Modeling the specificity of protein-DNA nteractions  

作　　者：Gary D. Stormo 

机构地区：[1]Department of Genetics, Center for Genome Sciences and Systems Biology, School of Medicine, Washington University, St.Louis, MO 63108-8510, USA

出　　处：《Frontiers of Electrical and Electronic Engineering in China》2013年第2期115-130,共16页中国电气与电子工程前沿（英文版）

摘　　要：The specificity of protein-DNA interactions is most commonly modeled using position weight matrices （PWMs）. First introduced in 1982, they have been adapted to many new types of data and many different approaches have been developed to determine the parameters of the PWM. New high-throughput technologies provide a large amount of data rapidly and offer an unprecedented opportunity to determine accurately the specificities of many transcription factors （TFs）. But taking full advantage of the new data requires advanced algorithms that take into account the biophysical processes involved in generating the data. The new large datasets can also aid in determining when the PWM model is inadequate and must be extended to provide accurate predictions of binding sites. This article provides a general mathematical description of a PWM and how it is used to score potential binding sites, a brief history of the approaches that have been developed and the types of data that are used with an emphasis on algorithms that we have developed for analyzing high-throughput datasets from several new technologies. It also describes extensions that can be added when the simple PWM model is inadequate and further enhancements that may be necessary, it briefly describes some applications of PWMs in the discovery and modeling of in vivo regulatory networks.The specificity of protein-DNA interactions is most commonly modeled using position weight matrices （PWMs）. First introduced in 1982, they have been adapted to many new types of data and many different approaches have been developed to determine the parameters of the PWM. New high-throughput technologies provide a large amount of data rapidly and offer an unprecedented opportunity to determine accurately the specificities of many transcription factors （TFs）. But taking full advantage of the new data requires advanced algorithms that take into account the biophysical processes involved in generating the data. The new large datasets can also aid in determining when the PWM model is inadequate and must be extended to provide accurate predictions of binding sites. This article provides a general mathematical description of a PWM and how it is used to score potential binding sites, a brief history of the approaches that have been developed and the types of data that are used with an emphasis on algorithms that we have developed for analyzing high-throughput datasets from several new technologies. It also describes extensions that can be added when the simple PWM model is inadequate and further enhancements that may be necessary, it briefly describes some applications of PWMs in the discovery and modeling of in vivo regulatory networks.

分 类 号：TP312[自动化与计算机技术—计算机软件与理论] S511.01[自动化与计算机技术—计算机科学与技术]