Mathematical and Statistical Models for Nucleosome

描述（由申请人提供）：核小体是真核生物染色体中DNA和组蛋白八聚体结合形成的染色质的基本包装单元。核小体在基因组DNA上的详细位置对于适当的基因调控至关重要，因此对人类和所有其他真核生物的健康和发育至关重要。然而，目前，我们缺乏任何方法来“在计算机上”预测核小体在基因组中的定位序列元素。该项目的长期目标是发展预测给定DNA序列的核小体形成倾向的能力，并将核小体形成序列的全基因组分布与染色体功能联系起来。为此，我们建议在未来数年逐步达致以下五个目标：(1)实验收集1000个酵母核小体核心DNA序列和1000个酵母双核小体DNA序列（两个相邻的核小体通过连接子DNA连接）。这些酵母核小体DNA序列将形成一个新的训练集，以及一个验证集的核小体定位预测横跨酵母基因组。(2)发展和完善核小体DNA序列比对的统计方法。在初步研究中，提出了一种新的核sorne DNA序列比对统计模型。我们将通过引入基于自由能考虑的不同周期二核苷酸信号重要性的加权方案来改进该模型。(3)利用(2)中的比对来训练一个预测核小体定位的模型。考虑了非齐次马尔可夫链模型和“混合序列”模型来模拟核苷酸的顺序依赖结构。(4)通过纳入邻近核小体的间距特性知识来推广模型。由(1)生成的二核小体序列将为核小体之间的空间提供分布证据，dl1极大地促进了预测

DESCRIPTION (provided by applicant): The nucleosome is the fundamental packing unit of chromatin formed by association of DNA and histone octamer in eukaryotic chromosomes. The detailed locations of nucleosomes along genomic DNA are critical for proper gene regulation - and therefore for the health and development of humans and all other eukaryotes - yet, at present, we lack any methods for the "in-silico" prediction of nucleosome positioning sequence elements in genomes. The long-term goal of this project is to develop capability to predict nucleosome-forming propensity given DNA sequences and correlate the genome-wide distribution of nucleosome-forming sequences with chromosome function. Towards this goal, we propose to achieve the following five aims progressively in the subsequent years. (1) Experimentally collect 1000 yeast nucleosome core DNA sequences, and 1000 yeast di-nucleosome DNA sequences (two neighboring nucleosomes linked by linker DNA). These yeast nucleosome DNA sequences will form a new training set as well as a validating set for nucleosome positioning prediction across the yeast genome. (2) Develop and refine statistical methods to align nucleosome DNA sequences. A novel statistical model for nucleosorne DNA sequence alignment was proposed in the preliminary study. We will refine this model by introducing a weighting scheme based on the importance of different periodic di-nucleotide signals from free energy consideration. (3) Use the alignment in (2) to train a model to predict nucleosome positioning. An inhomogeneous Markov chain model and a "mixture train" model are under consideration to model the sequential dependent structure of nucleotides. (4) Generalize the model by incorporating knowledge in spacing properties of neighboring nucleosomes. The di-nucleosome sequences generated from (1) will provide distributional evidence for between-nucleosome space and dl1 greatly facilitate prediction of genome-wide nucleosome positioning. (5) Automate the developed tools and algorithms.