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1.
Nucleic Acids Res ; 52(D1): D938-D949, 2024 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-38000386

RESUMO

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.


Assuntos
Bases de Dados Factuais , Doença , Genes , Fenótipo , Humanos , Internet , Bases de Dados Factuais/normas , Software , Genes/genética , Doença/genética
2.
Nucleic Acids Res ; 52(D1): D1333-D1346, 2024 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-37953324

RESUMO

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.


Assuntos
Ontologias Biológicas , Humanos , Fenótipo , Genômica , Algoritmos , Doenças Raras
3.
Bioinformatics ; 39(7)2023 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-37389415

RESUMO

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org.


Assuntos
Ontologias Biológicas , COVID-19 , Humanos , Reconhecimento Automatizado de Padrão , Doenças Raras , Aprendizado de Máquina
4.
Bioinformatics ; 37(19): 3343-3348, 2021 Oct 11.
Artigo em Inglês | MEDLINE | ID: mdl-33964129

RESUMO

MOTIVATION: Gene Ontology 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 data are available at Bioinformatics online.

5.
PLoS Comput Biol ; 17(10): e1009463, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34710081

RESUMO

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.


Assuntos
Crowdsourcing/métodos , Ontologia Genética , Anotação de Sequência Molecular/métodos , Biologia Computacional , Bases de Dados Genéticas , Humanos , Proteínas/genética , Proteínas/fisiologia
6.
Nucleic Acids Res ; 48(D1): D704-D715, 2020 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-31701156

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-Computador
7.
Nucleic Acids Res ; 46(D1): D1168-D1180, 2018 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-29186578

RESUMO

The Planteome project (http://www.planteome.org) provides a suite of reference and species-specific ontologies for plants and annotations to genes and phenotypes. Ontologies serve as common standards for semantic integration of a large and growing corpus of plant genomics, phenomics and genetics data. The reference ontologies include the Plant Ontology, Plant Trait Ontology and the Plant Experimental Conditions Ontology developed by the Planteome project, along with the Gene Ontology, Chemical Entities of Biological Interest, Phenotype and Attribute Ontology, and others. The project also provides access to species-specific Crop Ontologies developed by various plant breeding and research communities from around the world. We provide integrated data on plant traits, phenotypes, and gene function and expression from 95 plant taxa, annotated with reference ontology terms. The Planteome project is developing a plant gene annotation platform; Planteome Noctua, to facilitate community engagement. All the Planteome ontologies are publicly available and are maintained at the Planteome GitHub site (https://github.com/Planteome) for sharing, tracking revisions and new requests. The annotated data are freely accessible from the ontology browser (http://browser.planteome.org/amigo) and our data repository.


Assuntos
Bases de Dados Genéticas , Genoma de Planta , Plantas/genética , Produtos Agrícolas/genética , Curadoria de Dados , Regulação da Expressão Gênica de Plantas , Ontologia Genética , Anotação de Sequência Molecular , Fenótipo , Software , Interface Usuário-Computador
8.
Nucleic Acids Res ; 45(D1): D712-D722, 2017 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-27899636

RESUMO

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 , Navegador
9.
BMC Bioinformatics ; 15: 155, 2014 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-24885854

RESUMO

BACKGROUND: The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations. RESULTS: The GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector-target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions. CONCLUSIONS: The additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism's gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction.


Assuntos
Ontologia Genética , Anotação de Sequência Molecular , Biologia Computacional/métodos , Humanos , Proteínas/genética
10.
Nucleic Acids Res ; 40(Database issue): D1082-8, 2012 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-22080565

RESUMO

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.


Assuntos
Caenorhabditis elegans/genética , Bases de Dados Genéticas , Drosophila melanogaster/genética , Animais , Expressão Gênica , Genoma Helmíntico , Genoma de Inseto , Genômica , Internet , Interface Usuário-Computador
11.
Genetics ; 224(1)2023 05 04.
Artigo em Inglês | MEDLINE | ID: mdl-36866529

RESUMO

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.


Assuntos
Bases de Dados Genéticas , Proteínas , Ontologia Genética , Proteínas/genética , Anotação de Sequência Molecular , Biologia Computacional
12.
Database (Oxford) ; 20222022 10 08.
Artigo em Inglês | MEDLINE | ID: mdl-36208225

RESUMO

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community. Database URL: https://github.com/INCATools/ontology-development-kit.


Assuntos
Ontologias Biológicas , Bases de Dados Factuais , Metadados , Controle de Qualidade , Software , Fluxo de Trabalho
13.
Patterns (N Y) ; 2(1): 100155, 2021 Jan 08.
Artigo em Inglês | MEDLINE | ID: mdl-33196056

RESUMO

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

14.
Database (Oxford) ; 20212021 10 26.
Artigo em Inglês | MEDLINE | ID: mdl-34697637

RESUMO

Biological ontologies are used to organize, curate and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here, we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology's compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable, federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data Findable, Accessible, Interoperable, and Reusable (FAIR). Database URL http://obofoundry.org/.


Assuntos
Ontologias Biológicas , Bases de Dados Factuais , Metadados
15.
Bioinformatics ; 25(2): 288-9, 2009 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-19033274

RESUMO

AmiGO is a web application that allows users to query, browse and visualize ontologies and related gene product annotation (association) data. AmiGO can be used online at the Gene Ontology (GO) website to access the data provided by the GO Consortium; it can also be downloaded and installed to browse local ontologies and annotations. AmiGO is free open source software developed and maintained by the GO Consortium.


Assuntos
Bases de Dados Genéticas , Software , Biologia Computacional/métodos , Armazenamento e Recuperação da Informação/métodos , Internet , Interface Usuário-Computador
16.
bioRxiv ; 2020 Aug 18.
Artigo em Inglês | MEDLINE | ID: mdl-32839776

RESUMO

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics. BIGGER PICTURE: An effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.

17.
Open Biol ; 10(9): 200149, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32875947

RESUMO

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.


Assuntos
Biologia Computacional/métodos , Ontologia Genética , Anotação de Sequência Molecular , Bases de Dados Genéticas , Evolução Molecular , Genoma Fúngico , Genômica/métodos , Controle de Qualidade , Schizosaccharomyces/genética , Navegador , Fluxo de Trabalho
18.
PLoS One ; 14(3): e0213090, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30917137

RESUMO

Data are the foundation of science, and there is an increasing focus on how data can be reused and enhanced to drive scientific discoveries. However, most seemingly "open data" do not provide legal permissions for reuse and redistribution. The inability to integrate and redistribute our collective data resources blocks innovation and stymies the creation of life-improving diagnostic and drug selection tools. To help the biomedical research and research support communities (e.g. libraries, funders, repositories, etc.) understand and navigate the data licensing landscape, the (Re)usable Data Project (RDP) (http://reusabledata.org) assesses the licensing characteristics of data resources and how licensing behaviors impact reuse. We have created a ruleset to determine the reusability of data resources and have applied it to 56 scientific data resources (e.g. databases) to date. The results show significant reuse and interoperability barriers. Inspired by game-changing projects like Creative Commons, the Wikipedia Foundation, and the Free Software movement, we hope to engage the scientific community in the discussion regarding the legal use and reuse of scientific data, including the balance of openness and how to create sustainable data resources in an increasingly competitive environment.


Assuntos
Acesso à Informação , Pesquisa Biomédica , Licenciamento , Bases de Dados Factuais , Humanos , Software
19.
Methods Mol Biol ; 1446: 149-160, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-27812941

RESUMO

The Gene Ontology Consortium (GOC) produces a wealth of resources widely used throughout the scientific community. In this chapter, we discuss the different ways in which researchers can access the resources of the GOC. We here share details about the mechanics of obtaining GO annotations, both by manually browsing, querying, and downloading data from the GO website, as well as computationally accessing the resources from the command line, including the ability to restrict the data being retrieved to subsets with only certain attributes.


Assuntos
Ontologia Genética , Anotação de Sequência Molecular , Animais , Biologia Computacional/métodos , Bases de Dados Genéticas , Humanos , Internet , Anotação de Sequência Molecular/métodos , Software
20.
Genetics ; 203(4): 1491-5, 2016 08.
Artigo em Inglês | MEDLINE | ID: mdl-27516611

RESUMO

The principles of genetics apply across the entire tree of life. At the cellular level we share biological mechanisms with species from which we diverged millions, even billions of years ago. We can exploit this common ancestry to learn about health and disease, by analyzing DNA and protein sequences, but also through the observable outcomes of genetic differences, i.e. phenotypes. To solve challenging disease problems we need to unify the heterogeneous data that relates genomics to disease traits. Without a big-picture view of phenotypic data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) provides tools for genotype-phenotype analysis, genomic diagnostics, and precision medicine across broad areas of disease.


Assuntos
Biologia Computacional , Estudos de Associação Genética , Genômica , Medicina de Precisão , Bases de Dados Genéticas , Humanos , Análise de Sequência de DNA , Análise de Sequência de Proteína
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