Computational tools for the analysis of high-throughput immunoglobulin sequencing

项目摘要/摘要 我们的免疫系统有效应对病原挑战或疫苗接种的能力取决于 B淋巴细胞表达的免疫球蛋白受体的不同谱系。每个Ig受体都是独一无二的， 在淋巴细胞发育过程中通过基因片段的体细胞重组而组装。在.期间 在免疫反应的过程中，最初通过其Ig受体以低亲和力结合抗原的B细胞是 通过体细胞超突变(SHM)和亲和力依赖的选择循环进行修改，以产生高密度的 亲和记忆和浆细胞。这种亲和力成熟是T细胞依赖适应性的关键组成部分 免疫反应。它有助于防止快速变异的病原体，并奠定了许多 疫苗。B细胞免疫球蛋白谱系的大规模鉴定现在在人类身上是可行的。由 高通量测序技术的戏剧性改进，这些数据打开了令人兴奋的大门 调查的途径。B细胞谱系的特征，包括多态、有偏见的片段使用和 多样性，可能与临床相关的结果相关，如感染易感性或接种疫苗 回应。这些数据还有助于对B细胞和获得性免疫的基本了解。在……里面 特别是，从免疫球蛋白突变模式估计正选择和负选择的能力是广泛的 应用程序不仅用于了解对病原体的免疫反应，而且对于确定 体细胞高突变在自身免疫和B细胞癌中的作用虽然前景看好，但剧目规模 数据也对分析提出了根本的挑战，需要开发新的技术和 重新思考现有的方法，这些方法不能扩展到生成的数百万个序列。这 提案描述了用于分析高通量免疫球蛋白测序数据集的新方法 通过生物信息学和统计方法的结合开发，计算建模和 序列数据挖掘。将开发新的方法来表征曲目属性，这些方法具有 有可能作为疾病风险、诊断和预后的生物标志物。具体来说，方法将是 发展到：(目标1)将序列分组为克隆，并改进V(D)J片段分配，从而允许 体细胞突变的鉴定，(目标2)模型SHM突变和替换模式，以便它们可以 量化和跨组比较，从而提供对潜在突变机制的洞察，以及 (目标3)量化选择和描述克隆多样性，提供关于亲和力成熟和 响应动力学。这些方法将通过基于模拟的研究相结合进行验证，如 以及在来自人类和小鼠系统的新的实验性金标准数据集上进行测试。所有的 这些方法将通过网络界面和开放源代码的分发广泛提供。

PROJECT SUMMARY/ABSTRACT The ability of our immune system to respond effectively to pathogenic challenge or vaccination depends on a diverse repertoire of Immunoglobulin (Ig) receptors expressed by B lymphocytes. Each Ig receptor is unique, having been assembled during lymphocyte development by somatic recombination of gene segments. During the course of an immune response, B cell that initially bind antigen with low affinity through their Ig receptor are modified through cycles of somatic hypermutation (SHM) and affinity-dependent selection to produce high- affinity memory and plasma cells. This affinity maturation is a critical component of T cell dependent adaptive immune responses. It helps guard against rapidly mutating pathogens and underlies the basis for many vaccines. Large-scale characterization of B cell Ig repertoires is now feasible in humans. Driven by the dramatic improvements in high-throughput sequencing technologies, these data are opening up exciting avenues of inquiry. Features of the B cell repertoire, including polymorphisms, biased segment usage and diversity, can be correlated with clinically relevant outcomes, such as susceptibility to infection or vaccination response. These data can also contribute to basic understanding of B cells and adaptive immunity. In particular, the ability to estimate positive and negative selection from Ig mutation patterns has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B cell cancers. Although promising, repertoire-scale data also present fundamental challenges for analysis requiring the development of new techniques and the rethinking of existing methods that are not scalable to the millions of sequences being generated. This proposal describes novel approaches for the analysis of high-throughput Ig sequencing data sets enabled through a combination of bioinformatics and statistics method development, computational modeling and sequence data-mining. New ways to characterize repertoire properties will be developed that have the potential for use as biomarkers for disease risk, diagnosis and prognosis. Specifically, methods will be developed to: (Aim 1) group sequences into clones and improve V(D)J segment assignment, thus allowing identification of somatic mutations, (Aim 2) model SHM mutability and substitution patterns so they can be quantified and compared across groups, thus providing insights into underlying mutation mechanisms, and (Aim 3) quantify selection and characterize clonal diversity, providing information on affinity maturation and response dynamics. These methods will be validated through a combination of simulation-based studies, as well as testing on new experimental gold-standard data sets from both human and murine systems. All of the methods will be made widely available through web interfaces and distribution of open-source code.