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Based on the Information of Sequences to Predict the Transcription Factor Binding Sites and Promoter

Author: YangKeLi
Tutor: LiQianZhong
School: Inner Mongolia University
Course: Biophysics
Keywords: Transcription factor binding sites Promoter Position weight matrix Discrete increments Support Vector Machine
CLC: Q75
Type: Master's thesis
Year: 2007
Downloads: 352
Quote: 1
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Abstract


Gene transcriptional regulation has been an important element of bioinformatics research, transcription factor binding sites and promoter recognition is an important part of the study the regulation of gene transcription is a core issue of gene regulatory networks. This article based on the known transcription factor combined sites and promoter sequence data, starting from the sequence information and proposed the integration sites conservative parameters and the position of the right weight matrix predicted transcription factor combined sites of the position of the right weight matrix scoring function method as well as the joint position weight matrix and discrete incremental support vector machine predicted promoter. Based on the transcription factor binding site sequences are usually relatively short and conservative, the introduction of the characteristics of the matrix model describes the transcription factor binding sites, both based on the transcription factor binding sites vary loci conservative nucleotide conservative parameters into the matrix model The prediction of transcription factor binding sites, which raised position weight scoring function algorithm (PWMSA). First random frequency of occurrence of the four bases as background frequency binding the Single nucleotide position weight matrix PWMSA algorithm to predict the 22 kinds of transcription factor binding sites, single base site conservative parameters, overall Self-consistency test for the 85.48% 87.59% ,10-fold cross-validation test. Then taking into account the four bases in the gene sequence is not random, as the background frequency to the actual frequency of occurrence of the four bases, yeast nine transcription factor binding site prediction PWMSA algorithm, Self-consistency test predicted success rate up to 83.14% ,10-fold cross-validation test predicted success rate of 77.51%. Meanwhile, the introduction of the two latest evaluation index, the PWMSA algorithm with the existing 10 types of prediction of transcription factor binding sites software compares results showed PWMSA algorithm evaluation indicators are higher than the existing algorithms, at the nucleotide and fragment two evaluation levels of binding sites, predicted success rate higher than the other algorithms 4%, 7% percent. Taking into account the common contribution base interactions with protein affinity between transcription factor binding sites, the nine kinds yeast using known transcription factor binding sites in sequence to build a close neighbor nucleotide diad position right weight matrix, calculated sites close neighbors The diad nucleotide conservative parameters use PWMSA algorithms to 9 kinds of yeast transcription factor binding site prediction, the Self-Consistency examination, and 10-fold cross-validation test predictive success rates were 88.04%, 81.10%, a significantly higher in single-base position weight matrices results. Based on the difference between the promoter sequence of content characteristics and signal characteristics with non-start sub-sequence of discrete methods to extract the promoter sequence of content characteristics; build core start the sub-components of the position of the right weight matrix, use the position of the right weight matrix extract promoter sequences of signal characteristics, and finally extract the promoter and non-promoter sequence is nucleotide component characteristics. Build support vector machine classifier based on the sequence of the the integrated promoter sequence characteristics and signal characteristics predicted promoter and human Pol II promoter prediction ,10-fold cross-validation test was 95.70%, on the other selected independent test set prediction success rate of 98.30%, compared with 7 software and algorithms predict promoter, the sensitivity of our algorithms to predict the success rate of 97.00%, a specificity of 97.98%, significantly better than the existing prediction algorithms and software.

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