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

Proteinortho: Detection of (Co-)orthologs in large-scale analysis

<p>Abstract</p> <p>Background</p> <p>Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases.</p> <p>Results</p> <p>The program <monospace>Proteinortho</monospace> described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply <monospace>Proteinortho</monospace> to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes.</p> <p>Conclusions</p> <p><monospace>Proteinortho</monospace> significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware.</p

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

<p>Abstract</p> <p>Background</p> <p>In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present <it>clusterMaker</it>, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. <it>clusterMaker </it>is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.</p> <p>Results</p> <p>Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast <it>Saccharomyces cerevisiae</it>; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.</p> <p>Conclusions</p> <p>The Cytoscape plugin <it>clusterMaker </it>provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the <it>clusterMaker </it>plugin. <it>clusterMaker </it>is available via the Cytoscape plugin manager.</p

Comparison of the protein-coding genomes of three deep-sea, sulfur-oxidising bacteria: “Candidatus Ruthia magnifica”, “Candidatus Vesicomyosocius okutanii” and Thiomicrospira crunogena

Abstract Objective “ Candidatus Ruthia magnifica”, “Candidatus Vesicomyosocius okutanii” and Thiomicrospira crunogena are all sulfur-oxidising bacteria found in deep-sea vent environments. Recent research suggests that the two symbiotic organisms, “Candidatus R. magnifica” and “Candidatus V. okutanii”, may share common ancestry with the autonomously living species T. crunogena. We used comparative genomics to examine the genome-wide protein-coding content of all three species to explore their similarities. In particular, we used the OrthoMCL algorithm to sort proteins into groups of putative orthologs on the basis of sequence similarity. Results The OrthoMCL inflation parameter was tuned using biological criteria. Using the tuned value, OrthoMCL delimited 1070 protein groups. 63.5% of these groups contained one protein from each species. Two groups contained duplicate protein copies from all three species. 123 groups were unique to T. crunogena and ten groups included multiple copies of T. crunogena proteins but only single copies from the other species. “Candidatus R. magnifica” had one unique group, and had multiple copies in one group where the other species had a single copy. There were no groups unique to “Candidatus V. okutanii”, and no groups in which there were multiple “Candidatus V. okutanii” proteins but only single proteins from the other species. Results align with previous suggestions that all three species share a common ancestor. However this is not definitive evidence to make taxonomic conclusions and the possibility of horizontal gene transfer was not investigated. Methodologically, the tuning of the OrthoMCL inflation parameter using biological criteria provides further methods to refine the OrthoMCL procedure

BranchClust: a phylogenetic algorithm for selecting gene families

BACKGROUND: Automated methods for assembling families of orthologous genes include those based on sequence similarity scores and those based on phylogenetic approaches. The first are easy to automate but usually they do not distinguish between paralogs and orthologs or have restriction on the number of taxa. Phylogenetic methods often are based on reconciliation of a gene tree with a known rooted species tree; a limitation of this approach, especially in case of prokaryotes, is that the species tree is often unknown, and that from the analyses of single gene families the branching order between related organisms frequently is unresolved. RESULTS: Here we describe an algorithm for the automated selection of orthologous genes that recognizes orthologous genes from different species in a phylogenetic tree for any number of taxa. The algorithm is capable of distinguishing complete (containing all taxa) and incomplete (not containing all taxa) families and recognizes in- and outparalogs. The BranchClust algorithm is implemented in Perl with the use of the BioPerl module for parsing trees and is freely available at . CONCLUSION: BranchClust outperforms the Reciprocal Best Blast hit method in selecting more sets of putatively orthologous genes. In the test cases examined, the correctness of the selected families and of the identified in- and outparalogs was confirmed by inspection of the pertinent phylogenetic trees

De novo single-nucleotide and copy number variation in discordant monozygotic twins reveals disease-related genes

Recent studies have demonstrated genetic differences between monozygotic (MZ) twins. To test the hypothesis that early post-twinning mutational events associate with phenotypic discordance, we investigated a cohort of 13 twin pairs (n = 26) discordant for various clinical phenotypes using whole-exome sequencing and screened for copy number variation (CNV). We identified a de novo variant in PLCB1, a gene involved in the hydrolysis of lipid phosphorus in milk from dairy cows, associated with lactase non-persistence, and a variant in the mitochondrial complex I gene MT-ND5 associated with amyotrophic lateral sclerosis (ALS). We also found somatic variants in multiple genes (TMEM225B, KBTBD3, TUBGCP4, TFIP11) in another MZ twin pair discordant for ALS. Based on the assumption that discordance between twins could be explained by a common variant with variable penetrance or expressivity, we screened the twin samples for known pathogenic variants that are shared and identified a rare deletion overlapping ARHGAP11B, in the twin pair manifesting with either schizotypal personality disorder or schizophrenia. Parent-offspring trio analysis was implemented for two twin pairs to assess potential association of variants of parental origin with susceptibility to disease. We identified a de novo variant in RASD2 shared by 8-year-old male twins with a suspected diagnosis of autism spectrum disorder (ASD) manifesting as different traits. A de novo CNV duplication was also identified in these twins overlapping CD38, a gene previously implicated in ASD. In twins discordant for Tourette's syndrome, a paternally inherited stop loss variant was detected in AADAC, a known candidate gene for the disorder

Phylogenetic and functional marker genes to study ammonia-oxidizing microorganisms (AOM) in the environment

The oxidation of ammonia plays a significant role in the transformation of fixed nitrogen in the global nitrogen cycle. Autotrophic ammonia oxidation is known in three groups of microorganisms. Aerobic ammonia-oxidizing bacteria and archaea convert ammonia into nitrite during nitrification. Anaerobic ammonia-oxidizing bacteria (anammox) oxidize ammonia using nitrite as electron acceptor and producing atmospheric dinitrogen. The isolation and cultivation of all three groups in the laboratory are quite problematic due to their slow growth rates, poor growth yields, unpredictable lag phases, and sensitivity to certain organic compounds. Culture-independent approaches have contributed importantly to our understanding of the diversity and distribution of these microorganisms in the environment. In this review, we present an overview of approaches that have been used for the molecular study of ammonia oxidizers and discuss their application in different environments