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Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput "omics" Data.

ABSTRACT: High-throughput "omics" technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualization, and comparative analysis to facilitate data-driven hypothesis generation and biological knowledge discovery. In this paper, we present the challenges in high-throughput "omics" data integration and analysis, introduce a protein-centric approach for systems integration of large and heterogeneous high-throughput "omics" data including microarray, mass spectrometry, protein sequence, protein structure, and protein interaction data, and use scientific case study to illustrate how one can use varied "omics" data from different laboratories to make useful connections that could lead to new biological knowledge.

SUBMITTER: Chen C

PROVIDER: S-EPMC2847380 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput "omics" Data.

Chen Chuming C McGarvey Peter B PB Huang Hongzhan H Wu Cathy H CH

Advances in bioinformatics 20100329

High-throughput "omics" technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualiz ...[more]

PMID: 20369061

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Project description:BackgroundBiliary atresia (BA) is the most common form of severe neonatal obstructive jaundice. The etiology and pathogenesis of BA are multifactorial, and different factors may interact to produce heterogeneous pathological features and clinical outcomes. Despite different pathological features, all patients received the same treatment strategy. This study performed integrative clustering analysis based on multiple high-throughput datasets to identify the molecular subtypes of BA and provide a new treatment strategy for personalized treatment of the different subtypes of BA.MethodsThe RNA sequence dataset GSE122340 in the Gene Expression Omnibus (GEO) database was downloaded; 31 BA and 20 control normal liver tissues were collected at our center for transcriptome sequencing, and clinical and follow-up data of BA patients were available. Molecular subtypes were identified using integrated unsupervised cluster analysis involving gene expression, biliary fibrosis, and immune enrichment scores based on the transcriptome dataset, and the results were validated using independent datasets.ResultsBased on the results of the integrated unsupervised clustering analysis, four molecular subtypes were identified: autoimmune, inflammatory, virus infection-related, and oxidative stress. The autoimmune subtype with a moderate prognosis was dominated by autoimmune responses and morphogenesis, such as the Fc-gamma receptor and Wnt signaling pathway. The biological process of the inflammatory subtype was mainly the inflammatory response, with the best prognosis, youngest age at surgery, and lowest liver stiffness. The virus infection-related subtype had the worst prognosis and was enriched for a variety of biological processes such as viral infection, immunity, anatomical morphogenesis, and epithelial mesenchymal transition. The oxidative stress subtype was characterized by the activation of oxidative stress and various metabolic pathways and had a poor prognosis. The above results were verified independently in the validation sets.ConclusionsThis study identified four molecular subtypes of BA with distinct prognosis and biological processes. According to the pathological characteristics of the different subtypes, individualized perioperative and preoperative treatment may be a new strategy to improve the prognosis of BA.

Project description:BackgroundThe Bioinformatics Resource Manager (BRM) is a web-based tool developed to facilitate identifier conversion and data integration for Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus (rat), Danio rerio (zebrafish), and Macaca mulatta (macaque), as well as perform orthologous conversions among the supported species. In addition to providing a robust means of identifier conversion, BRM also incorporates a suite of microRNA (miRNA)-target databases upon which to query target genes or to perform reverse target lookups using gene identifiers.ResultsBRM has the capability to perform cross-species identifier lookups across common identifier types, directly integrate datasets across platform or species by performing identifier retrievals in the background, and retrieve miRNA targets from multiple databases simultaneously and integrate the resulting gene targets with experimental mRNA data. Here we use workflows provided in BRM to integrate RNA sequencing data across species to identify common biomarkers of exposure after treatment of human lung cells and zebrafish to benzo[a]pyrene (BAP). We further use the miRNA Target workflow to experimentally determine the role of miRNAs as regulators of BAP toxicity and identify the predicted functional consequences of miRNA-target regulation in our system. The output from BRM can easily and directly be uploaded to freely available visualization tools for further analysis. From these examples, we were able to identify an important role for several miRNAs as potential regulators of BAP toxicity in human lung cells associated with cell migration, cell communication, cell junction assembly and regulation of cell death.ConclusionsOverall, BRM provides bioinformatics tools to assist biologists having minimal programming skills with analysis and integration of high-content omics' data from various transcriptomic and proteomic platforms. BRM workflows were developed in Java and other open-source technologies and are served publicly using Apache Tomcat at https://cbb.pnnl.gov/brm/ .

Dataset Information

Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput "omics" Data.

Publications

Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput "omics" Data.

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