Computational Analysis

计算分析

• 批准号: 8976268
• 负责人: WALTER Lawrence RUZZO
• 金额: $ 19.01万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8976268
• 关键词: Address; Bioinformatics; Biological; Biology; Biomedical Research; Cardiac Myocytes; Chromatin; Chromosome Mapping; Collaborations; Communities; Computer Analysis; Data; Data Analyses; Data Set; Databases; Development; Diapause; Disease; Engineering; Epiblast; Genes; Genetic Transcription; Genome; Goals; High-Throughput Nucleotide Sequencing; Human; Imagery; Individual; Institution; Investigation; Investments; Knowledge; Knowledge Discovery; Left; Link; Maps; Measurement; Measures; Messenger RNA; Metadata; MicroRNAs; Molecular; Noise; Ontology; Pathway interactions; Play; Procedures; Process; Protein Isoforms; Protocols documentation; Qualifying; RNA; Reading; Research Personnel; Resources; Role; Running; Sampling; Source; Staging; Structure; Synapses; Technology; The Cancer Genome Atlas; Transcript; Universities; Untranslated RNA; Variant; Washington; Work; biological systems; comparative; computer science; computerized data processing; differential expression; embryonic stem cell; expectation; experience; genetic signature; genome browser; genomic data; human embryonic stem cell; implantation; insight; interest; next generation sequencing; novel; overexpression; preimplantation; programs; repository; research study; self-renewal; tool; transcriptome sequencing; Address; Bioinformatics; Biological; Biology; Biomedical Research; Cardiac Myocytes; Chromatin; Chromosome Mapping; Collaborations; Communities; Computer Analysis; Data; Data Analyses; Data Set; Databases; Development; Diapause; Disease; Engineering; Epiblast; Genes; Genetic Transcription; Genome; Goals; High-Throughput Nucleotide Sequencing; Human; Imagery; Individual; Institution; Investigation; Investments; Knowledge; Knowledge Discovery; Left; Link; Maps; Measurement; Measures; Messenger RNA; Metadata; MicroRNAs; Molecular; Noise; Ontology; Pathway interactions; Play; Procedures; Process; Protein Isoforms; Protocols documentation; Qualifying; RNA; Reading; Research Personnel; Resources; Role; Running; Sampling; Source; Staging; Structure; Synapses; Technology; The Cancer Genome Atlas; Transcript; Universities; Untranslated RNA; Variant; Washington; Work; biological systems; comparative; computer science; computerized data processing; differential expression; embryonic stem cell; expectation; experience; genetic signature; genome browser; genomic data; human embryonic stem cell; implantation; insight; interest; next generation sequencing; novel; overexpression; preimplantation; programs; repository; research study; self-renewal; tool; transcriptome sequencing

The goal of Computational Analysis Core B is to manage, analyze, and extract maximum scientific value from the data generated by the investigators of this Program Project. The primary emphasis of Core B will be to assist Program investigators in the analysis of RNA-Seq data and to connect the results of these measurements with prior knowledge to unveil new biology. Importantly, these data will provide a rich source for discovering previously unappreciated shifts in mRNA isoform usage, novel microRNAs and biologically relevant noncoding transcripts. The Core reflects a novel collaboration between researchers spanning two institutions that collectively possess immense expertise and resources in computation and data analysis: The University of Washington (UW) Department of Computer Science & Engineering and Sage Bionetworks, a non-profit biomedical research organization created to revolutionize how researchers approach the complexity of human biological information and the treatment of disease. The vast data sets generated by next-generation sequence technologies create enormous opportunities but also significant challenges. Core B will assure optimal management and interpretation of data obtained from the RNA-Seq studies proposed by Projects 1, 3 and 4 and Core A. These studies will generate RNA-Seq data for both short (e.g., microRNA) and long (e.g., messenger RNA and long noncoding RNA) protocols. As described below. Core Director Dr. Ruzzo (UW), and co-investigators Dr. Brig Mecham and Dr. Adam Margolin (Sage) are exceptionally well qualified to carry out the proposed work, and an existing collaboration with them has already generated interesting findings as described in Project 1 (Blau). In support of the Program, Core B's activities will vary in accordance with the needs of the individual Projects. Core B will provide an essential resource to P01 investigators who have not previously had access to formalized bioinformatics support. For investigators with existing bioinformatics collaborations Core B will assist in bringing uniform "best practices" to quality assessment, analysis and interpretation of RNA-Seq data while minimizing duplication of effort. This includes developing procedures and simple automated workflows to integrate existing specialized tools to provide a unified framework for storage, comparison, analysis and visualization of these data sets. Interaction with Sage Bionetworks will be particularly valuable, exploiting their tools for integration and visualization of diverse biological data, and their ongoing effort to standardize and distribute all publicly available microarray and sequencing data. This work required developing automated workflows that reliably processed 15,000 distinct microarray data sets, yielding standardized information in a usable format for the community. Similar work is underway for RNASeq data. These workflows will serve as a template for the proposed analyses. Additionally, Dr. Ruzzo's experience with next-generation sequence technologies (both RNA-Seq and ChlP-Seq analyses) and expertise in prediction of conserved noncoding RNAs offers the prospect of identifying important, novel players in the unique biological systems addressed by this POI.

计算分析核心B的目的是从该计划项目的研究人员生成的数据中管理，分析和提取最大的科学价值。核心B的主要重点是协助计划调查人员分析RNA-seq数据，并将这些测量结果与先验知识联系起来，以揭示新的生物学。重要的是，这些数据将为发现以前未批准的mRNA同工型使用，新型microRNA和生物学上相关的非编码转录本的转移提供丰富的来源。 核心反映了跨越两个机构的研究人员之间的新型合作，这些机构在计算和数据分析中统一拥有巨大的专业知识和资源：华盛顿大学计算机科学与工程系和Sage Bionetworks和Sage Bionetworks，这是一个非营利的生物医学研究组织，这是一个旨在彻底改变研究人员的生物学信息和疾病的复杂性。 下一代序列技术产生的庞大数据集创造了巨大的机会，但也带来了重大挑战。核心B将确保从项目1、3和4和核心A提出的RNA-seq研究获得的数据的最佳管理和解释。这些研究将生成简短（例如MicroRNA）和LONG（例如Messenger RNA和长期非编码RNA）方案的RNA-SEQ数据。如下所述。核心董事Ruzzo博士（UW）和共同研究人员Brig Mecham博士和Adam Margolin博士（Sage）（Sage）非常有资格从事拟议的工作，并且现有的合作已经产生了Project 1（Blau）中所述的有趣发现。 为了支持该计划，核心B的活动将根据各个项目的需求而有所不同。核心B将为P01调查人员提供重要的资源，这些调查人员以前曾获得正式的生物信息学支持。对于现有生物信息学合作的调查人员，核心B将有助于将统一的“最佳实践”带入质量评估，分析和解释RNA-seq数据，同时最大程度地减少努力的重复。这包括开发过程和简单的自动化工作流程，以整合现有的专用工具，以提供统一的框架，以存储这些数据集的存储，比较，分析和可视化。与Sage Bionetworks的互动将特别有价值，利用了它们的工具以整合和可视化不同的生物学数据，并持续进行标准化和分发所有公开可用的微阵列和测序数据。这项工作需要开发自动化工作流，这些工作流可靠地处理15,000个不同的微阵列数据集，并为社区提供可用格式的标准信息。 RNASEQ数据正在进行类似的工作。这些工作流将作为建议分析的模板。此外，Ruzzo博士在下一代序列技术（RNA-Seq和CHLP-Seq分析）和预测保守非编码RNA的专业知识方面的经验提供了识别该POI探讨的独特生物系统中重要的新颖参与者的前景。