Single-Cell & Computational Biology Core

摘要 单细胞测序和计算生物学核心 B 将成为设计和计算的中心枢纽 实施所有单细胞测序实验，以及强大计算能力的应用 算法对此类数据以及其他批量 mRNA 测序和代谢组数据生成集成的 转移进展背后的基因网络和调控因素模型。所有三个中心项目都将 依靠转录组、核糖体分析、单细胞的生成，系统地处理转移 测序、蛋白质组学、代谢组学和染色可及性数据。因此，该中心将严重依赖 Saeed 开创的严格且统计上合理的计算生物学和生物信息学方法 Tavazoie，系统生物学领域的领导者，他将成为该核心的联合领导者。同样，所有三个项目都将 广泛采用单细胞测序方法来定义和表征细胞间相互作用和细胞 转移性肿瘤内的基因表达状态并开发新的单细胞方法。曹俊跃 领导者 单细胞测序技术开发与应用方面的研究将是该核心的共同领导者。的 这些研究人员的综合系统级重点应用于从不同的数据生成的多层数据 转移进展阶段将能够建立前所未有的集成系统级 乳腺癌和结直肠癌转移模型——为进一步的机制研究提供框架 将完善这个模型，最终揭示关键节点，当基因或药理学中断时 将预防和根除转移性疾病。主要应用的计算方法 转移进展的问题包括： 1. iPAGE：基因表达算法的信息理论路径分析，允许系统地分析 发现任何细胞类型转录组之间差异调节的途径。 2. FIRE：一种信息论算法，可识别基因中的局部 DNA 和 RNA 元件 表达变化，揭示了控制这种变化的相关转录因子和 RNA 结合蛋白 程序。 3. TEISER：一种从转录组中发现RNA调控元件并实现识别的算法 它们的反式结合因子。 4. 整合大规模转录组和表型特征（例如生存）的算法 癌症概要涉及关键的临床相关基因。

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.