Statistical Analysis Methods and Software for ChIP-seq Data

描述(由申请人提供)：高通量下一代测序(NGS)技术的出现使数十亿个碱基的快速和廉价测序成为遗传学和基因组学领域的革命性变革。在NGS的应用中，CHIP-SEQ(染色质免疫沉淀后NGS)可能是迄今为止最成功的。ChIP-SEQ技术使研究人员能够研究转录因子的全基因组结合和表观基因组标记的映射。这两者都在以细胞特有的方式对基因表达进行编程方面发挥着至关重要的作用；因此，它们的全基因组图谱可以显着提高我们理解和诊断人类疾病的能力。尽管用于芯片序列数据的基本分析工具正在迅速增加，但所有可用的方法都有以下一个或多个缺点。首先，他们专注于一次分析一个芯片序列样本。随着ChIP-SEQ在表观基因组图谱中被普遍用于理解表型变异，对能够有效处理多个样本的方法的需求正在迅速上升。其次，他们只利用与参考基因组上唯一位置对齐的序列读数。这阻碍了芯片序列对基因组高重复区的研究。第三，芯片序列实验的常用设计为每个芯片序列样本使用一个匹配的控制样本。这限制了对照实验的基因组覆盖范围，并影响了芯片样本中浓缩的检测。这也大大增加了大规模芯片序列研究的测序成本。该项目的目标是在三个具体目标上解决芯片序列分析的这些挑战：(1)从多个样本中推断的统计方法；(2)利用映射到基因组中多个位置(多个读取)的读数的概率模型；(3)开发和评估用于对照实验的电子汇集设计。这些项目将通过方法学开发、模拟、计算分析和实验验证相结合的方式完成。将使用ENCODE、modENCODE和路线图表观基因组学联盟的数据集以及来自合作者的新数据集来开发和评估方法。该项目产生的统计资源将通过公开可用的软件传播，将为有效设计和分析芯片序列实验提供必要的工具。

DESCRIPTION (provided by applicant): The advent of high throughput next generation sequencing (NGS) technologies have revolutionized the fields of genetics and genomics by allowing rapid and inexpensive sequencing of billions of bases. Among the NGS applications, ChIP-seq (chromatin immunoprecipitation followed by NGS) is perhaps the most successful to date. ChIP-seq technology enables investigators to study genome-wide binding of transcription factors and mapping of epigenomic marks. Both of these play crucial roles in programming of gene expression in a cell specific manner; therefore their genome-wide mapping can significantly advance our ability to understand and diagnose human diseases. Although basic analysis tools for ChIP-seq data are rapidly increasing, all of the available methods share one or more of the following shortcomings. First, they focus on analyzing one ChIP- seq sample at a time. As ChIP-seq is becoming commonly utilized in epigenome mapping to understand phenotypic variation, the demand for methods that can handle multiple samples efficiently is rapidly rising. Second, they only utilize sequence reads that align to unique locations on the reference genome. This hinders the study of highly repetitive regions of genomes by ChIP-seq. Third, commonly used designs for ChIP-seq experiments employ one matching control sample per each ChIP-seq sample. This limits the genome coverage of control experiments and impacts the detection of enrichment in ChIP samples. It also significantly contributes to increase in sequencing costs for large-scale ChIP-seq studies. The objective of this project is to address these challenges of ChIP-seq analysis in three specific aims: (1) Statistical methods for inference from multiple samples; (2) Probabilistic models for utilizing reads that map to multiple locations (multi-reads) in the genome; (3) Development and evaluation of in silico pooling designs for control experiments. The projects will be accomplished through a combination of methodological development, simulation, computational analysis, and experimental validation. Methods will be developed and evaluated using datasets from the ENCODE, modENCODE, and the RoadMap Epigenomics consortiums as well as novel datasets from collaborators. Statistical resources generated from the project, which will be disseminated in publicly available software, will provide essential tools for the efficient design and analysis of ChIP-seq experiments.