Data Management, Analysis, and Resources Dissemination Core

数据管理、分析和资源传播核心

基本信息

批准号：
9919479
负责人：
Christina A Cuomo
金额：
$ 59.58万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
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/9919479
关键词：
Address Bioinformatics Biological Biotechnology Cells Clinical Data Cloud Computing Communicable Diseases Communities Computer Analysis Computer software Computing Methodologies Custom Data Data Analyses Data Set Data Storage and Retrieval Development Diagnosis Documentation Ensure Funding Generations Genomic Centers for Infectious Diseases Genomics Goals Human Resources Hybrids Individual Infection Infectious Diseases Research Infrastructure Institutes Institutional Review Boards Metadata Metagenomics Methods Microbe National Institute of Allergy and Infectious Disease Parasites Population Process Protocols documentation Quality Control Reagent Recording of previous events Research Research Project Grants Resources Sampling Scientist Secure Sequence Analysis Software Engineering Software Tools System Technology Time Training Update Variant Viral Virulence Work analytical tool biodefense bioinformatics resource cluster computing combat computational pipelines computer infrastructure data management data pipeline data standards data tools diverse data drug sensitivity experience flexibility genomic data improved innovation method development new technology novel novel strategies pathogen prevent programs public repository quality assurance software infrastructure tool transmission process transposon sequencing vector

项目摘要

Data Management, Analysis, and Resources Dissemination Core summary The Broad Institute’s Genomic Center for Infectious Disease (GCID) will continue to apply its deep experience in large-scale genomic analysis to study the virulence, transmission, and drug sensitivity of pathogens as well as their critical interactions with hosts, vectors and other microbes. Achieving this will require combining data sets from diverse technologies with bioinformatics capabilities and computational analyses. The Broad GCID will draw on its expertise building and maintaining efficient computational processes and computational infrastructure to create a robust Data Management, Analysis, and Resources Dissemination Core. The Core will support our Research Projects with high-throughput sample tracking, metadata and data management, large-scale analysis, development of novel computational methods, and the rapid release of high quality data and associated resources. Our Data Core will harness the extensive technological capabilities of the Broad Institute and the vast experience of our personnel in genomic research to serve the ambitious goals of our Research Projects. The Core will also maintain a program of ongoing method development tied to innovations in the Technology Core and the goals of the Research Projects; these include methods for strain analysis in metagenomic samples, improved quality control analysis of sequence data, updating assembly methods to use long read- and hybrid-data, customized 10x Genomics data applications, analysis of horizontally transferred sequences, methods for Tn-Seq analysis, and training and evaluating new methods for variant calling. These methods are cross-cutting across our Research Projects and will be widely applicable to genomic infectious disease research. In addition to releasing data to public repositories, the Data Core will continue to move pipelines, processes, and data sets to the cloud, to accelerate the infectious research community’s access to our data and tools and enable others to reproduce our analyses.

数据管理、分析和资源传播核心摘要布罗德研究所传染病基因组中心（GCID）将继续运用其深厚的经验用于大规模基因组分析，以研究病原体的毒力、传播和药物敏感性因为它们与宿主、载体和其他微生物的关键相互作用需要结合数据。来自具有生物信息学能力和计算分析的多种技术的集合。将利用其专业知识构建和维护高效的计算流程和计算基础设施，以创建强大的数据管理、分析和资源传播核心。将通过高通量样本跟踪、元数据和数据管理来支持我们的研究项目，大规模分析、新型计算方法的开发以及高质量数据的快速发布我们的数据核心将利用广泛的技术能力。研究所和我们人员在基因组研究方面的丰富经验，为我们的宏伟目标服务研究项目还将维持与创新相关的持续方法开发计划。技术核心和研究项目的目标；其中包括应变分析方法宏基因组样本，改进序列数据的质量控制分析，更新要使用的组装方法长读和混合数据、定制的 10x 基因组数据应用、水平传输分析序列、Tn-Seq 分析方法以及训练和评估变体识别的新方法。方法在我们的研究项目中是交叉的，并将广泛适用于基因组感染除了向公共存储库发布数据外，数据核心还将继续移动。将管道、流程和数据集传输到云端，以加速传染病研究社区的访问我们的数据和工具，并使其他人能够重现我们的分析。