High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn.

/pdf/kobas-2-0-a-web-server-for-annotation-and-identification-of-krnh95v775.pdf

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

The Universal Protein Resource (UniProt) provides the scientific community with a single, centralized, authoritative resource for protein sequences and functional information. Formed by uniting the Swiss-Prot, TrEMBL and PIR protein database activities, the UniProt consortium produces three layers of protein sequence databases: the UniProt Archive (UniParc), the UniProt Knowledgebase (UniProt) and the UniProt Reference (UniRef) databases. The UniProt Knowledgebase is a comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase with extensive cross-references. This centrepiece consists of two sections: UniProt/Swiss-Prot, with fully, manually curated entries; and UniProt/TrEMBL, enriched with automated classification and annotation. During 2004, tens of thousands of Knowledgebase records got manually annotated or updated; we introduced a new comment line topic: TOXIC DOSE to store information on the acute toxicity of a toxin; the UniProt keyword list got augmented by additional keywords; we improved the documentation of the keywords and are continuously overhauling and standardizing the annotation of post-translational modifications. Furthermore, we introduced a new documentation file of the strains and their synonyms. Many new database cross-references were introduced and we started to make use of Digital Object Identifiers. We also achieved in collaboration with the Macromolecular Structure Database group at EBI an improved integration with structural databases by residue level mapping of sequences from the Protein Data Bank entries onto corresponding UniProt entries. For convenient sequence searches we provide the UniRef non-redundant sequence databases. The comprehensive UniParc database stores the complete body of publicly available protein sequence data. The UniProt databases can be accessed online (http://www.uniprot.org) or downloaded in several formats (ftp://ftp.uniprot.org/pub). New releases are published every two weeks.

https://archive-ouverte.unige.ch/unige:38246/ATTACHMENT01

The Universal Protein Resource (UniProt)

The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

/pdf/disgenet-a-comprehensive-platform-integrating-information-on-8zrzm0m8xl.pdf

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

We present a DNA library preparation method that has allowed us to reconstruct a high-coverage (30×) genome sequence of a Denisovan, an extinct relative of Neandertals. The quality of this genome allows a direct estimation of Denisovan heterozygosity indicating that genetic diversity in these archaic hominins was extremely low. It also allows tentative dating of the specimen on the basis of “missing evolution” in its genome, detailed measurements of Denisovan and Neandertal admixture into present-day human populations, and the generation of a near-complete catalog of genetic changes that swept to high frequency in modern humans since their divergence from Denisovans.

https://science.sciencemag.org/content/sci/338/6104/222.full.pdf

A high-coverage genome sequence from an archaic Denisovan individual

Genomics, epigenomics, transcriptomics, proteomics and metabolomics efforts rapidly generate a plethora of data on the activity and levels of biomolecules within mammalian cells. At the same time, curation projects that organize knowledge from the biomedical literature into online databases are expanding. Hence, there is a wealth of information about genes, proteins and their associations, with an urgent need for data integration to achieve better knowledge extraction and data reuse. For this purpose, we developed the Harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins from over 70 major online resources. We extracted, abstracted and organized data into ∼72 million functional associations between genes/proteins and their attributes. Such attributes could be physical relationships with other biomolecules, expression in cell lines and tissues, genetic associations with knockout mouse or human phenotypes, or changes in expression after drug treatment. We stored these associations in a relational database along with rich metadata for the genes/proteins, their attributes and the original resources. The freely available Harmonizome web portal provides a graphical user interface, a web service and a mobile app for querying, browsing and downloading all of the collected data. To demonstrate the utility of the Harmonizome, we computed and visualized gene-gene and attribute-attribute similarity networks, and through unsupervised clustering, identified many unexpected relationships by combining pairs of datasets such as the association between kinase perturbations and disease signatures. We also applied supervised machine learning methods to predict novel substrates for kinases, endogenous ligands for G-protein coupled receptors, mouse phenotypes for knockout genes, and classified unannotated transmembrane proteins for likelihood of being ion channels. The Harmonizome is a comprehensive resource of knowledge about genes and proteins, and as such, it enables researchers to discover novel relationships between biological entities, as well as form novel data-driven hypotheses for experimental validation.Database URL: http://amp.pharm.mssm.edu/Harmonizome.

/pdf/the-harmonizome-a-collection-of-processed-datasets-gathered-2p59rm4moi.pdf

The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins.

To the editor: The increasing availability of polymorphism data has allowed more gene association studies to be carried out and the number of published genetic association studies is growing rapidly. Studies done secondarily to successful linkage studies over the last decade have also fueled the increase in published association studies. Although there are single-nucleotide polymorphism and human variation databases 1,2 , there is currently no public repository for genetic association data. It is difficult to query association data in a systematic manner or to integrate association data with other molecular databases. OMIM 3 , the main repository of genetic information for mendelian disorders, is largely text based and is of a historical narrative design, making it difficult to compare large sets of molecular data. Moreover, OMIM archives mature, high-quality data of high significance, the standard in rare mendelian disorders. Although this data is useful, OMIM does not routinely collect findings of lower significance or negative findings. The study of nonmendelian, common complex disorders is often a struggle to find disease relevance with lower significance values, and often conflicting evidence. Negative data are often not reported or are marginalized into obscure and less accessible scientific journals, resulting in a publication bias favoring positive genetic associations 4. Here, we describe the development of a genetic association database (GAD; http://geneticassociationdb.nih.gov) that aims to collect, standardize and archive genetic association study data and to make it easily accessible to the scientific community. There are no standards for designing, implementing, interpreting or reporting association studies (e.g., sample size, replication, significant P values), although guidelines have been suggested 4–7. The literature is filled with alternative, idiosyncratic and arbitrary gene names and gene symbols, as well as a continuum of phenotypic descriptions. Studies using arbitrary nomenclature continue to be published, making cross-comparison and meta-analysis difficult. One goal of GAD is to standardize molecular nomenclature in the archival process by including official HUGO gene symbols. After this assignment, each record is annotated with links to molecular databases (LocusLink, GeneCards, HapMap, etc.) and reference databases (PubMed, CDC), among others. Once they are standardized, integrating association data with other molecular databases, data mining tools, annotation and future sources of molecular data (e.g., gene interactions, quantitative trait loci) can be done systematically. Moreover, cross-comparison and meta-analysis of studies becomes more efficient. There are three main components of GAD: a web interface, Perl modules and the database, which uses the Oracle RDBMS. …

/pdf/the-genetic-association-database-28vnd4e4fn.pdf

The Genetic Association Database

The genetic contributions to common disease and complex disease phenotypes are pleiotropic, multifactorial, and combinatorial. Gene set analysis is a computational approach used in the analysis of microarray data to rapidly query gene combinations and multifactorial processes. Here we use novel gene sets based on population-based human genetic associations in common human disease or experimental genetic mouse models to analyze disease-related microarray studies. We developed a web-based analysis tool that uses these novel disease- and phenotype-related gene sets to analyze microarray-based gene expression data. These gene sets show disease and phenotype specificity in a species-specific and cross-species fashion. In this way, we integrate population-based common human disease genetics, mouse genetically determined phenotypes, and disease or phenotype structured ontologies, with gene expression studies relevant to human disease. This may aid in the translation of large-scale high-throughput datasets into the context of clinically relevant disease phenotypes.

Disease and phenotype gene set analysis of disease-based gene expression in mouse and human

The genetic contributions to human common disorders and mouse genetic models of disease are complex and often overlapping. In common human diseases, unlike classical Mendelian disorders, genetic factors generally have small effect sizes, are multifactorial, and are highly pleiotropic. Likewise, mouse genetic models of disease often have pleiotropic and overlapping phenotypes. Moreover, phenotypic descriptions in the literature in both human and mouse are often poorly characterized and difficult to compare directly. In this report, human genetic association results from the literature are summarized with regard to replication, disease phenotype, and gene specific results; and organized in the context of a systematic disease ontology. Similarly summarized mouse genetic disease models are organized within the Mammalian Phenotype ontology. Human and mouse disease and phenotype based gene sets are identified. These disease gene sets are then compared individually and in large groups through dendrogram analysis and hierarchical clustering analysis. Human disease and mouse phenotype gene sets are shown to group into disease and phenotypically relevant groups at both a coarse and fine level based on gene sharing. This analysis provides a systematic and global perspective on the genetics of common human disease as compared to itself and in the context of mouse genetic models of disease.

/pdf/systematic-analysis-comparison-and-integration-of-disease-22mi3kzewr.pdf

Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information

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The Genetic Association Database

Disease and phenotype gene set analysis of disease-based gene expression in mouse and human

Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information