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 实现所有单细胞测序实验，以及强大的计算能力的应用 算法将这样的数据以及其他大量的mRNA测序和代谢数据生成完整的 转移进展背后的基因网络和调控因素的模型。所有三个中心项目都将 系统地处理转移，依赖于转录体、核糖体、单细胞的生成 测序、蛋白质组学、代谢组学和显色可及性数据。因此，该中心将严重依赖于 赛义德开创的严谨和统计合理的计算生物学和生物信息学方法 Tavazoie是系统生物学的领导者，他将成为This Core的联合领导者。同样，所有三个项目都将 广泛使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。领袖曹军月 在单细胞测序技术的开发和应用方面，将共同引领这一核心。这个 这些调查者的综合系统级重点应用于从DISTINCT生成的多层数据 转移进展的阶段将使建立史无前例的综合系统水平成为可能 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 将改进这一模型，最终揭示当基因或药物中断时的关键节点 将预防和根除转移性疾病。将首先应用于 转移进展的问题包括： 1.IPAGE：一种基于信息论的基因表达路径分析算法 发现在任何细胞类型的转录本中存在差异调节的通路。 2.FIRE：一种信息论算法，它识别基因背后的局部DNA和RNA元件 表达变化，揭示相关的转录因子和控制这种变化的RNA结合蛋白 程序。 3.TEISER：一种从转录本中发现RNA调控元件的算法，使识别成为可能 它们的反式结合因子。 4.一种集成大规模转录和表型特征(如存活率)的算法 癌症纲要，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.