Analytics Core

分析核心

• 批准号: 10434071
• 负责人: IVAN P GORLOV
• 金额: $ 29.4万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10434071
• 关键词: Area; Bioinformatics; Biological; Biological databases; Biology; Biometry; Cell Communication; Cells; Centers of Research Excellence; Communities; Complex Analysis; Computer software; Computing Methodologies; DNA sequencing; Data; Data Analytics; Data Scientist; Development; Disease; Disease Outcome; Environment; Epigenetic Process; Etiology; Evaluation; Gene Expression; Gene Expression Regulation; Genotype; Goals; Human Biology; Individual; Informatics; Interdisciplinary Study; Licensing; Maintenance; Mentors; Methods; Mining; Multivariate Analysis; Organism; Phenotype; Population; Process; Protocols documentation; Research; Research Personnel; Research Project Grants; Research Support; Resources; Site; Software Tools; Statistical Algorithm; Statistical Data Interpretation; Statistical Methods; Testing; Time; Visualization; Visualization software; Work; analytical tool; cancer cell; cluster computing; design; disorder risk; frontier; high dimensionality; high throughput analysis; high throughput technology; human disease; infancy; insight; interest; mass spectrometric imaging; mathematical algorithm; member; multidimensional data; new technology; novel strategies; programs; public database; software development; tool; transcriptome sequencing

PROJECT SUMMARY The new frontier in “-omics” is represented by high-throughput analyses of individual cells. These single-celled, high-throughput technologies are being developed for many areas, including imaging, mass spectrometry, and DNA- and RNA-sequencing. Even though single-cell -omics studies are in their relative infancy, they have already revealed new insights behind gene regulation and the etiology of human diseases, including cancer and cell-to-cell interactions. Single-cell -omics methods are especially suited for complex analyses of heterogeneous cell populations involved in disease or phenotypes. The goal of this Core (Analytics) is to develop and to support the development of novel approaches for the analysis of -omics data (with a focus on single-cell -omics data), and to provide analytical and computational support for the research projects. This goal requires the integration, maintenance, and adequate statistical analysis of disparate high-dimensional data, arising from: (i) high-throughput analyses proposed by research projects of this COBRE; and (ii) publicly available databases. Utilization of publicly available -omics data is important for validating the interpretation and prioritization of the findings from individual studies. The “-omics revolution” has already generated a massive (and still growing) volume of data that mostly remains a “hidden treasure”; this resource is particularly valuable for re-mining and for asking new questions of old data. By using already available -omics data, this Core aims to put this hidden treasure to work. The use of high-dimensional data requires the capacity to obtain, retrieve, store, track, curate, and process information in a rapid and efficient manner. The members of this Core have extensive expertise and interests that unite their activities among the projects. This Core will enable a robust and profound level of support for research that could not otherwise be achieved. Bioinformatics and biostatistical design and analysis are critical for the planning of large projects proposed in this COBRE; this Core will provide an environment for coordinating research across the program project, and to identify and apply state-of-the-art statistical and computational methods.

项目摘要 “组学”的新前沿是对单个细胞的高通量分析。这些单细胞的， 高通量技术正在开发用于许多领域，包括成像，质谱， DNA和RNA测序。尽管单细胞组学研究还处于相对的初期阶段， 已经揭示了基因调控和人类疾病（包括癌症）病因学背后的新见解 和细胞间的相互作用。单细胞组学方法特别适用于复杂的分析， 参与疾病或表型的异质细胞群体。 该核心（分析）的目标是开发和支持开发新的方法， 分析组学数据（重点是单细胞组学数据），并提供分析和计算 支持研究项目。这一目标需要集成、维护和充分的统计 不同的高维数据的分析，产生于：（一）研究提出的高通量分析 （ii）可公开获得的数据库。利用公开可用的组学数据是 重要的是验证解释和优先次序的结果，从个别研究。“组学” 一场“革命”已经产生了大量（而且仍在增长）的数据，这些数据大多仍然是“隐藏的”。 这一资源对于重新挖掘和对旧数据提出新问题特别有价值。通过使用 已经可用的组学数据，这个核心的目的是把这个隐藏的宝藏工作。使用高维 数据需要有能力以快速和高效的方式获取、检索、存储、跟踪、管理和处理信息， 有效方式 该核心的成员拥有广泛的专业知识和兴趣，将他们的活动结合在一起， 项目该核心将为研究提供强大而深刻的支持，否则将无法实现。 办妥了一批生物信息学和生物统计学的设计和分析对于大型项目的规划至关重要 在COBRE中提出;该核心将为协调整个计划的研究提供一个环境 项目，并确定和应用最先进的统计和计算方法。