Bioinformatics & Data Management

生物信息学

• 批准号: 10176371
• 负责人: Yufeng Shen
• 金额: $ 32.68万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-25 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10176371
• 关键词: ATAC-seq; Age; Alternative Splicing; B-Cell Antigen Receptor; B-Lymphocytes; B-cell receptor repertoire sequencing; Bioinformatics; Biological Testing; Biomedical Engineering; Cells; Clonal Expansion; Clone Cells; Collaborations; Communities; Complex; Computer Analysis; Computing Methodologies; Data; Data Analyses; Data Set; Database Management Systems; Databases; Deposition; Development; Environment; Flow Cytometry; Funding; Genbank; Gene Expression; Gene Expression Profile; Genes; Genetic Transcription; Genome; Genomics; Goals; Grouping; High-Throughput Nucleotide Sequencing; Human; Immune; Immune system; Immunity; Immunoglobulin Somatic Hypermutation; Immunologic Receptors; Immunology; Individual; Infrastructure; Investigation; Joints; Laboratories; Link; Lymphocyte; Maintenance; Methodology; Methods; Molecular; Pathway interactions; Performance; Population; Positioning Attribute; Procedures; Proteomics; Published Database; Publishing; Quality Control; Receptor Cell; Receptor Gene; Regulator Genes; Research; Research Personnel; Residencies; Sampling; Schools; Sequence Analysis; Services; Statistical Methods; System; Systems Biology; T-Cell Receptor; T-Cell Receptor Genes; T-Lymphocyte; T-cell receptor repertoire; Technology; Testing; Time; Tissues; Transcript; Trees; Universities; Visualization; Work; analytical method; base; bioinformatics pipeline; cell type; computational pipelines; data integration; data management; data quality; data repository; data sharing; data submission; data tools; differential expression; experience; high standard; high throughput analysis; high throughput technology; human tissue; improved; large datasets; mathematical analysis; method development; next generation sequencing; novel; programs; receptor; single-cell RNA sequencing; transcriptome; transcriptome sequencing; transcriptomics

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.

核心D：项目摘要 该核心的主要目的是制定计算过程，以分析来自 B细胞和T细胞库，包括：高吞吐量测序RNA-seq和流式细胞仪数据，并应用 他们研究拟议项目中产生的组织特定数据。 T细胞和B细胞库测序 受体基因提供有关克隆谱系和T / B细胞组织特异性膨胀的信息 人群，这是测试项目1、2和4中假设的关键组成部分。RNA-Seq是一个强大的 谱图基因表达和替代剪接的方法，这对于研究特定状态很重要 淋巴细胞和不同组织的局部环境的含量，将在项目1、2和3中广泛应用。 对于所有项目 因此，我们可以将希望精确研究的不同免疫细胞群体分开。 ，我们有三个特定的 服务目的是在此核心中：（1）建立并应用计算方法来分析RNA-Seq数据以查找 区分细胞线条和组织的表达式的特征。我们有一个成熟的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。该领域处于活动状态 发展;正在发布较新的方法。对于核心的这一部分，我们将评估 新的和现有的方法，并优化找到定义本地的表达式签名的过程 不同组织和免疫细胞状态的环境。我们将对项目进行计算分析 1至3。（2）建立并应用计算方法来分析T和B细胞受体库 测序数据。我们已经发布了ImmunedB的内部生物信息学管道，以分析大规模帐户 来自Illumina Hiseq或Miseq平台的TCR和BCR曲目测序数据。对于核心的这一部分，我们 将继续开发用于表征曲目多样性和比较的分析方法 跨个体的不同组织。然后，我们将执行TCR的计算和数学分析 和项目1、2和4的BCR曲目。（3）创建用于数据集成，可视化和的新颖工具 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TCR/BCR和RNA-seq数据来回答克隆 特定的转录程序。利用新型可视化来关联序列库（BCR/TCR）和 基因表达库数据我们将将相关的克隆信息与我们的相关基因表达数据联系起来 其他RNA-seq/ atac-seq数据存储库。