Identifying regulatory signals in DNA-sequences with a nonstatistical approximation approach

Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 Pub Date : 2003-08-11 DOI:10.1109/CSB.2003.1227417

Cun-Quan Zhang, Yunkai Liu, Elaine M. Eschen, Keqiang Wu

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Abstract

The identification of regulatory signals is one of the most challenging tasks in bioinformatics. The development of gene-profiling technologies now makes it possible to obtain vast data on gene expression in a particular organism under various conditions. This has created the opportunity to identify and analyze the parts of the genome believed to be responsible for transcription control-the transcription factor DNA-binding motifs (TFBMs). Developing a practical and efficient computational tool to identify TFBMs will enable us to better understand the interplay among thousands of genes in a complex eukaryotic organism. This problem, which is mathematically formulated as the motif finding problem in computer science, has been studied extensively in recent years. We develop a new mathematical model and approximation technique for motif searching. Based on the graph theoretic and geometric properties of this approach, we propose a nonstatistical approximation algorithm to find motifs in a set of genome sequences.

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用非统计近似方法识别dna序列中的调控信号

调控信号的识别是生物信息学中最具挑战性的任务之一。基因图谱技术的发展使得获取特定生物体在不同条件下基因表达的大量数据成为可能。这为鉴定和分析基因组中被认为负责转录控制的部分——转录因子dna结合基序(TFBMs)创造了机会。开发一种实用高效的计算工具来识别tfbm将使我们能够更好地理解复杂真核生物中数千个基因之间的相互作用。该问题在数学上被表述为计算机科学中的基序寻找问题，近年来得到了广泛的研究。我们提出了一种新的基序搜索数学模型和近似技术。基于这种方法的图论和几何性质，我们提出了一种非统计近似算法来寻找一组基因组序列中的基序。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003

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