Core D: Bioinformatics and Modeling Core

核心 D：生物信息学和建模核心

• 批准号: 10394318
• 负责人: Ivan Marazzi
• 金额: $ 36.12万
• 依托单位: UNIVERSITY OF TEXAS MED BR GALVESTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10394318
• 关键词: Address; Affect; Algorithms; Autoimmune Diseases; Binding Proteins; Binding Sites; Bioinformatics; Cell model; Cells; Chromatin; Code; Collaborations; Communities; Computer software; Computing Methodologies; Consult; Custom; Data; Data Analytics; Data Collection; Data Set; Databases; Development; Diagnostic; Ebola virus; Emerging Technologies; Engineering; Epigenetic Process; Gene Expression Regulation; Genetic; Genetic Techniques; Genetic Transcription; Genome; Genomics; Goals; Histones; Human; Human Herpesvirus 4; Immune; Immune response; Immunity; In Vitro; Infection; Inflammatory; Interdisciplinary Study; Internet; Laboratories; Location; Measurement; Mediating; Metabolic; Metadata; Methods; MicroRNAs; Modeling; Modification; Molecular; Network-based; Nucleic Acid Regulatory Sequences; Organism; Outcome; Pathogenicity; Pathway interactions; Pediatric Hospitals; Play; Post-Translational Protein Processing; Process; Proteins; Proteome; Proteomics; Quality Control; RNA; RNA Processing; RNA analysis; RNA-Binding Proteins; Recording of previous events; Regulator Genes; Research; Research Personnel; Research Project Grants; Role; Standardization; System; Techniques; Testing; Therapeutic; Tissue-Specific Gene Expression; Type I DNA Topoisomerases; Viral; Viral Pathogenesis; Virus; Virus Diseases; analysis pipeline; base; bioinformatics pipeline; bioinformatics tool; cell behavior; cell type; cofactor; companion diagnostics; computational pipelines; computerized tools; data integration; data management; design; differential expression; functional genomics; genetic regulatory protein; genome-wide; genomic data; genomic locus; in vivo; influenza virus gene; innovation; medical schools; member; mutant; network models; novel; pathogen; personalized medicine; programs; proteogenomics; response; tool; transcription factor; transcriptomics; virus host interaction; web based interface; web portal

CORE D: PROJECT SUMMARY/ABSTRACT The role of the Bioinformatics and Modeling Core is to process, analyze, and model the genome and proteome-level datasets produced in this project. This will be accomplished through two main objectives: (1) to design and provide tools to analyze experimental data, and (2) to integrate data into network models of host response and pathogenicity. Previously developed in-house tools, publicly available established relevant software packages, and new bioinformatics tools developed in the Projects will be integrated, automated, and then applied to data generated by the Projects, and made available to the larger research community through web-based interfaces. EBOV-relevant publicly available and locally produced data will be organized into databases that can be used by Program investigators and others to test hypotheses. The following Specific Aims will be spearheaded by the Core in close collaboration with the project investigators. Aim 1. Characterize the transcriptional, posttranscriptional, and posttranslational mechanisms impacted by EBOV infection in cells and organisms. Tools aimed for revealing the host gene regulatory mechanisms impacted by EBOV infection will be developed and applied. Differentially expressed, modified, and processed RNAs and genomic regulatory regions between infected and uninfected cells will be identified. Differential posttranslational protein modifications will also be identified. Specific altered regulatory mechanisms will be predicted using transcription factor, cofactor, epigenetic regulator, RNA binding protein, and microRNA enrichment analyses. The Core will systematically intersect the genomic locations of infection-modified loci and processed custom databases containing more than 20,000 human publicly available genome-wide databases to reveal molecular processes and factors affected by EBOV infection. Aim 2. Model cell type-specific responses to EBOV in vitro and in vivo. The Core will build an integrative model of the mechanisms controlling EBOV pathogenesis by combining data generated the Projects. To this end, they will employ network-based data integration approaches using network propagation-based techniques. In brief, modeling approaches will be applied to find correlates and associations between transcriptional, posttranscriptional, and posttranslational data to define host and viral factors that (1) reflect the activation states of the network; (2) control cell response to EBOV; and (3) can be tested for both diagnostics and therapies. Aim 3. Facilitate internal and external collaborations through metadata standardization, and interactive web and programmatic data interfaces. Streamlined computational pipelines will be built using best-standard engineering processes and release the code and data through web-based interfaces. The Bioinformatics and Modeling Core will facilitate centralized data collection, exchange and analysis, and provide expertise by the members of the Core encompassing genetics, viral infection, functional genomics, gene regulation, proteomics, modeling, and bioinformatics.

核心D：项目总结/摘要 生物信息学和建模核心的作用是处理，分析和建模基因组， 蛋白质组水平的数据集在这个项目中产生。这将通过两个主要目标来实现： 设计并提供分析实验数据的工具;（2）将数据集成到主机的网络模型中 反应和致病性。以前开发的内部工具、公开可用的既定相关工具 项目中开发的软件包和新生物信息学工具将被集成、自动化并 然后应用于项目生成的数据，并通过以下方式提供给更大的研究社区： 基于Web的接口。与EBOV相关的公开数据和当地产生的数据将被组织成 项目调查人员和其他人可以使用的数据库来测试假设。以下具体目标 将由核心小组与项目调查人员密切合作牵头。 目标1。表征受影响的转录、转录后和翻译后机制 EBOV感染细胞和生物体。旨在揭示宿主基因调控机制的工具 受EBOV感染的影响将被开发和应用。差异表达、修改和处理 将鉴定感染和未感染细胞之间的RNA和基因组调控区域。微分 还将鉴定翻译后蛋白质修饰。具体改变的调节机制将是 使用转录因子、辅因子、表观遗传调节因子、RNA结合蛋白和microRNA进行预测 富集分析核心将系统地交叉感染修饰基因座的基因组位置， 处理了包含20，000多个人类全基因组公开数据库的定制数据库， 揭示EBOV感染的分子过程和影响因素。 目标2.体外和体内对EBOV的模型细胞类型特异性应答。核心将建立一个一体化的 通过结合项目产生的数据，建立控制EBOV发病机制的模型。本 最后，他们将采用基于网络的数据集成方法，使用基于网络传播的技术。 简而言之，建模方法将被应用于发现转录， 转录后和翻译后数据来定义宿主和病毒因子，其（1）反映激活状态 网络的功能;（2）控制细胞对EBOV的反应;（3）可以进行诊断和治疗测试。 目标3。通过元数据标准化和互动，促进内部和外部协作 Web和编程数据接口。流线型计算管道将使用最佳标准 工程流程，并通过基于Web的接口发布代码和数据。 生物信息学和建模核心将促进集中的数据收集、交换和分析， 核心成员提供包括遗传学、病毒感染、功能基因组学、基因 监管、蛋白质组学、建模和生物信息学。