Computational tools for the analysis of high-throughput immunoglobulin sequencing

描述（由申请方提供）：我们的免疫系统有效应答病原体攻击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）受体的多样性。每个IG受体都是独特的，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，最初通过其IG受体以低亲和力结合抗原的B细胞通过体细胞超突变（SHM）和亲和力依赖性选择的循环被修饰以产生高亲和力记忆细胞和浆细胞。这种亲和力成熟是T细胞依赖性适应性免疫应答的关键组成部分。它有助于防止快速变异的病原体，并成为许多疫苗的基础。大规模表征B细胞IG库现在在人类中是可行的。在高通量测序技术的巨大进步的推动下，这些数据正在开辟令人兴奋的研究途径。B细胞库的特征，包括多态性、偏向性片段使用和多样性，可以与临床相关结果相关，例如对以下疾病的易感性： 感染或疫苗接种反应。这些数据也有助于对B细胞和适应性免疫的基本理解。特别是，估计积极和消极选择的能力 从IG突变模式中获得的突变不仅在理解对病原体的免疫应答方面具有广泛的应用，而且对于确定体细胞超突变在自身免疫和B细胞癌症中的作用也至关重要。尽管很有前途，但库规模的数据也对分析提出了根本性的挑战，需要开发新技术并重新思考现有方法，这些方法无法扩展到生成的数百万个序列。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来分析高通量IG测序数据集的新方法。将开发表征库特性的新方法，这些方法有可能用作疾病风险、诊断和预后的生物标志物。具体而言，将制定方法，以便：（目的1）将序列分组到克隆中并改进V（D）J区段分配，从而允许鉴定体细胞突变，（目的2）模拟SHM突变性和置换模式，从而可以在各组之间对其进行定量和比较，从而提供对潜在突变机制的了解，以及（目的3）量化选择并表征克隆多样性，提供关于亲和力成熟和响应动力学的信息。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实验金标准数据集的测试进行验证。所有这些方法都将通过网络界面和开源代码的分发广泛提供。

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.