Orthology Assignment Research Articles

Aggregation of Similarity Measures for Ortholog Detection: Validation with Measures Based on Rough Set Theory

En el presente trabajo se propone un algoritmo para la detección de ortólogos que utiliza la agregación de medidas de similitud para caracterizar la relación entre los pares de genes de dos genomas. Las medidas se basan en la puntuación del alineamiento, la longitud de las secuencias, la pertenencia a regiones conservadas y el perfil físico-químico de las proteínas. La fase de agrupamiento sobre el grafo bipartido de similitudes se realiza con el algoritmo de agrupamiento de Markov (MCL). Se define una política de asignación de ortólogos a partir de los grupos de homología obtenidos del agrupamiento. La clasificación se valida con los genomas de Saccharomyces Cerevisiae y de Schizosaccharomyces Pombe usando la lista de ortólogos del algoritmo INPARANOID 7.0, con la medida de validación externa ARI. También se aplican medidas de validación empleando la teoría de conjuntos aproximados para medir la calidad con manejo del desbalance de las clases.

Transcriptomic Analysis of the Rice White Tip Nematode, Aphelenchoides besseyi (Nematoda: Aphelenchoididae)

BackgroundThe rice white tip nematode Aphelenchoides besseyi, a devastating nematode whose genome has not been sequenced, is distributed widely throughout almost all the rice-growing regions of the world. The aims of the present study were to define the transcriptome of A. besseyi and to identify parasite-related, mortality-related or host resistance-overcoming genes in this nematode.Methodology and Principal FindingsUsing Solexa/Illumina sequencing, we profiled the transcriptome of mixed-stage populations of A. besseyi. A total of 51,270 transcripts without gaps were produced based on high-quality clean reads. Of all the A. besseyi transcripts, 9,132 KEGG Orthology assignments were annotated. Carbohydrate-active enzymes of glycoside hydrolases (GHs), glycosyltransferases (GTs), carbohydrate esterases (CEs) and carbohydrate-binding modules (CBMs) were identified. The presence of the A. besseyi GH45 cellulase gene was verified by in situ hybridization. Given that 13 unique A. besseyi potential effector genes were identified from 41 candidate effector homologs, further studies of these homologs are merited. Finally, comparative analyses were conducted between A. besseyi contigs and Caenorhabditis elegans genes to look for orthologs of RNAi phenotypes, neuropeptides and peptidases.Conclusions and SignificanceThe present results provide comprehensive insight into the genetic makeup of A. besseyi. Many of this species' genes are parasite related, nematode mortality-related or necessary to overcome host resistance. The generated transcriptome dataset of A. besseyi reported here lays the foundation for further studies of the molecular mechanisms related to parasitism and facilitates the development of new control strategies for this species.

IPathCons and iPathDB: an improved insect pathway construction tool and the database.

Insects are one of the most successful animal groups on earth. Some insects, such as the silkworm and honeybee, are beneficial to humans, whereas others are notorious pests of crops. At present, the genomes of 38 insects have been sequenced and made publically available. In addition, the transcriptomes of dozens of insects have been sequenced. As gene data rapidly accumulate, constructing the pathway of molecular interactions becomes increasingly important for entomological research. Here, we developed an improved tool, iPathCons, for knowledge-based construction of pathways from the transcriptomes or the official gene sets of genomes. Considering the high evolution diversity in insects, iPathCons uses a voting system for Kyoto Encyclopedia of Genes and Genomes Orthology assignment. Both stand-alone software and a web server of iPathCons are provided. Using iPathCons, we constructed the pathways of molecular interactions of 52 insects, including 37 genome-sequenced and 15 transcriptome-sequenced ones. These pathways are available in the iPathDB, which provides searches, web server, data downloads, etc. This database will be highly useful for the insect research community.Database URL: http://ento.njau.edu.cn/ipath/

Can RNA-Seq resolve the rapid radiation of advanced moths and butterflies (Hexapoda: Lepidoptera: Apoditrysia)? An exploratory study.

Recent molecular phylogenetic studies of the insect order Lepidoptera have robustly resolved family-level divergences within most superfamilies, and most divergences among the relatively species-poor early-arising superfamilies. In sharp contrast, relationships among the superfamilies of more advanced moths and butterflies that comprise the mega-diverse clade Apoditrysia (ca. 145,000 spp.) remain mostly poorly supported. This uncertainty, in turn, limits our ability to discern the origins, ages and evolutionary consequences of traits hypothesized to promote the spectacular diversification of Apoditrysia. Low support along the apoditrysian “backbone” probably reflects rapid diversification. If so, it may be feasible to strengthen resolution by radically increasing the gene sample, but case studies have been few. We explored the potential of next-generation sequencing to conclusively resolve apoditrysian relationships. We used transcriptome RNA-Seq to generate 1579 putatively orthologous gene sequences across a broad sample of 40 apoditrysians plus four outgroups, to which we added two taxa from previously published data. Phylogenetic analysis of a 46-taxon, 741-gene matrix, resulting from a strict filter that eliminated ortholog groups containing any apparent paralogs, yielded dramatic overall increase in bootstrap support for deeper nodes within Apoditrysia as compared to results from previous and concurrent 19-gene analyses. High support was restricted mainly to the huge subclade Obtectomera broadly defined, in which 11 of 12 nodes subtending multiple superfamilies had bootstrap support of 100%. The strongly supported nodes showed little conflict with groupings from previous studies, and were little affected by changes in taxon sampling, suggesting that they reflect true signal rather than artifacts of massive gene sampling. In contrast, strong support was seen at only 2 of 11 deeper nodes among the “lower”, non-obtectomeran apoditrysians. These represent a much harder phylogenetic problem, for which one path to resolution might include further increase in gene sampling, together with improved orthology assignments.

EggNOG v4.0: nested orthology inference across 3686 organisms

With the increasing availability of various ‘omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at http://eggnog.embl.de) that derives nonsupervised orthologous groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. Compared with the previous version, we have more than tripled the underlying species set to cover 3686 organisms, keeping track with genome project completions while prioritizing the inclusion of high-quality genomes to minimize error propagation from incomplete proteome sets. Major technological advances include (i) a robust and scalable procedure for the identification and inclusion of high-quality genomes, (ii) provision of orthologous groups for 107 different taxonomic levels compared with 41 in eggNOGv3, (iii) identification and annotation of particularly closely related orthologous groups, facilitating analysis of related gene families, (iv) improvements of the clustering and functional annotation approach, (v) adoption of a revised tree building procedure based on the multiple alignments generated during the process and (vi) implementation of quality control procedures throughout the entire pipeline. As in previous versions, eggNOGv4 provides multiple sequence alignments and maximum-likelihood trees, as well as broad functional annotation. Users can access the complete database of orthologous groups via a web interface, as well as through bulk download.

Biggest challenges in bioinformatics

The third Heidelberg Unseminars in Bioinformatics (HUB) was held on 18th October 2012, at Heidelberg University, Germany. HUB brought together around 40 bioinformaticians from academia and industry to discuss the 'Biggest Challenges in Bioinformatics' in a 'World Café' style event.

The Eimeria Transcript DB: an integrated resource for annotated transcripts of protozoan parasites of the genus Eimeria

Parasites of the genus Eimeria infect a wide range of vertebrate hosts, including chickens. We have recently reported a comparative analysis of the transcriptomes of Eimeria acervulina, Eimeria maxima and Eimeria tenella, integrating ORESTES data produced by our group and publicly available Expressed Sequence Tags (ESTs). All cDNA reads have been assembled, and the reconstructed transcripts have been submitted to a comprehensive functional annotation pipeline. Additional studies included orthology assignment across apicomplexan parasites and clustering analyses of gene expression profiles among different developmental stages of the parasites. To make all this body of information publicly available, we constructed the Eimeria Transcript Database (EimeriaTDB), a web repository that provides access to sequence data, annotation and comparative analyses. Here, we describe the web interface, available sequence data sets and query tools implemented on the site. The main goal of this work is to offer a public repository of sequence and functional annotation data of reconstructed transcripts of parasites of the genus Eimeria. We believe that EimeriaTDB will represent a valuable and complementary resource for the Eimeria scientific community and for those researchers interested in comparative genomics of apicomplexan parasites.Database URL: http://www.coccidia.icb.usp.br/eimeriatdb/

Construction of Plastid Reference Proteomes for Maize and Arabidopsis and Evaluation of Their Orthologous Relationships; The Concept of Orthoproteomics

Plastids are essential organelles because they contribute to primary and secondary metabolism and plant signaling networks. A high-quality inventory of the plastid proteome is therefore a critical tool in plant research. We present reference plastid proteomes for maize (Zea mays) and Arabidopsis (Arabidopsis thaliana) with, respectively, 1564 and 1559 proteins. This was based on manual curation of published experimental information, including >150 proteomics studies regarding different (sub)cellular fractions, and new quantitative proteomics experiments on plastid subfractions specifically designed to fill gaps in current knowledge. These plastid proteomes represent an estimated 40 (maize) to 50% (Arabidopsis) of all plastid proteins and can serve as a "gold standard" because of their low false-positive rate. To facilitate direct comparison of these plastid proteomes, identify "missing" proteins, and evaluate species-specific differences, we determined their orthologous relationships. The multistep strategy to best define these orthologous relationships is explained. Putative plastid locations for orthologs without known subcellular locations were inferred based on the robustness of orthology and weighing of experimental evidence, increasing both plastid proteome sizes. Examples that highlight differences and similarities between maize and Arabidopsis and underscore the quality of the orthology assignments are discussed.

RNA-seq analysis of the C. briggsae transcriptome.

Curation of a high-quality gene set is the critical first step in genome research, enabling subsequent analyses such as ortholog assignment, cis-regulatory element finding, and synteny detection. In this project, we have reannotated the genome of Caenorhabditis briggsae, the best studied sister species of the model organism Caenorhabditis elegans. First, we applied a homology-based gene predictor genBlastG to annotate the C. briggsae genome. We then validated and further improved the C. briggsae gene annotation through RNA-seq analysis of the C. briggsae transcriptome, which resulted in the first validated C. briggsae gene set (23,159 genes), among which 7347 genes (33.9% of all genes with introns) have all of their introns confirmed. Most genes (14,812, or 68.3%) have at least one intron validated, compared with only 3.9% in the most recent WormBase release (WS228). Of all introns in the revised gene set (103,083), 61,503 (60.1%) have been confirmed. Additionally, we have identified numerous trans-splicing leaders (SL1 and SL2 variants) in C. briggsae, leading to the first genome-wide annotation of operons in C. briggsae (1105 operons). The majority of the annotated operons (564, or 51.0%) are perfectly conserved in C. elegans, with an additional 345 operons (or 31.2%) somewhat divergent. Additionally, RNA-seq analysis revealed over 10 thousand small-size assembly errors in the current C. briggsae reference genome that can be readily corrected. The revised C. briggsae genome annotation represents a solid platform for comparative genomics analysis and evolutionary studies of Caenorhabditis species.

BBH-LS: an algorithm for computing positional homologs using sequence and gene context similarity

BackgroundIdentifying corresponding genes (orthologs) in different species is an important step in genome-wide comparative analysis. In particular, one-to-one correspondences between genes in different species greatly simplify certain problems such as transfer of function annotation and genome rearrangement studies. Positional homologs are the direct descendants of a single ancestral gene in the most recent common ancestor and by definition form one-to-one correspondence.ResultsIn this work, we present a simple yet effective method (BBH-LS) for the identification of positional homologs from the comparative analysis of two genomes. Our BBH-LS method integrates sequence similarity and gene context similarity in order to get more accurate ortholog assignments. Specifically, BBH-LS applies the bidirectional best hit heuristic to a combination of sequence similarity and gene context similarity scores.ConclusionWe applied our method to the human, mouse, and rat genomes and found that BBH-LS produced the best results when using both sequence and gene context information equally. Compared to the state-of-the-art algorithms, such as MSOAR2, BBH-LS is able to identify more positional homologs with fewer false positives.

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.

EggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges.

Orthologous relationships form the basis of most comparative genomic and metagenomic studies and are essential for proper phylogenetic and functional analyses. The third version of the eggNOG database (http://eggnog.embl.de) contains non-supervised orthologous groups constructed from 1133 organisms, doubling the number of genes with orthology assignment compared to eggNOG v2. The new release is the result of a number of improvements and expansions: (i) the underlying homology searches are now based on the SIMAP database; (ii) the orthologous groups have been extended to 41 levels of selected taxonomic ranges enabling much more fine-grained orthology assignments; and (iii) the newly designed web page is considerably faster with more functionality. In total, eggNOG v3 contains 721 801 orthologous groups, encompassing a total of 4 396 591 genes. Additionally, we updated 4873 and 4850 original COGs and KOGs, respectively, to include all 1133 organisms. At the universal level, covering all three domains of life, 101 208 orthologous groups are available, while the others are applicable at 40 more limited taxonomic ranges. Each group is amended by multiple sequence alignments and maximum-likelihood trees and broad functional descriptions are provided for 450 904 orthologous groups (62.5%).

Genomic distance under gene substitutions

BackgroundThe distance between two genomes is often computed by comparing only the common markers between them. Some approaches are also able to deal with non-common markers, allowing the insertion or the deletion of such markers. In these models, a deletion and a subsequent insertion that occur at the same position of the genome count for two sorting steps.ResultsHere we propose a new model that sorts non-common markers with substitutions, which are more powerful operations that comprehend insertions and deletions. A deletion and an insertion that occur at the same position of the genome can be modeled as a substitution, counting for a single sorting step.ConclusionsComparing genomes with unequal content, but without duplicated markers, we give a linear time algorithm to compute the genomic distance considering substitutions and double-cut-and-join (DCJ) operations. This model provides a parsimonious genomic distance to handle genomes free of duplicated markers, that is in practice a lower bound to the real genomic distances. The method could also be used to refine orthology assignments, since in some cases a substitution could actually correspond to an unannotated orthology.

Whole genome duplication: challenges and considerations associated with sequence orthology assignment in Salmoninae

To illustrate some of the challenges and considerations in assigning correct orthology necessary for any comparative genomic investigation among salmonids, sequence data from the non-coding regions of different chromosomes in three members of the subfamily Salmoninae, rainbow trout Oncorhynchus mykiss, Atlantic salmon Salmo salar and Arctic charr Salvelinus alpinus, were compared. By analysing c. 55 distinct loci, corresponding to c. 142 kbp sequence information per species, 18 duplicated patterns representative of the two sequential rounds of teleost-specific whole genome duplications (i.e. 3R and 4R WGD) were identified. Sequence similarities between the 4R paralogues were c. 90%, which was slightly lower than those of the 4R orthologues and c. 60% for the 3R products. Through careful examination of the sequence data, however, only 14 loci could reliably be assigned as true orthologues. Locus-specific trees were constructed through maximum parsimony, maximum likelihood and neighbour-joining methods and were rooted using the information from a close relative, lake whitefish Coregonus clupeaformis. All approaches generated congruent trees supporting the {Coregonus [Salmo (Oncorhynchus, Salvelinus)]} topology. The general phenotypic characteristics of sequences, however, were highly suggestive of the basal position of Oncorhynchus, raising the hypothesis of an accelerated rate of nucleotide evolution in this species.

A systematic analysis ofDrosophilaTUDOR domain-containing proteins identifies Vreteno and the Tdrd12 family as essential primary piRNA pathway factors

PIWI proteins and their bound PIWI-interacting RNAs (piRNAs) form the core of a gonad-specific small RNA silencing pathway that protects the animal genome against the deleterious activity of transposable elements. Recent studies linked the piRNA pathway to TUDOR biology as TUDOR domains of various proteins bind symmetrically methylated Arginine residues in PIWI proteins. We systematically analysed the Drosophila TUDOR protein family and identified four previously not characterized TUDOR domain-containing proteins (CG4771, CG14303, CG11133 and CG31755) as essential piRNA pathway factors. We characterized CG4771 (Vreteno) in detail and demonstrate a critical role for this protein in primary piRNA biogenesis. Vreteno physically and/or genetically interacts with the primary pathway components Piwi, Armitage, Yb and Zucchini. Vreteno also interacts with the Tdrd12 orthologues CG11133 (Brother of Yb) and CG31755 (Sister of Yb), which are essential for the primary piRNA pathway in the germline and probably replace the function of the related but soma-specific factor Yb.

Orthology prediction methods: A quality assessment using curated protein families

The increasing number of sequenced genomes has prompted the development of several automated orthology prediction methods. Tests to evaluate the accuracy of predictions and to explore biases caused by biological and technical factors are therefore required. We used 70 manually curated families to analyze the performance of five public methods in Metazoa. We analyzed the strengths and weaknesses of the methods and quantified the impact of biological and technical challenges. From the latter part of the analysis, genome annotation emerged as the largest single influencer, affecting up to 30% of the performance. Generally, most methods did well in assigning orthologous group but they failed to assign the exact number of genes for half of the groups. The publicly available benchmark set (http://eggnog.embl.de/orthobench/) should facilitate the improvement of current orthology assignment protocols, which is of utmost importance for many fields of biology and should be tackled by a broad scientific community.

Phylogenetic relationships within the Opisthokonta based on phylogenomic analyses of conserved single-copy protein domains.

Many of the eukaryotic phylogenomic analyses published to date were based on alignments of hundreds to thousands of genes. Frequently, in such analyses, the most realistic evolutionary models currently available are often used to minimize the impact of systematic error. However, controversy remains over whether or not idiosyncratic gene family dynamics (i.e., gene duplications and losses) and incorrect orthology assignments are always appropriately taken into account. In this paper, we present an innovative strategy for overcoming orthology assignment problems. Rather than identifying and eliminating genes with paralogy problems, we have constructed a data set comprised exclusively of conserved single-copy protein domains that, unlike most of the commonly used phylogenomic data sets, should be less confounded by orthology miss-assignments. To evaluate the power of this approach, we performed maximum likelihood and Bayesian analyses to infer the evolutionary relationships within the opisthokonts (which includes Metazoa, Fungi, and related unicellular lineages). We used this approach to test 1) whether Filasterea and Ichthyosporea form a clade, 2) the interrelationships of early-branching metazoans, and 3) the relationships among early-branching fungi. We also assessed the impact of some methods that are known to minimize systematic error, including reducing the distance between the outgroup and ingroup taxa or using the CAT evolutionary model. Overall, our analyses support the Filozoa hypothesis in which Ichthyosporea are the first holozoan lineage to emerge followed by Filasterea, Choanoflagellata, and Metazoa. Blastocladiomycota appears as a lineage separate from Chytridiomycota, although this result is not strongly supported. These results represent independent tests of previous phylogenetic hypotheses, highlighting the importance of sophisticated approaches for orthology assignment in phylogenomic analyses.

Conceptual framework and pilot study to benchmark phylogenomic databases based on reference gene trees

Phylogenomic databases provide orthology predictions for species with fully sequenced genomes. Although the goal seems well-defined, the content of these databases differs greatly. Seven ortholog databases (Ensembl Compara, eggNOG, HOGENOM, InParanoid, OMA, OrthoDB, Panther) were compared on the basis of reference trees. For three well-conserved protein families, we observed a generally high specificity of orthology assignments for these databases. We show that differences in the completeness of predicted gene relationships and in the phylogenetic information are, for the great majority, not due to the methods used, but to differences in the underlying database concepts. According to our metrics, none of the databases provides a fully correct and comprehensive protein classification. Our results provide a framework for meaningful and systematic comparisons of phylogenomic databases. In the future, a sustainable set of ‘Gold standard’ phylogenetic trees could provide a robust method for phylogenomic databases to assess their current quality status, measure changes following new database releases and diagnose improvements subsequent to an upgrade of the analysis procedure.

Comparative genomics approach to detecting split-coding regions in a low-coverage genome: lessons from the chimaera Callorhinchus milii (Holocephali, Chondrichthyes)

Recent development of deep sequencing technologies has facilitated de novo genome sequencing projects, now conducted even by individual laboratories. However, this will yield more and more genome sequences that are not well assembled, and will hinder thorough annotation when no closely related reference genome is available. One of the challenging issues is the identification of protein-coding sequences split into multiple unassembled genomic segments, which can confound orthology assignment and various laboratory experiments requiring the identification of individual genes. In this study, using the genome of a cartilaginous fish, Callorhinchus milii, as test case, we performed gene prediction using a model specifically trained for this genome. We implemented an algorithm, designated ESPRIT, to identify possible linkages between multiple protein-coding portions derived from a single genomic locus split into multiple unassembled genomic segments. We developed a validation framework based on an artificially fragmented human genome, improvements between early and recent mouse genome assemblies, comparison with experimentally validated sequences from GenBank, and phylogenetic analyses. Our strategy provided insights into practical solutions for efficient annotation of only partially sequenced (low-coverage) genomes. To our knowledge, our study is the first formulation of a method to link unassembled genomic segments based on proteomes of relatively distantly related species as references.

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.