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An Improved Graph Algorithm in Predicting Regulatory Elements

Author: ShaoZhen
Tutor: MaZhiQiang
School: Northeast Normal University
Course: Applied Computer Technology
Keywords: Regulatory elements Nematodes Gibbs sampler Graph algorithms Bioinformatics
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 11
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Abstract


With the rapid development of the life sciences , molecular biology in A major challenge is to reveal the mechanism of regulation of gene expression , gene expression that you want to know how to achieve activation and inhibition . Faced with this challenge is to identify a major step in regulatory elements , in particular, is to identify the DNA transcription factor binding sites. Transcription factors generally associated with DNA sequences upstream especially near the start site of transcription of the protein binding , or repress transcription by regulating mechanism to regulate gene expression . Such regulatory elements is still a problem. Even the most thorough study of the biological , regulatory elements about it we are still poorly understood. Want accurate identification of these loci is very difficult, because they are generally length is very short, only about 10 bases in length . The general input sequence length is 1000bp. Compounding the problem is that each has a variety of transcription factor binding sites , and sites of sequence variations exist . This means of weighted graph (weighted graph) theory, the use of improved scoring function to create a new algorithm, the input sequence every two fragments were scoring, so between the different nodes can use different weights and then measure the matching degree . While the introduction of biological a priori information , and with the Gibbs sampling algorithm combined. To test the performance of the algorithm , we use nematodes five transcription factors tested. And select the other three algorithms compare performance , namely AlignACE, Consensus, Gibbs. The algorithm in comparison with other classical algorithm has shown a higher degree of accuracy and sensitivity , which produces results more fully biological basis . As for the different species present method used to adjust the data to make it applicable to other species , the broader scope of the algorithm .

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