CAREER: Improving the Performance of Motif Finding Tools through Novel Reliable Significance Estimation and a Study of DNA Replication Origins

康奈尔大学的一名研究人员获得了美国国家科学基金会的职业补助金，以发现DNA序列中的调控基序。这仍然是计算生物学中的一个基本问题，因为识别这样的调控元件是理解基因表达调控的核心。最近的一项广泛的比较研究表明，在检测真实结合位点方面，现有工具有很大改进的潜力。这个项目的第一个目标是开发一个可靠的意义分析，以档案为基础的从头开始的模体搜索者。然后，这种分析可以进一步帮助描绘这些发现者的理论极限，并将他们的性能极限推向该极限。在过去的几年里，出现了几种新类型的数据，这些数据成功地整合到了主题搜索器中。特别是，随着亲缘关系密切的物种的增加，一类具有系统发育意识的基序发现者已经被开发出来。第二个主要目标是开发一种新的有效的重要性分析，可用于分析整合系统发育或定位数据的基序搜索者的结果，从而改善其性能。第三个目标是通过与分子生物学家的合作，更好地表征酵母物种中的复制起点，特别是表征解释酵母中复制起点之间的差异性的序列元件，以及在检测和分析相关物种中的新的复制起点时。这项提议的一个教育部分将培训和丰富来自计算机科学、生物学和统计学的所有学术水平的学生，使每个学生都接触到所有三个学科，这三个学科基本上结合在一起定义了计算生物学。

A Cornell University researcher is awarded an NSF CAREER grant to uncover regulatory motifs in DNA sequences. It remains a fundamental problem in computational biology as identification of such regulatory elements is central to understanding regulation of gene expression. A recent extensive comparative study showed there is potential for great improvement in existing tools when it comes to detection of real binding sites. The first goal of this project is to develop a reliable significance analysis for profile based de novo motif finders. This analysis can then further assist in delineating the theoretical limit of these finders and in pushing their performance envelope toward that limit. Over the last few years several new types of data emerged that were successfully integrated into motif finders. In particular, with the increased availability of closely related species, a class of phylogeny-aware motif finders has been developed. The second major goal is to develop a new efficient significance analysis that can be used to analyze the results of motif finders that integrate phylogeny or localization data and thereby improve their performance. The third goal is to acquire in a collaborative work with a molecular biologist a better characterization of replication origins in yeast species, in particular, characterizing the sequence elements that account for the variability among replication origins in yeast as well as in detecting and analyzing new replication origins in related species. An education component of this proposal will train and enrich students of all academic levels from Computer Science, Biology and Statistics exposing each one to all three disciplines that essentially combine to define computational biology.