Statistical Methods for Next-Generation Sequence Data

描述(由申请人提供)：该项目的广泛、长期目标涉及开发新的统计方法和计算工具，用于对由重要生物学问题和实验驱动的大规模下一代序列(NGS)数据进行统计和概率建模。当前项目的具体目标是开发新的统计模型和计算方法来分析NGS数据，重点是在生殖系DNA中发现拷贝数变体(CNV)的稳健方法，开发通用的对数线性模型来识别单样本和多样本RNA-SEQ数据上的替代外显子用法，考虑短读数测序速率的非一致性，开发基于染色质免疫沉淀和高通量测序(CHIP-SEQ)数据的新的非参数统计方法来确定组蛋白修饰位点，以及从人类微生物组研究中分析元基因组数据的新方法。这些问题都是由私家侦探与宾夕法尼亚大学调查人员的密切合作引发的。这些方法依赖于生物学洞察力和高维数据分析方法的新集成，包括稀疏结构信号的检测和识别、基于小波的非参数回归和树状结构协变量的非参数假设检验和惩罚回归分析。新方法可以应用于不同类型的NGS数据，理想地将有助于识别各种复杂的人类疾病和生物过程背后的基因和生物途径。该项目还将调查这些方法的稳健性、功率和效率，并将它们与现有方法进行比较。此外，该项目将开发实用和可行的计算机程序来实施所提出的方法，通过应用于与非洲人群中CNV和RNA-SEQ分析有关的NGS数据集、过氧物-某些增殖物激活受体(PPAR)3与脂肪分化和胰岛素抵抗的联系以及饮食对人类微生物群的影响来评估这些方法的性能。这里提出的工作将有助于建立超高维下一代序列数据的模型和研究复杂表型和生物系统的统计方法，并提供对各种数据集所代表的每个生物领域的洞察。根据这笔赠款开发的所有项目和详细的文件将免费提供给感兴趣的研究人员。

DESCRIPTION (provided by applicant): The broad, long-term objective of this project concerns the development of novel statistical methods and computational tools for statistical and probabilistic modeling of large-scale next-generation sequence (NGS) data motivated by important biological questions and experiments. The specific aim of the current project is to develop new statistical models and computational methods for analysis of NGS data, focusing on robust methods for discovering copy number variants (CNVs) in germline DNAs, development of a general log-linear model for identifying alternative exon usages on one- and multi-sample RNA-seq data allowing for non-uniformity on short- read sequencing rates, development of novel nonparametric statistical methods for identifying histone modification sites based on the chromatin immunoprecipitation and high-throughput sequencing (ChIP-seq) data, and novel methods for analysis of metagenomic data from human microbiome studies. These problems are all motivated by the PI's close collaborations with Penn investigators. The methods hinge on novel integration of biological insights and methods for high dimensional data analysis, including detection and identification of sparse structured-signals, wavelet-based nonparametric regression and nonparametric hypothesis testing and penalized regression analysis for tree-structured covariates. The new methods can be applied to different types of NGS data and will ideally facilitate the identifications of genes and biological pathways underlying various complex human diseases and biological processes. The project will also investigate the robustness, power and efficiencies of these methods and compare them with existing methods. In addition, this project will develop practical and feasible computer programs in order to implement the proposed methods, to evaluate the performance of these methods through applications to NGS data sets related to CNV and RNA-seq analysis in African populations, linkage of peroxi- some proliferator activator receptor (PPAR)3 and adipose differentiation and insulin resistance and effects of diets on human microbiome. The work proposed here will contribute statistical methodology to modeling ultra-high dimensional next-generation sequence data and to studying complex phenotypes and biological systems and offer insights into each of the biological areas represented by the various data sets. All programs developed under this grant and detailed documentation will be made available free-of-charge to interested researchers.