Molecular Sequence Analysis Using Word Counts: Statistics Power and Applications

描述（由申请人提供）：模式计数统计方法已用于许多计算生物学问题，包括：a）转录因子结合位点（TFBS）或顺式调控模块的鉴定，B）基因组序列和进化研究的比较，以及3）宏基因组学群落的比较。为实现这些目标，编制了许多统计数据。然而，这些统计量的性质，如权力，一直滞后的研究。此外，基于模式计数的方法对于来自下一代测序技术（NGS）（例如ABI/SOLiD和Roche 454焦磷酸测序）的序列数据的分析应该是非常有用的，因为这些统计不需要序列组装，而序列组装是NGS中的挑战性问题。然而，由于NGS期间引入的额外随机性，现有的模式计数统计不能容易地应用于序列片段数据，并且必须开发和研究新的统计。我们最近研究了在一个分子序列中检测富集模式和使用模式计数检测两个序列之间的关系的能力。根据这些研究的结果，我们将实现以下目标。在目标1中，我们研究了用于检测丰富模式的统计。1a）。当存在顺式调控模块时，将检测富集模式的功效研究扩展到更真实的背景序列和来自多种生物体的调控序列。1b）设计和研究新的统计数据，用于基于来自多种生物体的Chip-Seq数据检测富集模式。在目标2中，我们将开发无对齐统计来研究生物体之间的关系。2a）。扩展我们最近的工作比对自由序列比较统计更一般的进化模型，并设计新的统计水平基因转移。2b）。基于NGS数据的短序列读数，设计和研究新的用于基因组比较的无比对统计。拟议的项目将生成一套与功效分析相关的计算机算法，用于基于NGS的全基因组数据或序列片段数据检测富集对和无比对基因组比较。算法将通过网络传播，R代码将存放在R图书馆。本研究的结果对于检测基因组序列中的基序和顺式调节模块的研究以及进化研究具有重要意义。 公共卫生相关性：用于检测一个序列中的富集模式和用于无干扰序列比较的模式计数方法的统计功效还没有得到很好的理解。将开发新的统计数据、有效的算法和用户友好的软件，用于基于下一代测序（NGS）数据检测富集模式和基因组比较。这些工具将用于分析几个NGS数据集。

DESCRIPTION (provided by applicant): Pattern counting statistical methods have been used in many computational biology problems including: a) identification of transcription factor binding sites (TFBS) or cis-regulatory modules, b) comparison of genomic sequences and evolutionary studies, and 3) comparison of metagenomics communities. Many statistics have been developed to achieve these objectives. However, studies of properties of these statistics, e.g. power, have been lagging behind. In addition, pattern counting based methods should be very useful for the analysis of sequence data from the next generation sequencing technologies (NGS), e.g. ABI/SOLiD, and Roche 454 pyrosequencing, since these statistics do not need sequence assembly, a challenging problem in NGS. However, the available pattern counting statistics cannot be readily applied to the sequence fragment data due to the additional randomness introduced during NGS and new statistics have to be developed and studied. We recently studied the power of detecting enriched patterns in one molecular sequence and of detecting relationships between two sequences using pattern counting. Based on the results from these studies, we will achieve the following aims. In Aim 1, we study statistics for detecting enriched patterns. 1a). Extend the power study of detecting enriched patterns to more realistic background sequences when cis- regulatory modules are present and to regulatory sequences from multiple organisms. 1b) Design and study new statistics for detecting enriched patterns based on Chip-Seq data from multiple organisms. In Aim 2, we will develop alignment free statistics to study the relationships between organisms. 2a). Extend our recent work on alignment free sequence comparison statistics to more general evolutionary models and to design new statistics for horizontal gene transfers. 2b). Design and study new alignment free statistics for genome comparison based on short sequence reads from NGS data. The proposed projects will generate a suite of computer algorithms related to power analysis for detecting enriched pairs and alignment free genome comparison based on whole genome data or sequence fragment data from NGS. The algorithms will be disseminated through the web and R-code will be deposited in the R-library. The results from this study will be important for the study of detecting motifs and cisregulatory modules in genomic sequences and for evolutionary studies. PUBLIC HEALTH RELEVANCE: The statistical power of pattern counting methods for detecting enriched patterns in one sequence and for alignment-free sequence comparison is not well understood. New statistics, efficient algorithms and user-friendly software will be developed for detecting enriched patterns and genome comparison based on next generation sequencing (NGS) data. These tools will be used to analyze several NGS data sets.