Suppr超能文献

识别协同结合因子的靶位点。

Identifying target sites for cooperatively binding factors.

作者信息

GuhaThakurta D, Stormo G D

机构信息

Department of Genetics, Washington University School of Medicine, 4566 Scott Avenue, Campus Box: 8232, St Louis, MO 63110, USA.

出版信息

Bioinformatics. 2001 Jul;17(7):608-21. doi: 10.1093/bioinformatics/17.7.608.

Abstract

MOTIVATION

Transcriptional activation in eukaryotic organisms normally requires combinatorial interactions of multiple transcription factors. Though several methods exist for identification of individual protein binding site patterns in DNA sequences, there are few methods for discovery of binding site patterns for cooperatively acting factors. Here we present an algorithm, Co-Bind (for COperative BINDing), for discovering DNA target sites for cooperatively acting transcription factors. The method utilizes a Gibbs sampling strategy to model the cooperativity between two transcription factors and defines position weight matrices for the binding sites. Sequences from both the training set and the entire genome are taken into account, in order to discriminate against commonly occurring patterns in the genome, and produce patterns which are significant only in the training set.

RESULTS

We have tested Co-Bind on semi-synthetic and real data sets to show it can efficiently identify DNA target site patterns for cooperatively binding transcription factors. In cases where binding site patterns are weak and cannot be identified by other available methods, Co-Bind, by virtue of modeling the cooperativity between factors, can identify those sites efficiently. Though developed to model protein-DNA interactions, the scope of Co-Bind may be extended to combinatorial, sequence specific, interactions in other macromolecules.

AVAILABILITY

The program is available upon request from the authors or may be downloaded from http://ural.wustl.edu.

摘要

动机

真核生物中的转录激活通常需要多种转录因子的组合相互作用。虽然存在几种用于识别DNA序列中单个蛋白质结合位点模式的方法,但用于发现协同作用因子的结合位点模式的方法却很少。在此,我们提出一种算法Co-Bind(用于协同结合),用于发现协同作用转录因子的DNA靶位点。该方法利用吉布斯采样策略对两个转录因子之间的协同性进行建模,并为结合位点定义位置权重矩阵。同时考虑了训练集和整个基因组的序列,以区分基因组中常见的模式,并产生仅在训练集中有意义的模式。

结果

我们在半合成和真实数据集上测试了Co-Bind,以表明它可以有效地识别协同结合转录因子的DNA靶位点模式。在结合位点模式较弱且无法通过其他现有方法识别的情况下,Co-Bind凭借对因子之间协同性的建模,可以有效地识别这些位点。虽然Co-Bind是为模拟蛋白质-DNA相互作用而开发的,但其范围可能扩展到其他大分子中的组合、序列特异性相互作用。

可用性

该程序可应作者要求提供,也可从http://ural.wustl.edu下载。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验