial approach and high-throughput system MSOAR for ortholog identification between closely related genomes based on genome rearrangement and gene duplication has been proposed in Fu et al. MSOAR assumes that orthologous genes correspond to each other in the most parsimonious evolutionary scenario, minimizing the number of genome rearrangement and (postspeciation) gene duplication events. However, the parsimony approach used by MSOAR limits it to pairwise genome comparisons. In this paper, we extend MSOAR to multiple (closely related) genomes and propose an ortholog clustering method, called MultiMSOAR, to infer main orthologs in multiple genomes. As a preliminary experiment, we apply MultiMSOAR to rat, mouse, and human genomes, and validate our results using gene annotations and gene function classifications in the public databases. We further compare our results to the ortholog clusters predicted by MultiParanoid, which is an extension of the well-known program InParanoid for pairwise genome comparisons. The comparison reveals that MultiMSOAR gives more detailed and accurate orthology information, since it can effectively distinguish main orthologs from inparalogs.     

Ortholog clustering on a multipartite graph

We present a method for automatically extracting groups of orthologous genes from a large set of genomes by a new clustering algorithm on a weighted multipartite graph. The method assigns a score to an arbitrary subset of genes from multiple genomes to assess the orthologous relationships between genes in the subset. This score is computed using sequence similarities between the member genes and the phylogenetic relationship between the corresponding genomes. An ortholog cluster is found as the subset with the highest score, so ortholog clustering is formulated as a combinatorial optimization problem. The algorithm for finding an ortholog cluster runs in time O(|E| + |V| log |V|), where V and E are the sets of vertices and edges, respectively, in the graph. However, if we discretize the similarity scores into a constant number of bins, the runtime improves to O(|E| + |V|). The proposed method was applied to seven complete eukaryote genomes on which the manually curated database of eukaryotic ortholog clusters, KOG, is constructed. A comparison of our results with the manually curated ortholog clusters shows that our clusters are well correlated with the existing clusters     

Roundup: a multi-genome repository of orthologs and evolutionary distances

SUMMARY: We have created a tool for ortholog and phylogenetic profile retrieval called Roundup. Roundup is backed by a massive repository of orthologs and associated evolutionary distances that was built using the reciprocal smallest distance algorithm, an approach that has been shown to improve upon alternative approaches of ortholog detection, such as reciprocal blast. Presently, the Roundup repository contains all possible pair-wise comparisons for over 250 genomes, including 32 Eukaryotes, more than doubling the coverage of any similar resource. The orthologs are accessible through an intuitive web interface that allows searches by genome or gene identifier, presenting results as phylogenetic profiles together with gene and molecular function annotations. Results may be downloaded as phylogenetic matrices for subsequent analysis, including the construction of whole-genome phylogenies based on gene-content data. AVAILABILITY: http://rodeo.med.harvard.edu/tools/roundup.     

Automatic clustering of orthologs and inparalogs shared by multiple proteomes

MOTIVATION: The complete sequencing of many genomes has made it possible to identify orthologous genes descending from a common ancestor. However, reconstruction of evolutionary history over long time periods faces many challenges due to gene duplications and losses. Identification of orthologous groups shared by multiple proteomes therefore becomes a clustering problem in which an optimal compromise between conflicting evidences needs to be found. RESULTS: Here we present a new proteome-scale analysis program called MultiParanoid that can automatically find orthology relationships between proteins in multiple proteomes. The software is an extension of the InParanoid program that identifies orthologs and inparalogs in pairwise proteome comparisons. MultiParanoid applies a clustering algorithm to merge multiple pairwise ortholog groups from InParanoid into multi-species ortholog groups. To avoid outparalogs in the same cluster, MultiParanoid only combines species that share the same last ancestor. To validate the clustering technique, we compared the results to a reference set obtained by manual phylogenetic analysis. We further compared the results to ortholog groups in KOGs and OrthoMCL, which revealed that MultiParanoid produces substantially fewer outparalogs than these resources. AVAILABILITY: MultiParanoid is a freely available standalone program that enables efficient orthology analysis much needed in the post-genomic era. A web-based service providing access to the original datasets, the resulting groups of orthologs, and the source code of the program can be found at http://multiparanoid.cgb.ki.se.     

Automatic detection of conserved gene clusters in multiple genomes by graph comparison and P-quasi grouping

We previously reported two graph algorithms for analysis of genomic information: a graph comparison algorithm to detect locally similar regions called correlated clusters and an algorithm to find a graph feature called P-quasi complete linkage. Based on these algorithms we have developed an automatic procedure to detect conserved gene clusters and align orthologous gene orders in multiple genomes. In the first step, the graph comparison is applied to pairwise genome comparisons, where the genome is considered as a one-dimensionally connected graph with genes as its nodes, and correlated clusters of genes that share sequence similarities are identified. In the next step, the P-quasi complete linkage analysis is applied to grouping of related clusters and conserved gene clusters in multiple genomes are identified. In the last step, orthologous relations of genes are established among each conserved cluster. We analyzed 17 completely sequenced microbial genomes and obtained 2313 clusters when the completeness parameter P was 40%. About one quarter contained at least two genes that appeared in the metabolic and regulatory pathways in the KEGG database. This collection of conserved gene clusters is used to refine and augment ortholog group tables in KEGG and also to define ortholog identifiers as an extension of EC numbers.     

OrthoFocus: program for identification of orthologs in multiple genomes in family-focused studies

The identification of orthologs to a set of known genes is often the starting point for evolutionary studies focused on gene families of interest. To date, the existing orthology detection tools (COG, InParanoid, OrthoMCL, etc.) are aimed at genome-wide ortholog identification and lack flexibility for the purposes of case studies. We developed a program OrthoFocus, which employs an extended reciprocal best hit approach to quickly search for orthologs in a pair of genomes. A group of paralogs from the input genome is used as the start for the forward search and the criterion for the reverse search, which allows handling many-to-one and many-to-many relationships. By pairwise comparison of genomes with the input species genome, OrthoFocus enables quick identification of orthologs in multiple genomes and generates a multiple alignment of orthologs so that it can further be used in phylogenetic analysis. The program is available at http://www.lipidomics.ru/.     

ReMark: an automatic program for clustering orthologs flexibly combining a Recursive and a Markov clustering algorithms

SUMMARY: ReMark is a fully automatic tool for clustering orthologs by combining a Recursive and a Markov clustering (MCL) algorithms. The ReMark detects and recursively clusters ortholog pairs through reciprocal BLAST best hits between multiple genomes running software program (RecursiveClustering.java) in the first step. Then, it employs MCL algorithm to compute the clusters (score matrices generated from the previous step) and refines the clusters by adjusting an inflation factor running software program (MarkovClustering.java). This method has two key features. One utilizes, to get more reliable results, the diagonal scores in the matrix of the initial ortholog clusters. Another clusters orthologs flexibly through being controlled naturally by MCL with a selected inflation factor. Users can therefore select the fitting state of orthologous protein clusters by regulating the inflation factor according to their research interests. AVAILABILITY AND IMPLEMENTATION: Source code for the orthologous protein clustering software is freely available for non-commercial use at http://dasan.sejong.ac.kr/~wikim/notice.html, implemented in Java 1.6 and supported on Windows and Linux.     

Calculating orthologs in bacteria and Archaea: a divide and conquer approach

Among proteins, orthologs are defined as those that are derived by vertical descent from a single progenitor in the last common ancestor of their host organisms. Our goal is to compute a complete set of protein orthologs derived from all currently available complete bacterial and archaeal genomes. Traditional approaches typically rely on all-against-all BLAST searching which is prohibitively expensive in terms of hardware requirements or computational time (requiring an estimated 18 months or more on a typical server). Here, we present xBASE-Orth, a system for ongoing ortholog annotation, which applies a "divide and conquer" approach and adopts a pragmatic scheme that trades accuracy for speed. Starting at species level, xBASE-Orth carefully constructs and uses pan-genomes as proxies for the full collections of coding sequences at each level as it progressively climbs the taxonomic tree using the previously computed data. This leads to a significant decrease in the number of alignments that need to be performed, which translates into faster computation, making ortholog computation possible on a global scale. Using xBASE-Orth, we analyzed an NCBI collection of 1,288 bacterial and 94 archaeal complete genomes with more than 4 million coding sequences in 5 weeks and predicted more than 700 million ortholog pairs, clustered in 175,531 orthologous groups. We have also identified sets of highly conserved bacterial and archaeal orthologs and in so doing have highlighted anomalies in genome annotation and in the proposed composition of the minimal bacterial genome. In summary, our approach allows for scalable and efficient computation of the bacterial and archaeal ortholog annotations. In addition, due to its hierarchical nature, it is suitable for incorporating novel complete genomes and alternative genome annotations. The computed ortholog data and a continuously evolving set of applications based on it are integrated in the xBASE database, available at http://www.xbase.ac.uk/.     

Appearance of new tetraspanin genes during vertebrate evolution

A detailed phylogenetic analysis of tetraspanins from 10 fully sequenced metazoan genomes and several fungal and protist genomes gives insight into their evolutionary origins and organization. Our analysis suggests that the superfamily can be divided into four large families. These four families-the CD family, CD63 family, uroplakin family, and RDS family-are further classified as consisting of several ortholog groups. The clustering of several ortholog groups together, such as the CD9/Tsp2/CD81 cluster, suggests functional relatedness of those ortholog groups. The fact that our studies are based on whole genome analysis enabled us to estimate not only the phylogenetic relationships among the tetraspanins, but also the first appearance in the tree of life of certain tetraspanin ortholog groups. Taken together, our data suggest that the tetraspanins are derived from a single (or a few) ancestral gene(s) through sequence divergence, rather than convergence, and that the majority of tetraspanins found in the human genome are vertebrate (21 instances), tetrapod (4 instances), or mammalian (6 instances) inventions.     

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.     

GeConT: gene context analysis

SUMMARY: The fact that adjacent genes in bacteria are often functionally related is widely known. GeConT (Gene Context Tool) is a web interface designed to visualize genome context of a gene or a group of genes and their orthologs in all the completely sequenced genomes. The graphical information of GeConT can be used to analyze genome annotation, functional ortholog identification or to verify the genomic context congruence of any set of genes that share a common property. AVAILABILITY: http://www.ibt.unam.mx/biocomputo/gecont.html     

Multiple Horizontal Gene Transfer Events and Domain Fusions Have Created Novel Regulatory and Metabolic Networks in the Oomycete Genome

Complex enzymes with multiple catalytic activities are hypothesized to have evolved from more primitive precursors. Global analysis of the Phytophthora sojae genome using conservative criteria for evaluation of complex proteins identified 273 novel multifunctional proteins that were also conserved in P. ramorum. Each of these proteins contains combinations of protein motifs that are not present in bacterial, plant, animal, or fungal genomes. A subset of these proteins were also identified in the two diatom genomes, but the majority of these proteins have formed after the split between diatoms and oomycetes. Documentation of multiple cases of domain fusions that are common to both oomycetes and diatom genomes lends additional support for the hypothesis that oomycetes and diatoms are monophyletic. Bifunctional proteins that catalyze two steps in a metabolic pathway can be used to infer the interaction of orthologous proteins that exist as separate entities in other genomes. We postulated that the novel multifunctional proteins of oomycetes could function as potential Rosetta Stones to identify interacting proteins of conserved metabolic and regulatory networks in other eukaryotic genomes. However ortholog analysis of each domain within our set of 273 multifunctional proteins against 39 sequenced bacterial and eukaryotic genomes, identified only 18 candidate Rosetta Stone proteins. Thus the majority of multifunctional proteins are not Rosetta Stones, but they may nonetheless be useful in identifying novel metabolic and regulatory networks in oomycetes. Phylogenetic analysis of all the enzymes in three pathways with one or more novel multifunctional proteins was conducted to determine the probable origins of individual enzymes. These analyses revealed multiple examples of horizontal transfer from both bacterial genomes and the photosynthetic endosymbiont in the ancestral genome of Stramenopiles. The complexity of the phylogenetic origins of these metabolic pathways and the paucity of Rosetta Stones relative to the total number of multifunctional proteins suggests that the proteome of oomycetes has few features in common with other Kingdoms.     

Efficient large-scale protein sequence comparison and gene matching to identify orthologs and co-orthologs

Broadly, computational approaches for ortholog assignment is a three steps process: (i) identify all putative homologs between the genomes, (ii) identify gene anchors and (iii) link anchors to identify best gene matches given their order and context. In this article, we engineer two methods to improve two important aspects of this pipeline [specifically steps (ii) and (iii)]. First, computing sequence similarity data [step (i)] is a computationally intensive task for large sequence sets, creating a bottleneck in the ortholog assignment pipeline. We have designed a fast and highly scalable sort-join method (afree) based on k-mer counts to rapidly compare all pairs of sequences in a large protein sequence set to identify putative homologs. Second, availability of complex genomes containing large gene families with prevalence of complex evolutionary events, such as duplications, has made the task of assigning orthologs and co-orthologs difficult. Here, we have developed an iterative graph matching strategy where at each iteration the best gene assignments are identified resulting in a set of orthologs and co-orthologs. We find that the afree algorithm is faster than existing methods and maintains high accuracy in identifying similar genes. The iterative graph matching strategy also showed high accuracy in identifying complex gene relationships. Standalone afree available from http://vbc.med.monash.edu.au/～kmahmood/afree. EGM2, complete ortholog assignment pipeline (including afree and the iterative graph matching method) available from http://vbc.med.monash.edu.au/～kmahmood/EGM2.     

COMPAM :visualization of combining pairwise alignments for multiple genomes

COMPAM is a tool for visualizing relationships among multiple whole genomes by combining all pairwise genome alignments. It displays shared conserved regions (blocks) and where these blocks occur (edges) as block relation graphs which can be explored interactively. An unannotated genome, e.g. can then be explored using information from well-annotated genomes, COG-based genome annotation and genes. COMPAM can run either as a stand-alone application or through an applet that is provided as service to PLATCOM, a toolset for whole genome comparative analysis, where a wide variety of genomes can be easily selected. Features provided by COMPAM include the ability to export genome relationship information into file formats that can be used by other existing tools. AVAILABILITY: http://bio.informatics.indiana.edu/projects/compam/     

BACA: a mitochondrial genome retriever,organizer and visualizer

Retrieving and organizing data from complete genomes is a time‐consuming task, even more so if the interest lies only in part of the genome (for nongenomic analysis). Furthermore, when comparing several genomes or genes, data retrieval has to be repeated multiple times. We present baca , a software for retrieving, organizing and visualizing multiple mitochondrial genomes. baca takes a GenBank query, retrieves all related genomes and generates multiple fasta files organized both by genomes and genes. A web‐based user interface and an interactive graphical map of all genomes with all genes are also provided. The program is available from http://cibio.up.pt/software/baca .     

Assessing performance of orthology detection strategies applied to eukaryotic genomes

Orthology detection is critically important for accurate functional annotation, and has been widely used to facilitate studies on comparative and evolutionary genomics. Although various methods are now available, there has been no comprehensive analysis of performance, due to the lack of a genomic-scale 'gold standard' orthology dataset. Even in the absence of such datasets, the comparison of results from alternative methodologies contains useful information, as agreement enhances confidence and disagreement indicates possible errors. Latent Class Analysis (LCA) is a statistical technique that can exploit this information to reasonably infer sensitivities and specificities, and is applied here to evaluate the performance of various orthology detection methods on a eukaryotic dataset. Overall, we observe a trade-off between sensitivity and specificity in orthology detection, with BLAST-based methods characterized by high sensitivity, and tree-based methods by high specificity. Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species. Among methods that permit clustering of ortholog groups spanning multiple genomes, the (automated) OrthoMCL algorithm exhibits better within-group consistency with respect to protein function and domain architecture than the (manually curated) KOG database, and the homolog clustering algorithm TribeMCL as well. By way of using LCA, we are also able to comprehensively assess similarities and statistical dependence between various strategies, and evaluate the effects of parameter settings on performance. In summary, we present a comprehensive evaluation of orthology detection on a divergent set of eukaryotic genomes, thus providing insights and guides for method selection, tuning and development for different applications. Many biological questions have been addressed by multiple tests yielding binary (yes/no) outcomes but no clear definition of truth, making LCA an attractive approach for computational biology.     

Dating the early evolution of plants: detection and molecular clock analyses of orthologs

Orthologs generally are under selective pressure against loss of function, while paralogs usually accumulate mutations and finally die or deviate in terms of function or regulation. Most ortholog detection methods contaminate the resulting datasets with a substantial amount of paralogs. Therefore we aimed to implement a straightforward method that allows the detection of ortholog clusters with a reduced amount of paralogs from completely sequenced genomes. The described cross-species expansion of the reciprocal best BLAST hit method is a time-effective method for ortholog detection, which results in 68% truly orthologous clusters and the procedure specifically enriches single-copy orthologs. The detection of true orthologs can provide a phylogenetic toolkit to better understand evolutionary processes. In a study across six photosynthetic eukaryotes, nuclear genes of putative mitochondrial origin were shown to be over-represented among single copy orthologs. These orthologs are involved in fundamental biological processes like amino acid metabolism or translation. Molecular clock analyses based on this dataset yielded divergence time estimates for the red/green algae (1,142 MYA), green algae/land plant (725 MYA), mosses/seed plant (496 MYA), gymno-/angiosperm (385 MYA) and monocotyledons/core eudicotyledons (301 MYA) divergence times. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.