Bioinformatics

这个核心的主要目标是开发用于分析高通量数据的计算程序 从T细胞受体(TCR)或B细胞受体(BCR)谱系，RNA-SEQ和流式细胞术，并应用它们 研究拟议项目中产生的特定组织数据。TCR和BCR曲目测序 提供有关T/B细胞群体的克隆谱系和组织特异性扩展的信息，这是一种 检验项目1、2和4中假设的关键组件。RNA-seq是研究基因图谱的有力方法 表达和选择性剪接，这对于研究淋巴细胞的特定状态和 不同组织的局部环境，将在项目1、2和3中广泛应用。对于所有项目， 分析大规模多维流式细胞仪数据的简化程序至关重要，这样我们就可以 分离我们想要的不同免疫细胞群；精确研究。我们有三项具体的服务 核心目标：(1)建立和应用计算方法来分析T和B细胞受体 曲目测序数据。我们已经建立了一个内部生物信息学渠道来分析海量 来自LLUMINA HiSeq或MiSeq平台的TCR和BCR曲目测序数据的帐户。对于这一部分 作为核心，我们将继续开发分析方法来表征曲目多样性和 不同组织在不同个体间的谱系比较。然后我们将执行计算和 项目1、2和4的TCR和BCR曲目的数学分析。(2)建立和应用 分析RNA-SEQ数据以找到区分细胞的表达式签名的计算方法 衬布和纸巾。我们在哥伦比亚基因组中心有一条成熟的RNA-seq数据分析管道 下一代测序实验室。这一领域正在积极发展；新的方法正在进行 出版了。对于核心的这一部分，我们将评估新方法和现有方法的性能，以及 优化查找定义不同组织中局部环境的表情签名的过程 和免疫细胞状态。我们将对项目1至3进行计算分析。(3)建立 并应用计算方法分析高通量流式细胞仪数据。其目的是为了找到 不仅是免疫细胞的规范种群，还可以从多个物种中发现新的或稀有的种群 多维流式细胞仪数据。我们将建立一个分析管道，基于现有的和在- 开发R/BioConductor工具。

The main goal of this core is to develop computational procedures for the analysis of high-throughput data from T cell receptor (TCR) or B cell receptor (BCR) repertoire, RNA-seq and flow cytometry, and apply them to study the tissue specific data generated in the proposed projects. TCR and BCR repertoire sequencing provides information about clonal lineage and tissue-specific expansion of T / B cell populations, which is a key component to test the hypotheses in Projects 1, 2 and 4. RNA-seq is a powerful approach to profile gene expression and alternative splicing, which are important for studying the specific states of lymphocytes and local environment of different tissues, and will be applied extensively in Projects 1, 2 and 3. For all projects, a streamlined procedure to analyze large-scale multidimensional flow cytometry data is crucial so we can separate the different immune cell populations we wish to; study precisely. We have three specific service aims in this core: (1) Establish and apply computational approaches to analyze T and B cell receptor repertoire sequencing data. We have established an in-house bioinformatics pipeline to analyze massive accounts of TCR and BCR repertoire sequencing data from lllumina HiSeq or MiSeq platforms. For this part ofthe core, we will continue to develop analytical methods for characterizing repertoire diversity and comparing of repertoire of different tissues across individuals. We will then perform the computational and mathematical analysis of TCR and BCR repertoires for Projects 1, 2 and 4. (2) Establish and apply computational approaches to analyze RNA-seq data to find signatures of expressions that distinguish cell linages and tissues. We have a mature analytical pipeline for RNA-seq data at Columbia Genome Center Next-Generation Sequencing Laboratory. The field is in active development; newer methods are being published. For this part of the core, we will assess the performance of new and existing methods, and optimize the procedure for finding expression signatures that define local environment in different tissues and immune cell states. We will perform the computational analysis for Projects 1 through 3. (3) Establish and apply computational approaches to analyze high-throughput flow cytometry data. The purpose is to find not only canonical populations of immune cells, but also discover novel or rare populations from multi- dimensional flow cytometry data. We will establish an analytical pipeline based on existing and in- development R/Bioconductor tools.