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1.
Nucleic Acids Res ; 2021 Nov 12.
Artigo em Inglês | MEDLINE | ID: mdl-34788843

RESUMO

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.

2.
Bioinformatics ; 2021 May 08.
Artigo em Inglês | MEDLINE | ID: mdl-33964129

RESUMO

MOTIVATION: GO Causal Activity Models (GO-CAMs) assemble individual associations of gene products with cellular components, molecular functions, and biological processes into causally linked activity flow models. Pathway databases such as the Reactome Knowledgebase create detailed molecular process descriptions of reactions and assemble them, based on sharing of entities between individual reactions into pathway descriptions. RESULTS: To convert the rich content of Reactome into GO-CAMs, we have developed a software tool, Pathways2GO, to convert the entire set of normal human Reactome pathways into GO-CAMs. This conversion yields standard GO annotations from Reactome content and supports enhanced quality control for both Reactome and GO, yielding a nearly seamless conversion between these two resources for the bioinformatics community. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

3.
Nucleic Acids Res ; 49(D1): D1452-D1463, 2021 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-33170273

RESUMO

Gramene (http://www.gramene.org), a knowledgebase founded on comparative functional analyses of genomic and pathway data for model plants and major crops, supports agricultural researchers worldwide. The resource is committed to open access and reproducible science based on the FAIR data principles. Since the last NAR update, we made nine releases; doubled the genome portal's content; expanded curated genes, pathways and expression sets; and implemented the Domain Informational Vocabulary Extraction (DIVE) algorithm for extracting gene function information from publications. The current release, #63 (October 2020), hosts 93 reference genomes-over 3.9 million genes in 122 947 families with orthologous and paralogous classifications. Plant Reactome portrays pathway networks using a combination of manual biocuration in rice (320 reference pathways) and orthology-based projections to 106 species. The Reactome platform facilitates comparison between reference and projected pathways, gene expression analyses and overlays of gene-gene interactions. Gramene integrates ontology-based protein structure-function annotation; information on genetic, epigenetic, expression, and phenotypic diversity; and gene functional annotations extracted from plant-focused journals using DIVE. We train plant researchers in biocuration of genes and pathways; host curated maize gene structures as tracks in the maize genome browser; and integrate curated rice genes and pathways in the Plant Reactome.


Assuntos
Bases de Dados Genéticas , Regulação da Expressão Gênica de Plantas , Genoma de Planta , Genômica/métodos , Proteínas de Plantas/genética , Plantas/genética , Produtos Agrícolas , Elementos de DNA Transponíveis , Duplicação Gênica , Ontologia Genética , Redes Reguladoras de Genes , Internet , Bases de Conhecimento , Redes e Vias Metabólicas , Anotação de Sequência Molecular , Oryza/genética , Oryza/metabolismo , Proteínas de Plantas/metabolismo , Plantas/classificação , Plantas/metabolismo , Poliploidia , Mapeamento de Interação de Proteínas , Software , Zea mays/genética , Zea mays/metabolismo
4.
Autophagy ; 17(6): 1543-1554, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-32486891

RESUMO

The 21st century has revealed much about the fundamental cellular process of autophagy. Autophagy controls the catabolism and recycling of various cellular components both as a constitutive process and as a response to stress and foreign material invasion. There is considerable knowledge of the molecular mechanisms of autophagy, and this is still growing as new modalities emerge. There is a need to investigate autophagy mechanisms reliably, comprehensively and conveniently. Reactome is a freely available knowledgebase that consists of manually curated molecular events (reactions) organized into cellular pathways (https://reactome.org/). Pathways/reactions in Reactome are hierarchically structured, graphically presented and extensively annotated. Data analysis tools, such as pathway enrichment, expression data overlay and species comparison, are also available. For customized analysis, information can also be programmatically queried. Here, we discuss the curation and annotation of the molecular mechanisms of autophagy in Reactome. We also demonstrate the value that Reactome adds to research by reanalyzing a previously published work on genome-wide CRISPR screening of autophagy components.Abbreviations: CMA: chaperone-mediated autophagy; GO: Gene Ontology; MA: macroautophagy; MI: microautophagy; MTOR: mechanistic target of rapamycin kinase; SQSTM1: sequestosome 1.

5.
Nucleic Acids Res ; 48(D1): D498-D503, 2020 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-31691815

RESUMO

The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations in a single consistent data model, an extended version of a classic metabolic map. Reactome functions both as an archive of biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. To extend our ability to annotate human disease processes, we have implemented a new drug class and have used it initially to annotate drugs relevant to cardiovascular disease. Our annotation model depends on external domain experts to identify new areas for annotation and to review new content. New web pages facilitate recruitment of community experts and allow those who have contributed to Reactome to identify their contributions and link them to their ORCID records. To improve visualization of our content, we have implemented a new tool to automatically lay out the components of individual reactions with multiple options for downloading the reaction diagrams and associated data, and a new display of our event hierarchy that will facilitate visual interpretation of pathway analysis results.


Assuntos
Bases de Dados de Compostos Químicos , Bases de Dados de Produtos Farmacêuticos , Bases de Conhecimento , Software , Genoma Humano , Humanos , Redes e Vias Metabólicas , Mapas de Interação de Proteínas , Transdução de Sinais
6.
Nucleic Acids Res ; 48(D1): D1093-D1103, 2020 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-31680153

RESUMO

Plant Reactome (https://plantreactome.gramene.org) is an open-source, comparative plant pathway knowledgebase of the Gramene project. It uses Oryza sativa (rice) as a reference species for manual curation of pathways and extends pathway knowledge to another 82 plant species via gene-orthology projection using the Reactome data model and framework. It currently hosts 298 reference pathways, including metabolic and transport pathways, transcriptional networks, hormone signaling pathways, and plant developmental processes. In addition to browsing plant pathways, users can upload and analyze their omics data, such as the gene-expression data, and overlay curated or experimental gene-gene interaction data to extend pathway knowledge. The curation team actively engages researchers and students on gene and pathway curation by offering workshops and online tutorials. The Plant Reactome supports, implements and collaborates with the wider community to make data and tools related to genes, genomes, and pathways Findable, Accessible, Interoperable and Re-usable (FAIR).


Assuntos
Biologia Computacional/métodos , Bases de Dados Genéticas , Genômica , Metabolômica , Plantas/genética , Plantas/metabolismo , Proteômica , Redes Reguladoras de Genes , Genômica/métodos , Humanos , Redes e Vias Metabólicas , Metabolômica/métodos , Proteômica/métodos , Transdução de Sinais , Navegador
7.
Database (Oxford) ; 20192019 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-31802127

RESUMO

Reactome is a manually curated, open-source, open-data knowledge base of biomolecular pathways. Reactome has always provided clear credit attribution for authors, curators and reviewers through fine-grained annotation of all three roles at the reaction and pathway level. These data are visible in the web interface and provided through the various data download formats. To enhance visibility and credit attribution for the work of authors, curators and reviewers, and to provide additional opportunities for Reactome community engagement, we have implemented key changes to Reactome: contributor names are now fully searchable in the web interface, and contributors can 'claim' their contributions to their ORCID profile with a few clicks. In addition, we are reaching out to domain experts to request their help in reviewing and editing Reactome pathways through a new 'Contribution' section, highlighting pathways which are awaiting community review. Database URL: https://reactome.org.


Assuntos
Curadoria de Dados , Transdução de Sinais , Interface Usuário-Computador
8.
Sci Rep ; 8(1): 6518, 2018 04 25.
Artigo em Inglês | MEDLINE | ID: mdl-29695735

RESUMO

Many bioinformatics resources with unique perspectives on the protein landscape are currently available. However, generating new knowledge from these resources requires interoperable workflows that support cross-resource queries. In this study, we employ federated queries linking information from the Protein Kinase Ontology, iPTMnet, Protein Ontology, neXtProt, and the Mouse Genome Informatics to identify key knowledge gaps in the functional coverage of the human kinome and prioritize understudied kinases, cancer variants and post-translational modifications (PTMs) for functional studies. We identify 32 functional domains enriched in cancer variants and PTMs and generate mechanistic hypotheses on overlapping variant and PTM sites by aggregating information at the residue, protein, pathway and species level from these resources. We experimentally test the hypothesis that S768 phosphorylation in the C-helix of EGFR is inhibitory by showing that oncogenic variants altering S768 phosphorylation increase basal EGFR activity. In contrast, oncogenic variants altering conserved phosphorylation sites in the 'hydrophobic motif' of PKCßII (S660F and S660C) are loss-of-function in that they reduce kinase activity and enhance membrane translocation. Our studies provide a framework for integrative, consistent, and reproducible annotation of the cancer kinomes.


Assuntos
Mutação/genética , Neoplasias/genética , Proteínas Quinases/genética , Processamento de Proteína Pós-Traducional/genética , Proteínas/genética , Animais , Células CHO , Células COS , Linhagem Celular , Chlorocebus aethiops , Biologia Computacional/métodos , Cricetulus , Ontologia Genética , Variação Genética/genética , Humanos , Camundongos , Fosforilação/genética
9.
PLoS Comput Biol ; 14(1): e1005968, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-29377902

RESUMO

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.


Assuntos
Biologia Computacional/métodos , Bases de Dados Factuais , Armazenamento e Recuperação da Informação , Gráficos por Computador , Humanos , Internet , Bases de Conhecimento , Software , Biologia de Sistemas , Interface Usuário-Computador
10.
J Allergy Clin Immunol ; 141(4): 1411-1416, 2018 04.
Artigo em Inglês | MEDLINE | ID: mdl-29378288

RESUMO

BACKGROUND: There is a wealth of biological pathway information available in the scientific literature, but it is spread across many thousands of publications. Alongside publications that contain definitive experimental discoveries are many others that have been dismissed as spurious, found to be irreproducible, or are contradicted by later results and consequently now considered controversial. Many descriptions and images of pathways are incomplete stylized representations that assume the reader is an expert and familiar with the established details of the process, which are consequently not fully explained. Pathway representations in publications frequently do not represent a complete, detailed, and unambiguous description of the molecules involved; their precise posttranslational state; or a full account of the molecular events they undergo while participating in a process. Although this might be sufficient to be interpreted by an expert reader, the lack of detail makes such pathways less useful and difficult to understand for anyone unfamiliar with the area and of limited use as the basis for computational models. OBJECTIVE: Reactome was established as a freely accessible knowledge base of human biological pathways. It is manually populated with interconnected molecular events that fully detail the molecular participants linked to published experimental data and background material by using a formal and open data structure that facilitates computational reuse. These data are accessible on a Web site in the form of pathway diagrams that have descriptive summaries and annotations and as downloadable data sets in several formats that can be reused with other computational tools. The entire database and all supporting software can be downloaded and reused under a Creative Commons license. METHODS: Pathways are authored by expert biologists who work with Reactome curators and editorial staff to represent the consensus in the field. Pathways are represented as interactive diagrams that include as much molecular detail as possible and are linked to literature citations that contain supporting experimental details. All newly created events undergo a peer-review process before they are added to the database and made available on the associated Web site. New content is added quarterly. RESULTS: The 63rd release of Reactome in December 2017 contains 10,996 human proteins participating in 11,426 events in 2,179 pathways. In addition, analytic tools allow data set submission for the identification and visualization of pathway enrichment and representation of expression profiles as an overlay on Reactome pathways. Protein-protein and compound-protein interactions from several sources, including custom user data sets, can be added to extend pathways. Pathway diagrams and analytic result displays can be downloaded as editable images, human-readable reports, and files in several standard formats that are suitable for computational reuse. Reactome content is available programmatically through a REpresentational State Transfer (REST)-based content service and as a Neo4J graph database. Signaling pathways for IL-1 to IL-38 are hierarchically classified within the pathway "signaling by interleukins." The classification used is largely derived from Akdis et al. CONCLUSION: The addition to Reactome of a complete set of the known human interleukins, their receptors, and established signaling pathways linked to annotations of relevant aspects of immune function provides a significant computationally accessible resource of information about this important family. This information can be extended easily as new discoveries become accepted as the consensus in the field. A key aim for the future is to increase coverage of gene expression changes induced by interleukin signaling.


Assuntos
Interleucinas/imunologia , Transdução de Sinais/imunologia , Bases de Dados Factuais , Humanos , Internet , Mapas de Interação de Proteínas/imunologia , Proteínas/imunologia , Software
11.
Bioinformatics ; 34(7): 1208-1214, 2018 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-29186351

RESUMO

Motivation: Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. Results: The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources. Availability and implementation: Reactome is available online at: https://reactome.org. The diagram viewer is part of the Reactome pathway browser (https://reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://reactome.org/dev/diagram/. The source code is freely available at: https://github.com/reactome-pwp/diagram. Contact: fabregat@ebi.ac.uk or hhe@ebi.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Biologia Computacional/métodos , Bases de Dados Factuais , Bases de Conhecimento , Redes e Vias Metabólicas , Software , Algoritmos , Humanos , Internet
12.
Nucleic Acids Res ; 46(D1): D649-D655, 2018 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-29145629

RESUMO

The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism, and other cellular processes as an ordered network of molecular transformations-an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression profiles or somatic mutation catalogues from tumor cells. To support the continued brisk growth in the size and complexity of Reactome, we have implemented a graph database, improved performance of data analysis tools, and designed new data structures and strategies to boost diagram viewer performance. To make our website more accessible to human users, we have improved pathway display and navigation by implementing interactive Enhanced High Level Diagrams (EHLDs) with an associated icon library, and subpathway highlighting and zooming, in a simplified and reorganized web site with adaptive design. To encourage re-use of our content, we have enabled export of pathway diagrams as 'PowerPoint' files.


Assuntos
Bases de Conhecimento , Redes e Vias Metabólicas , Gráficos por Computador , Bases de Dados de Compostos Químicos , Bases de Dados de Proteínas , Humanos , Internet , Anotação de Sequência Molecular , Transdução de Sinais , Interface Usuário-Computador
13.
Nucleic Acids Res ; 46(D1): D1181-D1189, 2018 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-29165610

RESUMO

Gramene (http://www.gramene.org) is a knowledgebase for comparative functional analysis in major crops and model plant species. The current release, #54, includes over 1.7 million genes from 44 reference genomes, most of which were organized into 62,367 gene families through orthologous and paralogous gene classification, whole-genome alignments, and synteny. Additional gene annotations include ontology-based protein structure and function; genetic, epigenetic, and phenotypic diversity; and pathway associations. Gramene's Plant Reactome provides a knowledgebase of cellular-level plant pathway networks. Specifically, it uses curated rice reference pathways to derive pathway projections for an additional 66 species based on gene orthology, and facilitates display of gene expression, gene-gene interactions, and user-defined omics data in the context of these pathways. As a community portal, Gramene integrates best-of-class software and infrastructure components including the Ensembl genome browser, Reactome pathway browser, and Expression Atlas widgets, and undergoes periodic data and software upgrades. Via powerful, intuitive search interfaces, users can easily query across various portals and interactively analyze search results by clicking on diverse features such as genomic context, highly augmented gene trees, gene expression anatomograms, associated pathways, and external informatics resources. All data in Gramene are accessible through both visual and programmatic interfaces.


Assuntos
Bases de Dados Genéticas , Regulação da Expressão Gênica de Plantas , Genômica/métodos , Bases de Conhecimento , Plantas/genética , Epigênese Genética , Ontologia Genética , Pesquisa em Genética , Variação Genética , Genoma de Planta , Redes e Vias Metabólicas/genética , Anotação de Sequência Molecular , Plantas/metabolismo , Software , Interface Usuário-Computador
14.
Bioinformatics ; 33(21): 3461-3467, 2017 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-29077811

RESUMO

Motivation: Reactome is a free, open-source, open-data, curated and peer-reviewed knowledge base of biomolecular pathways. Pathways are arranged in a hierarchical structure that largely corresponds to the GO biological process hierarchy, allowing the user to navigate from high level concepts like immune system to detailed pathway diagrams showing biomolecular events like membrane transport or phosphorylation. Here, we present new developments in the Reactome visualization system that facilitate navigation through the pathway hierarchy and enable efficient reuse of Reactome visualizations for users' own research presentations and publications. Results: For the higher levels of the hierarchy, Reactome now provides scalable, interactive textbook-style diagrams in SVG format, which are also freely downloadable and editable. Repeated diagram elements like 'mitochondrion' or 'receptor' are available as a library of graphic elements. Detailed lower-level diagrams are now downloadable in editable PPTX format as sets of interconnected objects. Availability and implementation: http://reactome.org. Contact: fabregat@ebi.ac.uk or hhe@ebi.ac.uk.


Assuntos
Fenômenos Biológicos , Bases de Conhecimento , Interface Usuário-Computador , Gráficos por Computador , Ontologia Genética , Internet , Bibliotecas , Transdução de Sinais
15.
BMC Bioinformatics ; 18(1): 142, 2017 Mar 02.
Artigo em Inglês | MEDLINE | ID: mdl-28249561

RESUMO

BACKGROUND: Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. RESULTS: Here, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user's sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. CONCLUSION: Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. The proposed pathway analysis approach is available in the Reactome production web site either via the AnalysisService for programmatic access or the user submission interface integrated into the PathwayBrowser. Reactome is an open data and open source project and all of its source code, including the one described here, is available in the AnalysisTools repository in the Reactome GitHub ( https://github.com/reactome/ ).


Assuntos
Biologia Computacional , Software , Algoritmos , Bases de Dados Factuais , Humanos , Ácidos Nucleicos/metabolismo , Proteínas/metabolismo
16.
Nucleic Acids Res ; 45(D1): D1029-D1039, 2017 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-27799469

RESUMO

Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic, developmental and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX.


Assuntos
Biologia Computacional/métodos , Bases de Dados Genéticas , Plantas/genética , Plantas/metabolismo , Ferramenta de Busca , Genômica/métodos , Redes e Vias Metabólicas , Transdução de Sinais , Biologia de Sistemas/métodos , Interface Usuário-Computador , Navegador
17.
Nucleic Acids Res ; 45(D1): D339-D346, 2017 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-27899649

RESUMO

The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translational modification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities.


Assuntos
Biologia Computacional/métodos , Bases de Dados Genéticas , Proteínas , Animais , Humanos , Proteínas/química , Proteínas/genética , Navegador
18.
Artigo em Inglês | MEDLINE | ID: mdl-27589964

RESUMO

The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes in the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.


Assuntos
Metabolismo dos Carboidratos/fisiologia , Bases de Dados Genéticas , Ontologia Genética , Modelos Biológicos , Animais , Humanos
19.
RNA ; 22(5): 667-76, 2016 May.
Artigo em Inglês | MEDLINE | ID: mdl-26917558

RESUMO

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual).


Assuntos
Guias como Assunto , MicroRNAs/genética , Animais , Inativação Gênica , Humanos , Camundongos
20.
Curr Plant Biol ; 7-8: 10-15, 2016 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-28713666

RESUMO

Gramene (http://www.gramene.org) is an online, open source, curated resource for plant comparative genomics and pathway analysis designed to support researchers working in plant genomics, breeding, evolutionary biology, system biology, and metabolic engineering. It exploits phylogenetic relationships to enrich the annotation of genomic data and provides tools to perform powerful comparative analyses across a wide spectrum of plant species. It consists of an integrated portal for querying, visualizing and analyzing data for 44 plant reference genomes, genetic variation data sets for 12 species, expression data for 16 species, curated rice pathways and orthology-based pathway projections for 66 plant species including various crops. Here we briefly describe the functions and uses of the Gramene database.

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