High-throughput informatics for antibodies

抗体高通量信息学

• 批准号: 8835590
• 负责人: Thomas MacCarthy
• 金额: $ 29.65万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8835590
• 关键词: Accounting; Acquired Immunodeficiency Syndrome; Algorithms; Antibodies; Antibody Diversity; Autoimmune Diseases; B lymphoid malignancy; Base Composition; Benchmarking; Binding; Bioinformatics; Chronic Lymphocytic Leukemia; Complex; Computers; DNA; Data; Data Set; Defect; Dengue; Detection of Minimal Residual Disease; Dideoxy Chain Termination DNA Sequencing; Equipment; Excision; Frequencies; Generation of Antibody Diversity; Genes; HIV; Health; High-Throughput Nucleotide Sequencing; Hot Spot; Human; Human Cell Line; IGH@ gene cluster; IGL@ gene cluster; Immune response; Immunoglobulin Genes; Immunoglobulin Somatic Hypermutation; Immunoglobulins; Immunologic Deficiency Syndromes; Infection; Informatics; Internet; Length; Libraries; Location; Lupus Erythematosus; Lymphoma; Malignant Neoplasms; Methods; Mismatch Repair; Modeling; Mus; Mutation; Mutation Spectra; Odds Ratio; Organism; Outcome; Patients; Performance; Process; Reading; Recurrence; Rheumatoid Arthritis; Sampling; Site; Spottings; Statistical Methods; Statistical Models; Testing; Time; Vaccines; activation-induced cytidine deaminase; base; case control; clinical application; comparative; cost; deep sequencing; density; falls; graphical user interface; human disease; improved; influenza virus vaccine; insight; open source; outcome forecast; predictive modeling; repaired; response; tool; user-friendly; vaccine response

DESCRIPTION (provided by applicant): Somatic hypermutation (SHM) of the Immunoglobulin (Ig) loci is a fundamental process in generating antibody diversity. High-throughput methods for profiling mutation spectra of Ig genes such as Roche 454 deep sequencing remain inaccurate and are expensive in part due to specialized bioinformatics required for post-processing. Recent improvements to the Illumina MiSeq platform allowing paired-end 2x300 nt reads will enable more accurate IgV sequencing at a >60 times lower cost than the 454 platform. As costs fall further, platforms such as MiSeq will become common lab equipment, but will only be useful if the appropriate bioinformatics tools are available. Accurate deep sequencing of the Ig loci is rapidly becoming the standard in a broad range of clinical applications including determining prognosis and detection of Minimal Residual Disease in B cell malignancies, characterization of autoimmune diseases and evaluating vaccine responses. In Aim 1 we will develop a user-friendly bioinformatics pipeline (SHMPrep) to improve mutation calls for IgV sequences from the Illumina MiSeq platform. We hypothesize that statistical modeling of independent PCR vs sequencing error effects can improve the quality of MiSeq IgV sequences to levels comparable to Sanger sequencing. The pipeline will be integrated with a previously developed analysis tool (SHMTool) to allow non computer experts, such as most clinicians, to process MiSeq IgV datasets on an ordinary desktop computer. As high-throughput data accumulates it becomes more important to have analysis methods for the data we already have rather than producing yet more data. IgV mutation spectra depend on many factors including base composition, abundance and location of activation induced deaminase (AID) hot and cold spots, Pol-η hot spot composition and overall mutation frequency. This complexity makes it difficult to compare mutation spectra from different IgV regions. In Aim 2 we will develop statistical methods for comparing different IgV regions taking into account sequence composition as well as mutation saturation and strand bias, which is important in identifying repair defects in immunodeficiencies such as AIDS and in B-cell malignancies and other cancers. We still understand little about the differences between the IGHV genes. Why are there so many V regions and such strong associations between particular Ig genes and immune responses? In Aim 3 we will develop a statistical model for predicting mutation frequencies that will allow known molecular interactions to be represented, for example, the interaction between AID targeting and error-prone mismatch repair. Predicted mutation frequencies from the model will be used by SHMTool to provide a comparative benchmark in situations where no control dataset is available. The model will be used to characterize each IGHV gene at a deeper level than was previously possible, allowing cross-species comparisons. In the longer term such a model will facilitate a better understanding of evolutionary changes in the IGHV genes and repertoire.

描述（由申请方提供）：免疫球蛋白（IG）基因座的体细胞超突变（SHM）是产生抗体多样性的基本过程。用于分析IG基因的突变谱的高通量方法（例如Roche 454深度测序）仍然不准确，并且部分由于后处理所需的专门生物信息学而昂贵。最近对Illumina MiSeq平台的改进允许配对末端2x 300 nt读取，这将使IgV测序更准确，成本比454平台低60倍。随着成本进一步下降，MiSeq等平台将成为常见的实验室设备，但只有在适当的生物信息学工具可用的情况下才有用。IG基因座的精确深度测序正迅速成为广泛临床应用的标准，包括确定预后和检测B细胞恶性肿瘤中的微小残留病、表征自身免疫性疾病和评估疫苗应答。 在目标1中，我们将开发一个用户友好的生物信息学管道（SHMPrep），以改善Illumina MiSeq平台中IgV序列的突变调用。我们假设独立PCR与测序误差效应的统计建模可以将MiSeq IgV序列的质量提高到与桑格测序相当的水平。该管道将与先前开发的分析工具（SHMTool）集成，以允许非计算机专家（如大多数临床医生）在普通台式计算机上处理MiSeq IgV数据集。 随着高通量数据的积累，为我们已经拥有的数据提供分析方法变得更加重要，而不是产生更多的数据。IgV突变谱取决于许多因素，包括碱基组成、活化诱导的脱氨酶（AID）热点和冷点的丰度和位置、Pol-η热点组成和总体突变频率。这种复杂性使得难以比较来自不同IgV区域的突变谱。在目标2中，我们将开发统计方法，用于比较不同的IgV区域，同时考虑序列组成以及突变饱和度和链偏差，这在识别免疫缺陷如AIDS和B细胞恶性肿瘤和其他癌症中的修复缺陷方面很重要。 我们仍然对IGHV基因之间的差异知之甚少。为什么有这么多的V区，为什么特定的IG基因和免疫反应之间有如此强的联系？在目标3中，我们将开发一种用于预测突变频率的统计模型，该模型将允许表示已知的分子相互作用，例如，AID靶向和易错配修复之间的相互作用。SHMTool将使用模型预测的突变频率，在没有对照数据集的情况下提供比较基准。该模型将用于在比以前可能的更深层次上表征每个IGHV基因，从而允许跨物种比较。从长远来看，这样的模型将有助于更好地了解IGHV基因和库的进化变化。