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1.
J Biomed Semantics ; 12(1): 18, 2021 08 28.
Artigo em Inglês | MEDLINE | ID: mdl-34454610

RESUMO

BACKGROUND: With COVID-19 still in its pandemic stage, extensive research has generated increasing amounts of data and knowledge. As many studies are published within a short span of time, we often lose an integrative and comprehensive picture of host-coronavirus interaction (HCI) mechanisms. As of early April 2021, the ImmPort database has stored 7 studies (with 6 having details) that cover topics including molecular immune signatures, epitopes, and sex differences in terms of mortality in COVID-19 patients. The Coronavirus Infectious Disease Ontology (CIDO) represents basic HCI information. We hypothesize that the CIDO can be used as the platform to represent newly recorded information from ImmPort leading the reinforcement of CIDO. METHODS: The CIDO was used as the semantic platform for logically modeling and representing newly identified knowledge reported in the 6 ImmPort studies. A recursive eXtensible Ontology Development (XOD) strategy was established to support the CIDO representation and enhancement. Secondary data analysis was also performed to analyze different aspects of the HCI from these ImmPort studies and other related literature reports. RESULTS: The topics covered by the 6 ImmPort papers were identified to overlap with existing CIDO representation. SARS-CoV-2 viral S protein related HCI knowledge was emphasized for CIDO modeling, including its binding with ACE2, mutations causing different variants, and epitope homology by comparison with other coronavirus S proteins. Different types of cytokine signatures were also identified and added to CIDO. Our secondary analysis of two cohort COVID-19 studies with cytokine panel detection found that a total of 11 cytokines were up-regulated in female patients after infection and 8 cytokines in male patients. These sex-specific gene responses were newly modeled and represented in CIDO. A new DL query was generated to demonstrate the benefits of such integrative ontology representation. Furthermore, IL-10 signaling pathway was found to be statistically significant for both male patients and female patients. CONCLUSION: Using the recursive XOD strategy, six new ImmPort COVID-19 studies were systematically reviewed, the results were modeled and represented in CIDO, leading to the enhancement of CIDO. The enhanced ontology and further seconary analysis supported more comprehensive understanding of the molecular mechanism of host responses to COVID-19 infection.


Assuntos
Ontologias Biológicas , COVID-19 , Interações entre Hospedeiro e Microrganismos , Humanos , Semântica , Glicoproteína da Espícula de Coronavírus/metabolismo
2.
Front Immunol ; 12: 639491, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33777032

RESUMO

Vaccines stimulate various immune factors critical to protective immune responses. However, a comprehensive picture of vaccine-induced immune factors and pathways have not been systematically collected and analyzed. To address this issue, we developed VaximmutorDB, a web-based database system of vaccine immune factors (abbreviated as "vaximmutors") manually curated from peer-reviewed articles. VaximmutorDB currently stores 1,740 vaccine immune factors from 13 host species (e.g., human, mouse, and pig). These vaximmutors were induced by 154 vaccines for 46 pathogens. Top 10 vaximmutors include three antibodies (IgG, IgG2a and IgG1), Th1 immune factors (IFN-γ and IL-2), Th2 immune factors (IL-4 and IL-6), TNF-α, CASP-1, and TLR8. Many enriched host processes (e.g., stimulatory C-type lectin receptor signaling pathway, SRP-dependent cotranslational protein targeting to membrane) and cellular components (e.g., extracellular exosome, nucleoplasm) by all the vaximmutors were identified. Using influenza as a model, live attenuated and killed inactivated influenza vaccines stimulate many shared pathways such as signaling of many interleukins (including IL-1, IL-4, IL-6, IL-13, IL-20, and IL-27), interferon signaling, MARK1 activation, and neutrophil degranulation. However, they also present their unique response patterns. While live attenuated influenza vaccine FluMist induced significant signal transduction responses, killed inactivated influenza vaccine Fluarix induced significant metabolism of protein responses. Two different Yellow Fever vaccine (YF-Vax) studies resulted in overlapping gene lists; however, they shared more portions of pathways than gene lists. Interestingly, live attenuated YF-Vax simulates significant metabolism of protein responses, which was similar to the pattern induced by killed inactivated Fluarix. A user-friendly web interface was generated to access, browse and search the VaximmutorDB database information. As the first web-based database of vaccine immune factors, VaximmutorDB provides systematical collection, standardization, storage, and analysis of experimentally verified vaccine immune factors, supporting better understanding of protective vaccine immunity.


Assuntos
Anticorpos Antivirais/imunologia , Imunidade/imunologia , Fatores Imunológicos/imunologia , Vacinas/imunologia , Animais , Bases de Dados Factuais , Humanos , Internet , Transdução de Sinais/imunologia , Vacinação/métodos
3.
Autophagy ; 17(6): 1543-1554, 2021 Jun.
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.

4.
Methods Mol Biol ; 2074: 165-179, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31583638

RESUMO

Modern large-scale biological data analysis often generates a set of significant genes, frequently associated with scores. Pathway-based approaches are routinely performed to understand the functional contexts of these genes. Reactome is the most comprehensive open-access biological pathway knowledge base, widely used in the research community, providing a solid foundation for pathway-based data analysis. ReactomeFIViz is a Cytoscape app built upon Reactome pathways to help users perform pathway- and network-based data analysis and visualization. In this chapter we describe procedures on how to perform pathway enrichment analysis using ReactomeFIViz for a gene score file. We describe two types of analysis: pathway enrichment based on a set of significant genes and GSEA analysis using gene scores without cutoff. We also describe a feature to overlay gene scores onto pathway diagrams, enabling users to understand the underlying mechanisms for up- or down- regulated pathways collected from pathway analysis.


Assuntos
Biologia Computacional/métodos , Mapas de Interação de Proteínas , Software
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.
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
7.
BMC Bioinformatics ; 20(Suppl 21): 704, 2019 Dec 23.
Artigo em Inglês | MEDLINE | ID: mdl-31865910

RESUMO

BACKGROUND: Different human responses to the same vaccine were frequently observed. For example, independent studies identified overlapping but different transcriptomic gene expression profiles in Yellow Fever vaccine 17D (YF-17D) immunized human subjects. Different experimental and analysis conditions were likely contributed to the observed differences. To investigate this issue, we developed a Vaccine Investigation Ontology (VIO), and applied VIO to classify the different variables and relations among these variables systematically. We then evaluated whether the ontological VIO modeling and VIO-based statistical analysis would contribute to the enhanced vaccine investigation studies and a better understanding of vaccine response mechanisms. RESULTS: Our VIO modeling identified many variables related to data processing and analysis such as normalization method, cut-off criteria, software settings including software version. The datasets from two previous studies on human responses to YF-17D vaccine, reported by Gaucher et al. (2008) and Querec et al. (2009), were re-analyzed. We first applied the same LIMMA statistical method to re-analyze the Gaucher data set and identified a big difference in terms of significantly differentiated gene lists compared to the original study. The different results were likely due to the LIMMA version and software package differences. Our second study re-analyzed both Gaucher and Querec data sets but with the same data processing and analysis pipeline. Significant differences in differential gene lists were also identified. In both studies, we found that Gene Ontology (GO) enrichment results had more overlapping than the gene lists and enriched pathway lists. The visualization of the identified GO hierarchical structures among the enriched GO terms and their associated ancestor terms using GOfox allowed us to find more associations among enriched but often different GO terms, demonstrating the usage of GO hierarchical relations enhance data analysis. CONCLUSIONS: The ontology-based analysis framework supports standardized representation, integration, and analysis of heterogeneous data of host responses to vaccines. Our study also showed that differences in specific variables might explain different results drawn from similar studies.


Assuntos
Vacinas , Ontologias Biológicas , Humanos , Software
8.
Blood Adv ; 3(20): 3038-3051, 2019 10 22.
Artigo em Inglês | MEDLINE | ID: mdl-31648326

RESUMO

Acute myeloid leukemia (AML) remains difficult to treat due to mutational heterogeneity and the development of resistance to therapy. Targeted agents, such as MEK inhibitors, may be incorporated into treatment; however, the impact of MEK inhibitors on the immune microenvironment in AML is not well understood. A greater understanding of the implications of MEK inhibition on immune responses may lead to a greater understanding of immune evasion and more rational combinations with immunotherapies. This study describes the impact of trametinib on both T cells and AML blast cells by using an immunosuppressive mouse model of AML and primary patient samples. We also used a large AML database of functional drug screens to understand characteristics of trametinib-sensitive samples. In the mouse model, trametinib increased T-cell viability and restored T-cell proliferation. Importantly, we report greater proliferation in the CD8+CD44+ effector subpopulation and impaired activation of CD8+CD62L+ naive cells. Transcriptome analysis revealed that trametinib-sensitive samples have an inflammatory gene expression profile, and we also observed increased programmed cell death ligand 1 (PD-L1) expression on trametinib-sensitive samples. Finally, we found that trametinib consistently reduced PD-L1 and PD-L2 expression in a dose-dependent manner on the myeloid population. Altogether, our data present greater insight into the impact of trametinib on the immune microenvironment and characteristics of trametinib-sensitive patient samples.


Assuntos
Imunomodulação , Leucemia Mieloide Aguda/etiologia , Leucemia Mieloide Aguda/metabolismo , Sistema de Sinalização das MAP Quinases , Linfócitos T/imunologia , Linfócitos T/metabolismo , Animais , Antineoplásicos/farmacologia , Biomarcadores , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Modelos Animais de Doenças , Perfilação da Expressão Gênica/métodos , Humanos , Imunomodulação/efeitos dos fármacos , Imunofenotipagem , Leucemia Mieloide Aguda/patologia , Ativação Linfocitária/imunologia , Sistema de Sinalização das MAP Quinases/efeitos dos fármacos , Linfócitos T/efeitos dos fármacos , Ensaios Antitumorais Modelo de Xenoenxerto , Proteínas ras/genética , Proteínas ras/metabolismo
9.
F1000Res ; 8: 908, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31372215

RESUMO

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.


Assuntos
Sistemas de Liberação de Medicamentos , Proteínas , Software , Visualização de Dados , Humanos
10.
Front Pharmacol ; 10: 557, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31214023

RESUMO

A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.

11.
Front Genet ; 9: 183, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29910823

RESUMO

The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.

12.
F1000Res ; 7: 531, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29946442

RESUMO

Pathway- and network-based approaches project seemingly unrelated genes onto the context of pathways and networks, enhancing the analysis power that cannot be achieved via gene-based approaches. Pathway and network approaches are routinely applied in large-scale data analysis for cancer and other complicated diseases. ReactomeFIViz is a Cytoscape app, providing features for researchers to perform pathway- and network-based data analysis and visualization by leveraging manually curated Reactome pathways and highly reliable Reactome functional interaction network. To facilitate adoption of this app in bioinformatics software pipeline and workflow development, we develop a CyREST API for ReactomeFIViz by exposing some major features in the app. We describe a use case to demonstrate the use of this API in a Python-based notebook, and believe the new API will provide the community a convenient and powerful tool to perform pathway- and network-based data analysis and visualization using our app in an automatic way.

13.
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
14.
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
15.
Trends Pharmacol Sci ; 38(12): 1085-1099, 2017 12.
Artigo em Inglês | MEDLINE | ID: mdl-28964549

RESUMO

A core tenet of precision oncology is the rational choice of drugs to interact with patient-specific biological targets of interest, but it is currently difficult for researchers to obtain consistent and well-supported target information for pharmaceutical drugs. We review current drug-target interaction resources and critically assess how supporting evidence is handled. We introduce the concept of a unified Cancer Targetome to aggregate drug-target interactions in an evidence-based framework. We discuss current unmet needs and the implications for evidence-based clinical omics. The focus of this review is precision oncology but the discussion is highly relevant to targeted therapies in any area.


Assuntos
Terapia de Alvo Molecular/métodos , Neoplasias/tratamento farmacológico , Neoplasias/genética , Animais , Medicina Baseada em Evidências , Humanos , Oncologia/métodos , Medicina de Precisão/métodos
16.
Artigo em Inglês | MEDLINE | ID: mdl-28775896

RESUMO

Despite widespread use of the Bacillus Calmette-Guerin vaccine, tuberculosis, caused by infection with Mycobacterium tuberculosis, remains a leading cause of morbidity and mortality worldwide. As CD8+ T cells are critical to tuberculosis host defense and a phase 2b vaccine trial of modified vaccinia Ankara expressing Ag85a that failed to demonstrate efficacy, also failed to induce a CD8+ T cell response, an effective tuberculosis vaccine may need to induce CD8+ T cells. However, little is known about CD8, as compared to CD4, antigens in tuberculosis. Herein, we report the results of the first ever HLA allele independent genome-wide CD8 antigen discovery program. Using CD8+ T cells derived from humans with latent tuberculosis infection or tuberculosis and an interferon-γ ELISPOT assay, we screened a synthetic peptide library representing 10% of the Mycobacterium tuberculosis proteome, selected to be enriched for Mycobacterium tuberculosis antigens. We defined a set of immunodominant CD8 antigens including part or all of 74 Mycobacterium tuberculosis proteins, only 16 of which are previously known CD8 antigens. Immunogenicity was associated with the degree of expression of mRNA and protein. Immunodominant antigens were enriched in cell wall proteins with preferential recognition of Esx protein family members, and within proteins comprising the Mycobacterium tuberculosis secretome. A validation study of immunodominant antigens demonstrated that these antigens were strongly recognized in Mycobacterium tuberculosis-infected individuals from a tuberculosis endemic region in Africa. The tuberculosis vaccine field will likely benefit from this greatly increased known repertoire of CD8 immunodominant antigens and definition of properties of Mycobacterium tuberculosis proteins important for CD8 antigenicity.

17.
Methods Mol Biol ; 1558: 235-253, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28150241

RESUMO

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.


Assuntos
Biologia Computacional/métodos , Suscetibilidade a Doenças , Software , Teorema de Bayes , Mineração de Dados/métodos , Bases de Dados Factuais , Redes Reguladoras de Genes , Humanos , Redes e Vias Metabólicas , Anotação de Sequência Molecular , Mapas de Interação de Proteínas , Curva ROC , Transdução de Sinais , Navegador , Fluxo de Trabalho
18.
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
19.
PLoS Comput Biol ; 12(5): e1004941, 2016 05.
Artigo em Inglês | MEDLINE | ID: mdl-27203685

RESUMO

Reactome and WikiPathways are two of the most popular freely available databases for biological pathways. Reactome pathways are centrally curated with periodic input from selected domain experts. WikiPathways is a community-based platform where pathways are created and continually curated by any interested party. The nascent collaboration between WikiPathways and Reactome illustrates the mutual benefits of combining these two approaches. We created a format converter that converts Reactome pathways to the GPML format used in WikiPathways. In addition, we developed the ComplexViz plugin for PathVisio which simplifies looking up complex components. The plugin can also score the complexes on a pathway based on a user defined criterion. This score can then be visualized on the complex nodes using the visualization options provided by the plugin. Using the merged collection of curated and converted Reactome pathways, we demonstrate improved pathway coverage of relevant biological processes for the analysis of a previously described polycystic ovary syndrome gene expression dataset. Additionally, this conversion allows researchers to visualize their data on Reactome pathways using PathVisio's advanced data visualization functionalities. WikiPathways benefits from the dedicated focus and attention provided to the content converted from Reactome and the wealth of semantic information about interactions. Reactome in turn benefits from the continuous community curation available on WikiPathways. The research community at large benefits from the availability of a larger set of pathways for analysis in PathVisio and Cytoscape. The pathway statistics results obtained from PathVisio are significantly better when using a larger set of candidate pathways for analysis. The conversion serves as a general model for integration of multiple pathway resources developed using different approaches.


Assuntos
Redes e Vias Metabólicas , Modelos Biológicos , Software , Biologia Computacional , Gráficos por Computador , Bases de Dados Factuais , Ontologia Genética , Humanos , Internet , Bases de Conhecimento
20.
Nucleic Acids Res ; 44(D1): D481-7, 2016 Jan 04.
Artigo em Inglês | MEDLINE | ID: mdl-26656494

RESUMO

The Reactome Knowledgebase (www.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 pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently.


Assuntos
Bases de Dados de Compostos Químicos , Redes e Vias Metabólicas , Expressão Gênica , Humanos , Bases de Conhecimento , Proteínas/metabolismo , Transdução de Sinais , Software
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