Abstract
The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely ...adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the ‘GO ribbon’ widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.
The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein ...Analysis Workbench and make it ready for the next 20 years. The main focus of our recent website upgrade work has been the acceleration of analyses in the face of increasing protein sequence database size. We additionally discuss any new software, the new hardware infrastructure, our webservices and web site. Lastly we survey updates to some of the key predictive algorithms available through our website.
Understanding the mechanisms of hip disease, such as osteoarthritis (OA), is crucial to advance their treatment. Such hip diseases often involve specific morphological changes. Genetic variations, ...called single nucleotide polymorphisms (SNPs), influence various hip morphological parameters. This study investigated the biological relevance of SNPs correlated to hip morphology in genome-wide association studies (GWAS). The SNP-associated genes were compared to genes associated with OA in other joints, aiming to see if the same genes play a role in both hip development and the risk of OA in other lower limb joints.
A systematic literature review was conducted to identify SNPs correlated with hip morphology, based on the Population, Intervention, Comparison, Outcome, and Study (PICOS) framework. Afterwards, Gene Ontology (GO) analysis was performed, using EnrichR, on the SNP-associated genes and compared with non-hip OA-associated genes, across different databases.
Reviewing 49 GWAS identified 436 SNPs associated with hip joint morphology, encompassing variance in bone size, structure and shape. Among the SNP-associated genes, SOX9 plays a pivotal role in size, GDF5 impacts bone structure, and BMP7 affects shape. Overall, skeletal system development, regulation of cell differentiation, and chondrocyte differentiation emerged as crucial processes influencing hip morphology. Eighteen percent of GWAS-identified genes related to hip morphology were also associated with non-hip OA.
Our findings indicate the existence of multiple shared genetic mechanisms across hip morphology and OA, highlighting the necessity for more extensive research in this area, as in contrast to the hip, the genetic background on knee or foot morphology remains largely understudied.
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, ...accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org.
Abstract
Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, ...but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
Abstract
PANTHER (Protein Analysis Through Evolutionary Relationships, http://www.pantherdb.org) is a resource for the evolutionary and functional classification of protein-coding genes from all ...domains of life. The evolutionary classification is based on a library of over 15,000 phylogenetic trees, and the functional classifications include Gene Ontology terms and pathways. Here, we analyze the current coverage of genes from genomes in different taxonomic groups, so that users can better understand what to expect when analyzing a gene list using PANTHER tools. We also describe extensive improvements to PANTHER made in the past two years. The PANTHER Protein Class ontology has been completely refactored, and 6101 PANTHER families have been manually assigned to a Protein Class, providing a high level classification of protein families and their genes. Users can access the TreeGrafter tool to add their own protein sequences to the reference phylogenetic trees in PANTHER, to infer evolutionary context as well as fine-grained annotations. We have added human enhancer-gene links that associate non-coding regions with the annotated human genes in PANTHER. We have also expanded the available services for programmatic access to PANTHER tools and data via application programming interfaces (APIs). Other improvements include additional plant genomes and an updated PANTHER GO-slim.
The Gene Ontology (GO; http://www.geneontology.org) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological ...knowledge. Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology.
Abstract
miRBase catalogs, names and distributes microRNA gene sequences. The latest release of miRBase (v22) contains microRNA sequences from 271 organisms: 38 589 hairpin precursors and 48 860 ...mature microRNAs. We describe improvements to the database and website to provide more information about the quality of microRNA gene annotations, and the cellular functions of their products. We have collected 1493 small RNA deep sequencing datasets and mapped a total of 5.5 billion reads to microRNA sequences. The read mapping patterns provide strong support for the validity of between 20% and 65% of microRNA annotations in different well-studied animal genomes, and evidence for the removal of >200 sequences from the database. To improve the availability of microRNA functional information, we are disseminating Gene Ontology terms annotated against miRBase sequences. We have also used a text-mining approach to search for microRNA gene names in the full-text of open access articles. Over 500 000 sentences from 18 542 papers contain microRNA names. We score these sentences for functional information and link them with 12 519 microRNA entries. The sentences themselves, and word clouds built from them, provide effective summaries of the functional information about specific microRNAs. miRBase is publicly and freely available at http://mirbase.org/.
Abstract
PANTHER (Protein Analysis Through Evolutionary Relationships, http://pantherdb.org) is a resource for the evolutionary and functional classification of genes from organisms across the tree ...of life. We report the improvements we have made to the resource during the past two years. For evolutionary classifications, we have added more prokaryotic and plant genomes to the phylogenetic gene trees, expanding the representation of gene evolution in these lineages. We have refined many protein family boundaries, and have aligned PANTHER with the MEROPS resource for protease and protease inhibitor families. For functional classifications, we have developed an entirely new PANTHER GO-slim, containing over four times as many Gene Ontology terms as our previous GO-slim, as well as curated associations of genes to these terms. Lastly, we have made substantial improvements to the enrichment analysis tools available on the PANTHER website: users can now analyze over 900 different genomes, using updated statistical tests with false discovery rate corrections for multiple testing. The overrepresentation test is also available as a web service, for easy addition to third-party sites.
A long-standing effort in biology is to precisely define and group phenotypes that characterize a biological process, and the genes that underpin them. In
and other organisms, functional screens have ...generated rich lists of phenotypes associated with individual genes. However, it is often challenging to identify sets of phenotypes and genes that are most closely associated with a given biological process. Here, we focused on the 166 phenotypes arising from loss-of-function and the 86 phenotypes from gain-of-function mutations in 571 genes currently assigned to cell cycle-related ontologies in
To reduce this complexity, we applied unbiased, computational approaches of correspondence analysis to identify a minimum set of phenotypic variables that accounts for as much of the variability in the data as possible. Loss-of-function phenotypes can be reduced to 20 dimensions, while gain-of-function ones to 14 dimensions. We also pinpoint the contributions of phenotypes and genes in each set. The approach we describe not only simplifies the categorization of phenotypes associated with cell cycle progression but might also potentially serve as a discovery tool for gene function.
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