Computational tools for the analysis of high-throughput immunoglobulin sequencing

描述（由申请人提供）：我们的免疫系统对致病性攻击或疫苗接种的有效反应能力取决于B淋巴细胞表达的多种免疫球蛋白（Ig）受体。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受体结合低亲和力抗原的B细胞通过体细胞超突变（SHM）和亲和力依赖选择的周期进行修饰，产生高亲和力记忆细胞和浆细胞。这种亲和成熟是T细胞依赖性适应性免疫反应的关键组成部分。它有助于预防迅速变异的病原体，是许多疫苗的基础。B细胞Ig谱的大规模表征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，这些数据开辟了令人兴奋的研究途径。B细胞库的特征，包括多态性、偏倚片段的使用和多样性，可以与临床相关的结果相关，如对

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.