Quantifying selection in high-throughput Immunoglobulin sequencing data sets.
Quantifying selection in high-throughput Immunoglobulin sequencing data sets.
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DOI：
10.1093/nar/gks457
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发表时间：
2012-09-01
影响因子：
14.9
通讯作者：
Kleinstein SH
中科院分区：
文献类型：
--
作者：
Yaari G;Uduman M;Kleinstein SH
高通量免疫球蛋白测序提供了对B细胞亲和力成熟和适应性免疫基础上的体细胞超突变和抗原驱动的选择过程的新见解。从这些序列数据中估计正选择和负选择的能力不仅在理解病原体的免疫反应方面有着广泛的应用，而且对于确定体细胞超突变在自身免疫和B细胞癌中的作用也是至关重要的。在这里，我们开发了一个基于体细胞突变模式分析的贝叶斯估计抗原驱动选择(基线)的统计框架。我们的方法比以前的方法有了根本的进步，将问题从简单的检测选择转变为量化选择。除了提供一种更直观的方法来评估和可视化选择，我们的方法还首次允许对来自不同生殖系V(D)J片段的一组序列进行比较分析。将该方法应用于下一代测序数据显示了不同同种类型的存储单元的不同选择压力。这一框架可以很容易地适用于分析由显示热点/冷点、替代偏好或其他内在偏见的突变子引起的其他类型的DNA突变模式。
High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data 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. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.
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影响因子：
14.9
作者：
Uduman, Mohamed;Yaari, Gur;Kleinstein, Steven H.
通讯作者：
Kleinstein, Steven H.
影响因子：
3.8
作者：
MacCarthy, Thomas;Roa, Sergio;Scharff, Matthew D.;Bergman, Aviu
通讯作者：
Bergman, Aviu
DOI：
10.1073/pnas.0607581103
发表时间：
2006-10-24
影响因子：
11.1
作者：
Hershberg, Uri;Shlomchik, Mark J.
通讯作者：
Shlomchik, Mark J.
影响因子：
5.8
作者：
Gaeta, Bruno A.;Malming, Harald R.;Collins, Andrew M.
通讯作者：
Collins, Andrew M.
影响因子：
1.9
作者：
Agresti, A;Min, YY
通讯作者：
Min, YY