Single-Cell & Computational Biology Core

单细胞

• 批准号: 10271740
• 负责人: Junyue Cao
• 金额: $ 35.98万
• 依托单位: ROCKEFELLER UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10271740
• 关键词: Algorithms; Binding; Bioinformatics; Biological; Biology; Breast Cancer Model; Cell Communication; Cells; Cellular biology; Chromatin; Clinical; Colorectal Cancer; Computational Biology; Computational algorithm; Computing Methodologies; DNA; Data; Data Set; Disease; Distal; Elements; Gene Expression; Gene Expression Profiling; Generations; Genes; Genetic Transcription; Glean; Interruption; Malignant Neoplasms; Metabolic; Metabolite Interaction; Metastatic to; Methodology; Methods; Modeling; Molecular; Neoplasm Metastasis; Organ; Pathway Analysis; Pathway interactions; Pharmacology; Phenotype; Primary Neoplasm; Proteomics; RNA; RNA-Binding Proteins; Regulatory Element; Regulon; Reporter; Research Personnel; Series; Site; System; Systems Biology; Universities; base; cancer cell; cell type; colorectal cancer metastasis; experimental study; innovation; insight; mRNA sequencing; malignant breast neoplasm; metabolic abnormality assessment; metabolomics; metastasis prevention; novel; prevent; programs; ribosome profiling; single cell sequencing; sound; technology development; therapeutic target; transcription factor; transcriptome; transcriptomics

SUMMARY The Single-Cell Sequencing and Computational Biology Core B will be the central hub for devising and implementing all Single-Cell Sequencing experiments, as well as the application of powerful computational algorithms to such data as well as other bulk mRNA sequencing and metabolomic data to generate integrated models of gene networks and regulatory factors underlying metastatic progression. All three Center Projects will approach metastasis systematically, relying on the generation of transcriptomic, ribosomal profiling, single-cell sequencing, proteomic, metabolomic and chromatic accessibility data. As such, this Center will rely heavily on rigorous and statistically sound Computational Biology and Bioinformatics approaches pioneered by Saeed Tavazoie, a leader in Systems Biology, who will be a co-leader of this Core. Similarly, all three Projects will extensively employ Single-Cell Sequencing methods to define and characterize cell-cell interactions and cellular gene expression states within metastatic tumors and to develop novel single-cell methods. Junyue Cao, a leader in Single-Cell Sequencing technology development and application will be a co-leader of this Core. The combined Systems-level focus of these investigators applied to the multi-layered data generated from distinct stages of metastatic progression will enable the establishment of an unprecedented integrated Systems-level model of breast and colorectal cancer metastasis—providing the framework for further mechanistic studies that will refine this model, ultimately revealing critical nodes that when interrupted genetically or pharmacologically will prevent and eradicate metastatic disease. Computational methods that will be foremost applied to the problem of metastatic progression include: 1. iPAGE: an information-theoretic Pathway Analysis of Gene Expression algorithm that allows the systematic discovery of pathways that are differentially modulated across transcriptomes of any cell-types. 2. FIRE: an information-theoretic algorithm that identifies local DNA and RNA elements that underlie gene expression changes, uncovering associated transcription factors and RNA-binding proteins that govern such programs. 3. TEISER: an algorithm that discovers RNA regulatory elements from transcriptomes, enabling identification of their trans-binding factors. 4. An algorithm that integrates transcriptomic and phenotypic features (such as survival) from large-scale cancer compendia to implicate critical clinically-associated genes.

概括 单细胞测序和计算生物学核心B将是设计和 实施所有单细胞测序实验以及强大的计算的应用 此类数据以及其他批量mRNA测序和代谢组数据的算法以生成集成 基因网络的模型和转移性进展的基础调节因素。这三个中心项目将 依靠转录组，核糖体分析，单细胞的产生，有系统地进近转移 测序，蛋白质组学，代谢组和色素可访问性数据。因此，这个中心将严重依赖 SAEED率先进行严格且统计上声音的计算生物学和生物信息学 Tavazoie是系统生物学的领导者，他将成为该核心的共同领导者。同样，这三个项目将 广泛的员工单细胞测序方法来定义和表征细胞 - 细胞相互作用和细胞 基因表达状态在转移性肿瘤中并开发新型的单细胞方法。 Junyue Cao，领导者 在单细胞测序中，技术开发和应用将成为该核心的共同领导者。 这些研究者的组合系统级焦点应用于从不同的多层数据中生成的多层数据 转移进展的阶段将使建立前所未有的集成系统级别 乳腺癌和结直肠癌转移的模型 - 为进一步的机械研究提供框架 将完善该模型，最终揭示关键节点，当一般或药物中断时 将预防和放射性转移性疾病。将外国应用于 转移性进展问题包括： 1。iPage：基因表达算法的信息理论途径分析，该算法允许系统 发现在任何细胞类型的转录组之间调节的途径的发现。 2。火：一种识别基因基于基因的局部DNA和RNA元素的信息理论算法 表达变化，发现相关的转录因子和控制此类的RNA结合蛋白 程序。 3. Teiser：一种从转录组中发现RNA调节元件的算法，实现识别能力 它们的变形因素。 4。从大规模整合转录组和表型特征（例如生存）的算法 癌症汇编牵涉至关键的临床相关基因。