RESUMO
Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.
Assuntos
Biologia Computacional/métodos , Software , Bases de Dados de Compostos Químicos , Bases de Dados Genéticas , Aprendizado Profundo , HumanosRESUMO
MOTIVATION: Understanding the protein structural context and patterning on proteins of genomic variants can help to separate benign from pathogenic variants and reveal molecular consequences. However, mapping genomic coordinates to protein structures is non-trivial, complicated by alternative splicing and transcript evidence. RESULTS: Here we present VarMap, a web tool for mapping a list of chromosome coordinates to canonical UniProt sequences and associated protein 3D structures, including validation checks, and annotating them with structural information. AVAILABILITY AND IMPLEMENTATION: https://www.ebi.ac.uk/thornton-srv/databases/VarMap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
Genômica , Software , Sequência de Aminoácidos , Bases de Dados de Proteínas , Anotação de Sequência Molecular , ProteínasRESUMO
Understanding the association of genetic variation with its functional consequences in proteins is essential for the interpretation of genomic data and identifying causal variants in diseases. Integration of protein function knowledge with genome annotation can assist in rapidly comprehending genetic variation within complex biological processes. Here, we describe mapping UniProtKB human sequences and positional annotations, such as active sites, binding sites, and variants to the human genome (GRCh38) and the release of a public genome track hub for genome browsers. To demonstrate the power of combining protein annotations with genome annotations for functional interpretation of variants, we present specific biological examples in disease-related genes and proteins. Computational comparisons of UniProtKB annotations and protein variants with ClinVar clinically annotated single nucleotide polymorphism (SNP) data show that 32% of UniProtKB variants colocate with 8% of ClinVar SNPs. The majority of colocated UniProtKB disease-associated variants (86%) map to 'pathogenic' ClinVar SNPs. UniProt and ClinVar are collaborating to provide a unified clinical variant annotation for genomic, protein, and clinical researchers. The genome track hubs, and related UniProtKB files, are downloadable from the UniProt FTP site and discoverable as public track hubs at the UCSC and Ensembl genome browsers.
Assuntos
Mapeamento Cromossômico/métodos , Bases de Dados Genéticas , Mutação de Sentido Incorreto , Proteínas/química , Sítios de Ligação , Bases de Dados de Proteínas , Predisposição Genética para Doença , Humanos , Anotação de Sequência Molecular , Polimorfismo de Nucleotídeo Único , Ligação Proteica , Proteínas/genética , Proteínas/metabolismo , Software , NavegadorRESUMO
The Proteins API provides searching and programmatic access to protein and associated genomics data such as curated protein sequence positional annotations from UniProtKB, as well as mapped variation and proteomics data from large scale data sources (LSS). Using the coordinates service, researchers are able to retrieve the genomic sequence coordinates for proteins in UniProtKB. This, the LSS genomics and proteomics data for UniProt proteins is programmatically only available through this service. A Swagger UI has been implemented to provide documentation, an interface for users, with little or no programming experience, to 'talk' to the services to quickly and easily formulate queries with the services and obtain dynamically generated source code for popular programming languages, such as Java, Perl, Python and Ruby. Search results are returned as standard JSON, XML or GFF data objects. The Proteins API is a scalable, reliable, fast, easy to use RESTful services that provides a broad protein information resource for users to ask questions based upon their field of expertise and allowing them to gain an integrated overview of protein annotations available to aid their knowledge gain on proteins in biological processes. The Proteins API is available at (http://www.ebi.ac.uk/proteins/api/doc).
Assuntos
ATPases Transportadoras de Cobre/genética , Genoma Humano , Degeneração Hepatolenticular/genética , Internet , Interface Usuário-Computador , Animais , Biomarcadores/metabolismo , ATPases Transportadoras de Cobre/metabolismo , Bases de Dados Genéticas , Expressão Gênica , Degeneração Hepatolenticular/metabolismo , Degeneração Hepatolenticular/patologia , Humanos , Camundongos , Anotação de Sequência Molecular , Proteômica/métodosRESUMO
Charcot-Marie-Tooth disease (CMT) is an inherited peripheral neuropathy that has been linked to mutations in multiple genes. Mutations in the neurofilament light (NFL) chain gene lead to the CMT2E form whereas mutations in the myotubularin-related protein 2 and 13 (MTMR2 and MTMR13) genes lead to the CMT4B form. These two forms share characteristic pathological hallmarks on nerve biopsies including concentric sheaths ('onion bulbs') and, in at least one case, myelin loops. In addition, MTMR2 protein has been shown to interact physically with both NFL and MTMR13. Here, we present evidence that CMT-linked mutations of MTMR2 can cause NFL aggregation in a cell line devoid of endogenous intermediate filaments, SW13vim(-). Mutations in the protein responsible for X-linked myotubular myopathy (myotubularin, MTM1) also induced NFL abnormalities in these cells. We also show that two MTMR2 mutant proteins, G103E and R283W, are unable to form dimers and undergo phosphorylation in vivo, implicating impaired complex formation in myotubularin-related pathology.