RESUMO
In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven't been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.
Assuntos
Biologia Computacional/métodos , Genótipo , Fenótipo , Algoritmos , Animais , Ontologias Biológicas , Bases de Dados Genéticas , Exoma , Estudos de Associação Genética , Variação Genética , Genômica , Humanos , Internet , Software , Pesquisa Translacional Biomédica , Interface Usuário-ComputadorRESUMO
Genome annotation is the process of identifying the location and function of a genome's encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, we present Apollo, an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform. Some of Apollo's newer user interface features include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible.
Assuntos
Biologia Computacional/métodos , Anotação de Sequência Molecular/métodos , Mapeamento Cromossômico/métodos , Sistemas de Gerenciamento de Base de Dados , Genoma/genética , Genômica , Armazenamento e Recuperação da Informação , Internet , Software , Interface Usuário-ComputadorRESUMO
The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.
Assuntos
Algoritmos , Doenças Genéticas Inatas/genética , Genoma Humano/genética , Mutação/genética , Frequência do Gene , Estudo de Associação Genômica Ampla , Humanos , Aprendizado de Máquina , Fases de Leitura Aberta/genética , Fenótipo , Mutação Puntual/genéticaRESUMO
Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.
Assuntos
Biologia Computacional/normas , Genômica/normas , Filogenia , Proteômica/normas , Archaea/classificação , Archaea/genética , Bactérias/classificação , Bactérias/genética , Biologia Computacional/métodos , Bases de Dados Genéticas , Eucariotos/classificação , Eucariotos/genética , Ontologia Genética , Genômica/métodos , Modelos Genéticos , Proteômica/métodos , Análise de Sequência de Proteína , Homologia de Sequência , Especificidade da EspécieRESUMO
The Quest for Orthologs (QfO) is an open collaboration framework for experts in comparative phylogenomics and related research areas who have an interest in highly accurate orthology predictions and their applications. We here report highlights and discussion points from the QfO meeting 2015 held in Barcelona. Achievements in recent years have established a basis to support developments for improved orthology prediction and to explore new approaches. Central to the QfO effort is proper benchmarking of methods and services, as well as design of standardized datasets and standardized formats to allow sharing and comparison of results. Simultaneously, analysis pipelines have been improved, evaluated and adapted to handle large datasets. All this would not have occurred without the long-term collaboration of Consortium members. Meeting regularly to review and coordinate complementary activities from a broad spectrum of innovative researchers clearly benefits the community. Highlights of the meeting include addressing sources of and legitimacy of disagreements between orthology calls, the context dependency of orthology definitions, special challenges encountered when analyzing very anciently rooted orthologies, orthology in the light of whole-genome duplications, and the concept of orthologous versus paralogous relationships at different levels, including domain-level orthology. Furthermore, particular needs for different applications (e.g. plant genomics, ancient gene families and others) and the infrastructure for making orthology inferences available (e.g. interfaces with model organism databases) were discussed, with several ongoing efforts that are expected to be reported on during the upcoming 2017 QfO meeting.
RESUMO
BACKGROUND: Recent increase in skin biopsies has been attributed to an epidemic of skin cancer. This may be avoidable, with potential savings. OBJECTIVE: To determine whether the increase in skin biopsies is attributable to increasing frequency of biopsies associated with histology lacking pathological cutaneous disease. Pathological cutaneous disease was defined as (1) a malignancy, precancerous lesion, or lesion of uncertain behavior; or (2) disease symptomatic or associated with adverse quality of life impact. PATIENTS AND METHODS: Retrospective cohort study, 2006 to 2013 of dermatology practice serving Florida and Ohio. Data were a consecutive sample of skin biopsies for diagnosis of dermatologic disease. RESULTS: A total of 267,706 biopsies by an average of 52 providers per month from January 06 to December 13 were analyzed. Number of biopsies per visit increased 2% per year (RR: 1.02, CI: 1.00-1.04). Likelihood of biopsy associated with histology indicative of nonpathological cutaneous disease did not increase over time (OR: 0.99, CI: 0.95-1.03, p = .6302). CONCLUSION: Rates of biopsies associated with nonpathological cutaneous disease is not increasing. Overall biopsy rates per visit have gradually increased; this seems attributable to greater rates of detection of pathological dermatologic disease.
Assuntos
Biópsia/estatística & dados numéricos , Padrões de Prática Médica/estatística & dados numéricos , Dermatopatias/diagnóstico , Feminino , Florida , Humanos , Masculino , Ohio , Estudos RetrospectivosRESUMO
The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype-phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype-phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.
Assuntos
Bases de Dados Genéticas , Estudos de Associação Genética/métodos , Genótipo , Fenótipo , Animais , Evolução Biológica , Biologia Computacional/métodos , Curadoria de Dados , Humanos , Ferramenta de Busca , Software , Especificidade da Espécie , Interface Usuário-Computador , NavegadorRESUMO
The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available.
Assuntos
Ontologia Genética/tendências , Doenças Genéticas Inatas/classificação , Doenças Genéticas Inatas/genética , Fenótipo , Terminologia como Assunto , Doenças Genéticas Inatas/patologia , Humanos , MEDLINE , Modelos BiológicosRESUMO
Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.
Assuntos
Estudos de Associação Genética , Animais , Biologia Computacional , Curadoria de Dados , Bases de Dados Factuais/normas , Interação Gene-Ambiente , Genômica , Humanos , Fenótipo , Padrões de Referência , Reprodutibilidade dos Testes , Terminologia como AssuntoRESUMO
Numerous new disease-gene associations have been identified by whole-exome sequencing studies in the last few years. However, many cases remain unsolved due to the sheer number of candidate variants remaining after common filtering strategies such as removing low quality and common variants and those deemed unlikely to be pathogenic. The observation that each of our genomes contains about 100 genuine loss-of-function variants makes identification of the causative mutation problematic when using these strategies alone. We propose using the wealth of genotype to phenotype data that already exists from model organism studies to assess the potential impact of these exome variants. Here, we introduce PHenotypic Interpretation of Variants in Exomes (PHIVE), an algorithm that integrates the calculation of phenotype similarity between human diseases and genetically modified mouse models with evaluation of the variants according to allele frequency, pathogenicity, and mode of inheritance approaches in our Exomiser tool. Large-scale validation of PHIVE analysis using 100,000 exomes containing known mutations demonstrated a substantial improvement (up to 54.1-fold) over purely variant-based (frequency and pathogenicity) methods with the correct gene recalled as the top hit in up to 83% of samples, corresponding to an area under the ROC curve of >95%. We conclude that incorporation of phenotype data can play a vital role in translational bioinformatics and propose that exome sequencing projects should systematically capture clinical phenotypes to take advantage of the strategy presented here.
Assuntos
Exoma/genética , Estudos de Associação Genética , Predisposição Genética para Doença , Polimorfismo de Nucleotídeo Único/genética , Algoritmos , Animais , Biologia Computacional , Bases de Dados Genéticas , Humanos , Camundongos , Fenótipo , Análise de Sequência de DNA , SoftwareRESUMO
The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is 'web ready': written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components. AVAILABILITY AND IMPLEMENTATION: The MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/Supplementary information: Supplementary data are available at Bioinformatics online. CONTACT: msa@bio.sh.
Assuntos
Alinhamento de Sequência , Software , Linguagens de Programação , NavegadorAssuntos
Produtos Biológicos , Preparações Farmacêuticas , Psoríase , Anti-Inflamatórios não Esteroides/uso terapêutico , Produtos Biológicos/efeitos adversos , Humanos , Psoríase/diagnóstico , Psoríase/tratamento farmacológico , Talidomida/efeitos adversos , Talidomida/análogos & derivados , Resultado do TratamentoRESUMO
The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.
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Ontologias Biológicas , Bases de Dados Factuais , Doenças Genéticas Inatas/genética , Fenótipo , Animais , Doenças Genéticas Inatas/diagnóstico , Genômica , Humanos , Internet , CamundongosRESUMO
The Matchmaker Exchange application programming interface (API) allows searching a patient's genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enable matching known diseases and variant-phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases.
Assuntos
Bases de Dados Genéticas , Doença/genética , Predisposição Genética para Doença/genética , Animais , Modelos Animais de Doenças , Variação Genética , Humanos , Disseminação de Informação , Fenótipo , Interface Usuário-ComputadorRESUMO
New sequencing technologies have ushered in a new era for diagnosis and discovery of new causative mutations for rare diseases. However, the sheer numbers of candidate variants that require interpretation in an exome or genomic analysis are still a challenging prospect. A powerful approach is the comparison of the patient's set of phenotypes (phenotypic profile) to known phenotypic profiles caused by mutations in orthologous genes associated with these variants. The most abundant source of relevant data for this task is available through the efforts of the Mouse Genome Informatics group and the International Mouse Phenotyping Consortium. In this review, we highlight the challenges in comparing human clinical phenotypes with mouse phenotypes and some of the solutions that have been developed by members of the Monarch Initiative. These tools allow the identification of mouse models for known disease-gene associations that may otherwise have been overlooked as well as candidate genes may be prioritized for novel associations. The culmination of these efforts is the Exomiser software package that allows clinical researchers to analyse patient exomes in the context of variant frequency and predicted pathogenicity as well the phenotypic similarity of the patient to any given candidate orthologous gene.
Assuntos
Bases de Dados Genéticas , Doenças Genéticas Inatas , Animais , Biologia Computacional , Modelos Animais de Doenças , Exoma/genética , Genômica , Humanos , Camundongos , Mutação , FenótipoRESUMO
Coxsackie eruptions concentrated in areas of atopic dermatitis, a phenomenon termed "eczema coxsackium," has been well described in the literature but, to our knowledge, the concentration of coxsackie viral lesions to areas of ichthyosis has not been reported. This case describes a rare presentation of coxsackie viral lesions concentrated in areas of epidermolytic ichthyosis in a 3-year-old boy.
Assuntos
Infecções por Coxsackievirus/virologia , Hiperceratose Epidermolítica/virologia , Pré-Escolar , Humanos , MasculinoRESUMO
In an effort to comprehensively characterize the functional elements within the genomes of the important model organisms Drosophila melanogaster and Caenorhabditis elegans, the NHGRI model organism Encyclopaedia of DNA Elements (modENCODE) consortium has generated an enormous library of genomic data along with detailed, structured information on all aspects of the experiments. The modMine database (http://intermine.modencode.org) described here has been built by the modENCODE Data Coordination Center to allow the broader research community to (i) search for and download data sets of interest among the thousands generated by modENCODE; (ii) access the data in an integrated form together with non-modENCODE data sets; and (iii) facilitate fine-grained analysis of the above data. The sophisticated search features are possible because of the collection of extensive experimental metadata by the consortium. Interfaces are provided to allow both biologists and bioinformaticians to exploit these rich modENCODE data sets now available via modMine.
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Caenorhabditis elegans/genética , Bases de Dados Genéticas , Drosophila melanogaster/genética , Animais , Expressão Gênica , Genoma Helmíntico , Genoma de Inseto , Genômica , Internet , Interface Usuário-ComputadorRESUMO
The goal of the Gene Ontology (GO) project is to provide a uniform way to describe the functions of gene products from organisms across all kingdoms of life and thereby enable analysis of genomic data. Protein annotations are either based on experiments or predicted from protein sequences. Since most sequences have not been experimentally characterized, most available annotations need to be based on predictions. To make as accurate inferences as possible, the GO Consortium's Reference Genome Project is using an explicit evolutionary framework to infer annotations of proteins from a broad set of genomes from experimental annotations in a semi-automated manner. Most components in the pipeline, such as selection of sequences, building multiple sequence alignments and phylogenetic trees, retrieving experimental annotations and depositing inferred annotations, are fully automated. However, the most crucial step in our pipeline relies on software-assisted curation by an expert biologist. This curation tool, Phylogenetic Annotation and INference Tool (PAINT) helps curators to infer annotations among members of a protein family. PAINT allows curators to make precise assertions as to when functions were gained and lost during evolution and record the evidence (e.g. experimentally supported GO annotations and phylogenetic information including orthology) for those assertions. In this article, we describe how we use PAINT to infer protein function in a phylogenetic context with emphasis on its strengths, limitations and guidelines. We also discuss specific examples showing how PAINT annotations compare with those generated by other highly used homology-based methods.
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Genômica/métodos , Anotação de Sequência Molecular/métodos , Filogenia , Proteínas/química , Bases de Dados Genéticas , Genoma , Proteínas/genéticaRESUMO
Logical models and physical specifications provide the foundation for storage, management and analysis of complex sets of data, and describe the relationships between measured data elements and metadata - the contextual descriptors that define the primary data. Here, we use imaging applications to illustrate the purpose of the various implementations of data specifications and the requirement for open, standardized, data formats to facilitate the sharing of critical digital data and metadata.
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Sistemas de Gerenciamento de Base de Dados/normas , Hipermídia/normas , Armazenamento e Recuperação da Informação/normas , Linguagens de Programação , Animais , Sistemas de Gerenciamento de Base de Dados/estatística & dados numéricos , Genoma , Humanos , Hipermídia/estatística & dados numéricos , Armazenamento e Recuperação da Informação/métodos , Armazenamento e Recuperação da Informação/estatística & dados numéricos , Pesquisa/normas , Pesquisa/estatística & dados numéricos , Projetos de Pesquisa , Integração de Sistemas , Fatores de TempoRESUMO
A recent paper (Nehrt et al., PLoS Comput. Biol. 7:e1002073, 2011) has proposed a metric for the "functional similarity" between two genes that uses only the Gene Ontology (GO) annotations directly derived from published experimental results. Applying this metric, the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes, an unexpected result with broad implications if true. We suggest, based on both theoretical and empirical considerations, that this proposed metric should not be interpreted as a functional similarity, and therefore cannot be used to support any conclusions about the "ortholog conjecture" (or, more properly, the "ortholog functional conservation hypothesis"). First, we reexamine the case studies presented by Nehrt et al. as examples of orthologs with divergent functions, and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions. We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms. We conclude that the reported statistical differences in annotations between pairs of orthologous genes do not reflect differences in biological function, but rather complementarity in experimental approaches. Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1) that GO annotations are often incomplete, potentially in a biased manner, and subject to an "open world assumption" (absence of an annotation does not imply absence of a function), and 2) that conclusions drawn from a novel, large-scale GO analysis should whenever possible be supported by careful, in-depth examination of examples, to help ensure the conclusions have a justifiable biological basis.