酵母核糖体蛋白基因组合转录调控位点统计分析

真核基因的转录调控是后基因组时代研究的主要问题之一,其基础是认识DNA上转录因子结合位点(模体)及分布状况。基于马尔可夫链模型对酵母核糖体蛋白基因上游启动子序列中模体出现次数进行统计,利用Z-score统计量抽提出过表达和低表达的模体,其中95%的模体与实验得到的转录因子结合位点相符合。然后将抽提出的模体两两配对,通过与背景序列比较,找出酵母核糖体蛋白基因中出现概率及距离分布均具有统计显著性的模体对,这些非随机出现的模体对具有潜在的组合转录调控功能,其中一些模体对的组合调控作用已有实验支持。对提取出的模体对在序列中的位置分布进行分析,发现近94%的模体对位于转录起始位点上游,超过半数的模体对两模体之间的最短距离在0～100bp之间,距离小于30bp的模体对接近30%,这样的短距离间隔有利于两模体的相同作用。这些结果将有助于对酵母核糖体蛋白基因转录调控机制的深入认识。     

酵母基因上游与内含子可能存在的转录协同作用

酵母基因上游与内含子可能存在的转录协同作用     

基于对数线性模型的酵母基因转录调控模体分析

识别真核基因的转录因子结合位点(或称模体)是后基因组时代的一项主要工作,对共表达或共调控的基因同时进行分析可以提高模体识别的准确性.本文基于2×2列联表的对数线性模型,以模体出现的基因条数计数,对酵母核糖体蛋白(RP)基因普遍使用的转录调控模体进行分析,然后用U-检验进一步筛选出相对于背景序列来说过表达的模体.这些模体为酵母RP基因潜在的转录调控元件,与实验获得的转录因子结合位点的符合率达90%.本方法的优点在于用严格的统计标准在一组基因启动子中搜索普遍使用的模体,克服了以往分析中对模体使用普遍性的模糊判断.本文的方法也可以有效地搜索共表达基因族的组合调控模体对.研究中还发现一个现象:2×2列联表中反映属性相关程度的Pearson相关系数与对数线性模型的交互效应之间存在着明显的相关性.这一结果提示,可以用对数线性模型的交互效应来评价两属性的关联情况.     

Bayesian models and Markov chain Monte Carlo methods for protein motifs with the secondary characteristics.

Statistical methods have been developed for finding local patterns, also called motifs, in multiple protein sequences. The aligned segments may imply functional or structural core regions. However, the existing methods often have difficulties in aligning multiple proteins when sequence residue identities are low (e.g., less than 25%). In this article, we develop a Bayesian model and Markov chain Monte Carlo (MCMC) methods for identifying subtle motifs in protein sequences. Specifically, a motif is defined not only in terms of specific sites characterized by amino acid frequency vectors, but also as a combination of secondary characteristics such as hydrophobicity, polarity, etc. Markov chain Monte Carlo methods are proposed to search for a motif pattern with high posterior probability under the new model. A special MCMC algorithm is developed, involving transitions between state spaces of different dimensions. The proposed methods were supported by a simulated study. It was then tested by two real datasets, including a group of helix-turn-helix proteins, and one set from the CATH Protein Structure Classification Database. Statistical comparisons showed that the new approach worked better than a typical Gibbs sampling approach which is based only on an amino acid model.