Bioinformatics and Biostatistics Data Analysis Core

生物信息学和生物统计学数据分析核心

• 批准号: 10478004
• 负责人: Sohrab Shah
• 金额: $ 32.83万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-13 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10478004
• 关键词: Achievement; Address; Animals; Back; Bioinformatics; Biological Specimen Banks; Biometry; CDK4 gene; Cells; Chromosomal Instability; Chromosomes; Clinical Data; Clinical Research; Clinical Trials; Cluster Analysis; Collection; Computational Biology; Computer Analysis; Computing Methodologies; Custom; DNA; DNA Repair; DNA Repair Disorder; DNA sequencing; Data; Data Analyses; Data Collection; Data Sources; Databases; Defect; Deposition; Development; Disease; Ensure; Estrogen receptor positive; Formulation; Genetic; Genomic Instability; Genomics; Goals; Heterogeneity; High Performance Computing; Hormones; Human; Human Resources; Immune; Immunocompetent; Infrastructure; Laboratory Research; Length; Link; Maintenance; Mediating; Memorial Sloan-Kettering Cancer Center; Metadata; Metastatic breast cancer; Methods; Modeling; Molecular; Mutation; Pathology; Patients; Periodicity; Phenotype; Prevalence; Procedures; Prospective cohort study; RNA; Reproducibility; Research; Research Design; Research Personnel; Research Project Grants; Resistance; Resources; Sample Size; Sampling; Scientist; Services; Single Nucleotide Polymorphism; Space Models; Specimen; Statistical Algorithm; Statistical Data Interpretation; Statistical Methods; Stratification; Time; Time Series Analysis; Validation; Variant; Visualization; Work; analysis pipeline; base; bioinformatics tool; biomarker development; biomarker validation; cBioPortal; cancer genome; cancer genomics; cell type; clinical care; clinical trial analysis; clinically significant; computerized data processing; computerized tools; data integration; design; diverse data; exome; experience; fitness; follow-up; genome sequencing; genomic data; genomic signature; homologous recombination; innovation; laboratory experiment; malignant breast neoplasm; member; mouse model; multimodality; multiple data sources; neoplastic cell; next generation sequencing; novel; online resource; patient derived xenograft model; preclinical study; research study; single cell sequencing; single-cell RNA sequencing; success; telomere; tool; transcriptome; transcriptome sequencing; translational goal; tumor; tumor-immune system interactions; validation studies; whole genome

ABSTRACT The Biostatistics and Computational Genomics Core will provide support in statistical and computational analysis of sequencing data, biomarker development and validation, pre-clinical study design and analysis to meet the needs of the SPORE Research Projects and interact with other Cores and institutional resources to achieve the scientific and translational purposes of the SPORE. The Research Projects in this SPORE require a broad range of statistical and bioinformatics expertise, including tools for the analysis and visualization of whole genome sequencing and single cell sequencing data. The Core will provide a team of dedicated personnel with extensive experience and strong track record of developing innovative statistical and computational methods. We will maintain and expand current tools and pipelines to assist the design and analysis of the SPORE research projects, and provide centralized support for data collection, processing, quality assessment, and normalization procedures to facilitate data integration and downstream analysis and visualization. Importantly, the core will devote significant effort in developing innovative computational tools that aim to detect, quantify, and track genomic signatures of specific DNA repair defects and/or genetic instability at the tumor bulk and single cell levels. In addition, the Core will synergize with the current infrastructure available at Memorial Sloan Kettering Cancer Center to provide the SPORE investigators not only with the state-of-the-art computational biology methods, but also with novel computational tools to address specific analytical challenges germane to the success of the SPORE Research Projects.

摘要 生物统计学和计算基因组学核心将提供统计和 测序数据的计算分析、生物标志物开发和验证、临床前 研究设计和分析，以满足SPORE研究项目的需求，并与 其他核心和机构资源，以实现科学和翻译的目的， 孢子。本SPORE中的研究项目需要广泛的统计和 生物信息学专业知识，包括全基因组分析和可视化工具 测序和单细胞测序数据。核心将提供一个专门的 具有丰富经验和良好的发展创新统计记录的人员 和计算方法。我们将维持和扩大现有的工具和管道， 设计和分析的SPORE研究项目，并提供集中的支持， 数据收集、处理、质量评估和标准化程序，以促进数据 集成和下游分析和可视化。重要的是，核心将致力于 在开发创新的计算工具，旨在检测，量化， 跟踪肿瘤中特定DNA修复缺陷和/或遗传不稳定性的基因组特征 体细胞和单细胞水平。此外，Core将与当前的基础设施协同工作， 可在纪念斯隆凯特琳癌症中心提供SPORE研究者， 不仅有最先进的计算生物学方法，而且有新的计算生物学方法， 解决与SPORE研究成功密切相关的特定分析挑战的工具 项目