Molecular Sequence Analysis Using Word Counts: Statistics Power and Applications

描述(申请人提供)：模式计数统计方法已被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)元基因组群落的比较。为了实现这些目标，已经开发了许多统计数据。然而，对这些统计数据的性质，如电力，的研究一直落后。此外，基于模式计数的方法对于来自下一代测序技术(NGS)的序列数据的分析应该非常有用，例如ABI/Solid和Roche 454焦糖测序，因为这些统计数据不需要序列组装，这在NGS中是一个具有挑战性的问题。然而，由于在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。我们最近研究了利用模式计数来检测一个分子序列中的丰富模式以及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，将检测丰富模式的能力研究扩展到更真实的背景序列，并扩展到来自多个生物体的调控序列。1b)设计和研究用于基于来自多个生物的芯片序列数据检测丰富模式的新的统计方法。在目标2中，我们将开发无比对统计量来研究生物之间的关系。2a)。将我们最近在无比对序列比较统计学方面的工作扩展到更一般的进化模型，并为水平基因转移设计新的统计学。2B)。设计和研究基于来自NGS数据的短序列读取的基因组比对的新的无比对统计量。拟议的项目将产生一套与能量分析相关的计算机算法，用于检测丰富的配对和基于来自NGS的全基因组数据或序列片段数据的无比对基因组比较。这些算法将通过网络传播，R代码将存放在R库中。这项研究的结果将对基因组序列中检测基序和顺式调控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丰富模式和用于无比对序列比较的模式计数方法的统计能力还没有被很好地理解。将开发新的统计数据、高效的算法和用户友好的软件，以检测丰富的模式和基于下一代测序(NGS)数据的基因组比较。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.