Single-Cell & Computational Biology Core

总结 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，以及强大的计算能力的应用。 这些数据以及其他批量mRNA测序和代谢组学数据的算法， 转移进展的基因网络和调控因子模型。三个中心项目将 系统地处理转移，依赖于转录组的产生，核糖体谱，单细胞 测序、蛋白质组学、代谢组学和染色质可及性数据。因此，该中心将严重依赖 由Saeed开创的严格和统计上合理的计算生物学和生物信息学方法 Tavazoie，系统生物学的领导者，他将成为这个核心的共同领导者。同样，这三个项目将 广泛采用单细胞测序方法来定义和表征细胞-细胞相互作用和细胞 转移性肿瘤中的基因表达状态，并开发新的单细胞方法。曹俊跃，一位领导者 在单细胞测序技术的开发和应用将是这一核心的共同领导者。的 这些研究人员的系统级重点适用于从不同的系统生成的多层数据， 转移进展的阶段将使建立一个前所未有的综合系统水平， 乳腺癌和结直肠癌转移模型-为进一步的机制研究提供了框架， 将完善这个模型，最终揭示关键节点，当基因或基因突变中断时， 可以预防和根除转移性疾病计算方法将首先应用于 转移进展的问题包括： 1. iPAGE：基因表达的信息论途径分析算法，允许系统的 发现在任何细胞类型的转录组中差异调节的途径。 2. FIRE：一种信息论算法，用于识别基因基础的局部DNA和RNA元件 表达变化，揭示相关的转录因子和RNA结合蛋白， 程序. 3. TEISER：一种从转录组中发现RNA调控元件的算法， 它们的反式结合因子。 4.一种算法，它集成了转录组和表型特征（如生存），从大规模的 癌症纲要，以牵连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.