Identification of tissue-specific cis-regulatory modules based on interactions between transcription factors

Background  

Evolutionary conservation has been used successfully to help identify cis-acting DNA regions that are important in regulating tissue-specific gene expression. Motivated by increasing evidence that some DNA regulatory regions are not evolutionary conserved, we have developed an approach for cis-regulatory region identification that does not rely upon evolutionary sequence conservation.     

Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

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。