Computational tools for the analysis of high-throughput immunoglobulin sequencing

描述(由申请人提供)：我们的免疫系统有效应对病原性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(Ig)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组装而成。在免疫应答过程中，最初通过Ig受体与低亲和力抗原结合的B细胞通过体细胞高突变(SHM)和亲和力依赖的选择循环进行修饰，以产生高亲和力的记忆细胞和浆细胞。这种亲和力的成熟是T细胞依赖的适应性免疫反应的关键组成部分。它有助于防止快速突变的病原体，也是许多疫苗的基础。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。在高通量测序技术的戏剧性改进的推动下，这些数据正在开辟令人兴奋的查询途径。B细胞谱的特征，包括多态、有偏见的片段使用和多样性，可以与临床相关的结果相关，如易感性 感染或疫苗接种反应。这些数据还有助于对B细胞和获得性免疫的基本了解。特别是，估计正面和负面选择的能力 来自Ig突变模式的研究不仅在了解病原体的免疫应答方面有广泛的应用，而且对于确定体细胞高突变在自身免疫和B细胞癌中的作用也是至关重要的。尽管很有希望，但曲目规模的数据也对分析提出了根本挑战，需要开发新技术，并重新思考现有方法，这些方法不能扩展到正在产生的数百万个序列。该提案描述了通过生物信息学和统计学方法开发、计算建模和序列数据挖掘相结合的方法来分析高通量免疫球蛋白测序数据集的新方法。将开发表征曲目特性的新方法，这些方法有可能用作疾病风险、诊断和预后的生物标记物。具体地说，将开发以下方法：(目标1)将群体序列转化为克隆并改进V(D)J片段分配，从而能够识别体细胞突变，(目标2)建立SHM突变和替换模式的模型，以便能够量化和跨群体比较它们，从而为潜在的突变机制提供洞察，以及(目标3)量化选择和表征克隆多样性，提供关于亲和力成熟和反应动力学的信息。这些方法将通过基于模拟的研究以及对来自人类和小鼠系统的新的实验性金标准数据集进行测试来验证。所有这些方法都将通过网络界面和分发开放源码广泛提供。

DESCRIPTION (provided by applicant): 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.