An integrative approach to ortholog prediction for disease-focused and other functional studies

Background Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. Results We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt webcite), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist webcite). Conclusions DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.Harvard CatalystNational Institutes of Health (U.S.) (NIH R01 GM067761)National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (5K08DK78361)Dana-Farber/Harvard Cancer Cente

A multilayer network approach for guiding drug repositioning in neglected diseases

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.Fil: Berenstein, Ariel José. Fundación Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; ArgentinaFil: Magariños, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Chernomoretz, Ariel. Fundación Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; ArgentinaFil: Fernandez Aguero, Maria Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentin

Identification And Functional Characterization Of Plant Small Secreted Proteins During Arbuscular Mycorrhizal Symbiosis

Plant small secreted proteins (SSPs) are sequences of 50 – 250 amino acids in size which are transported out of cells to fulfill multiple functions related to plant growth and development and response to various stresses. With the development of more accurate and affordable genome sequencing technology, an increasing number of SSPs have been predicted using diverse computational tools based on machine learning. Although experimentally validated plant SSPs are still limited, some studies have reported that plant SSPs can be induced and involved in mutualistic relationships between plants and microbes. In Chapter I, known SSPs and their functions in various plant species are reviewed. Additionally, current computational tools and experimental methods that have been widely applied to identify plant SSPs are summarized. A new, robust, and integrated pipeline to discover plant SSPs is proposed. Furthermore, strategies for elucidating the biological functions of SSPs in plants are discussed in Chapter I. Chapter II presents predicted SSPs from 60 plant species and elucidates the evolutionary convergence of changes in SSP sequences. Furthermore, the expression of SSPs induced by arbuscular mycorrhizal fungi (AMF) which correspond to the convergent abilityfor different plants to form mutualistic association with AMF are explored. Overall, this study provides insightful ideas to understand functions of plant SSPs that occur during symbiosis between plants and fungi

OrthoList: A Compendium of C. elegans Genes with Human Orthologs

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.We 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.We 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

Inference of gene-phenotype associations via protein-protein interaction and orthology

published_or_final_versio

Incorporating diverse data to improve genetic network alignment with IsoRank

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 26).To more accurately predict which genes from different species have the same function (orthologs), I extend the network-alignment algorithm IsoRank to simultaneously align multiple unrelated networks over the same set of nodes. In addition to the original protein-interaction networks, I align genetic-interaction networks, gene-expression correlations, and chromosome localization data to improve the functional similarity of aligned genes. Alignments are evaluated with consistency measurements of protein function within ortholog clusters, and with an information-retrieval statistic from a small set of known orthologs. Integrating these additional types of data is shown to improve IsoRank's predictions of classes of genes that have sparse coverage in the original protein-interaction networks.by Eric David Eisner.M.Eng

FlyBase at 25: looking to the future.

Since 1992, FlyBase (flybase.org) has been an essential online resource for the Drosophila research community. Concentrating on the most extensively studied species, Drosophila melanogaster, FlyBase includes information on genes (molecular and genetic), transgenic constructs, phenotypes, genetic and physical interactions, and reagents such as stocks and cDNAs. Access to data is provided through a number of tools, reports, and bulk-data downloads. Looking to the future, FlyBase is expanding its focus to serve a broader scientific community. In this update, we describe new features, datasets, reagent collections, and data presentations that address this goal, including enhanced orthology data, Human Disease Model Reports, protein domain search and visualization, concise gene summaries, a portal for external resources, video tutorials and the FlyBase Community Advisory Group

Alliance of Genome Resources Portal: unified model organism research platform

The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource

Alliance of Genome Resources Portal: unified model organism research platform

The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource