Bioinformatics & Data Management

核心D：项目总结 该核心的主要目标是开发计算程序，用于分析来自 B细胞和T细胞库包括：高通量测序RNA-seq和流式细胞术数据，并应用于 研究拟议项目中产生的组织特异性数据。T细胞和B细胞库测序 受体基因的表达提供了T / B细胞克隆谱系和组织特异性扩增的信息 这是检验项目1、2和4中假设的一个关键组成部分。RNA-seq是一个强大的 一种分析基因表达和选择性剪接的方法，这对研究特定状态很重要 的淋巴细胞和不同组织的局部环境，并将广泛应用于项目1，2和3。 对于所有项目来说，分析大规模多维流式细胞术数据的简化程序至关重要 这样我们就可以精确地分离我们想要研究的不同免疫细胞群。，我们有三个具体的 服务目标：（1）建立和应用计算方法来分析RNA-seq数据， 区分细胞系和组织的表达特征。我们有成熟的RNA分析管道- 哥伦比亚基因组中心下一代测序实验室的seq数据。该字段处于活动状态 开发;更新的方法正在发布。对于核心的这一部分，我们将评估 新的和现有的方法，并优化过程，寻找表达式签名，定义本地 不同组织和免疫细胞状态下的环境。我们将对项目进行计算分析 从1到3。(2)建立和应用计算机方法分析T和B细胞受体库 测序数据。我们已经发布了immuneDB，这是一个内部生物信息学管道，可以分析大量的账户， 来自Illumina HiSeq或MiSeq平台的TCR和BCR库测序数据。对于这部分核心，我们 将继续开发分析方法，以确定剧目多样性的特点，并比较 不同的组织。然后，我们将进行TCR的计算和数学分析 以及项目1、2和4的巴塞尔公约区域中心汇编。(3)创建用于数据集成、可视化和 高通量测序数据的管理。我们将解决有关数据集成的规模问题， 注释和分析。更具体地说，我们将结合联合收割机TCR/BCR和RNA-Seq数据来回答克隆- 特定的转录程序。利用新的可视化来关联序列库（BCR/TCR）和 基因表达谱数据，我们将链接相关的克隆信息相关的基因表达数据，在我们的 其他RNA-Seq/ ATAC-Seq数据库。

CORE D: PROJECT SUMMARY The main goal of this core is to develop computational procedures for the analysis of high throughput data from B cell and T cell repertoires including: high throughput sequencing RNA-seq and flow cytometry data, and apply them to study the tissue specific data generated in the proposed projects. T cell and B cell repertoire sequencing of receptor genes provides information about clonal lineage and tissue-specific expansion of T / B cell populations, which is a key component to test the hypotheses in project 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 project 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 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 project 1 through 3. (2) Establish and apply computational approaches to analyze T and B cell receptor repertoire sequencing data. We have published immuneDB an in-house bioinformatics pipeline to analyze massive account of TCR and BCR repertoire sequencing data from Illumina HiSeq or MiSeq platforms. For this part of 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 project 1, 2 and 4. (3) Create novel tools for Data integration, visualization, and management of high-throughput sequencing data. We will solve issues of scale regarding data integration, annotation and analysis. More specifically we will combine TCR/BCR and RNA-Seq data to answer clone- specific transcription programs. Utilizing novel visualizations to associate sequence repertoire (BCR/TCR) and gene expression repertoire data we will link relevant clonal information to related gene expression data in our other RNA-Seq/ ATAC-Seq data repositories.