Dataset Information

Simultaneous prediction of transcription factor binding sites in a group of prokaryotic genomes.

ABSTRACT:

Background

Our current understanding of transcription factor binding sites (TFBSs) in sequenced prokaryotic genomes is very limited due to the lack of an accurate and efficient computational method for the prediction of TFBSs at a genome scale. In an attempt to change this situation, we have recently developed a comparative genomics based algorithm called GLECLUBS for de novo genome-wide prediction of TFBSs in a target genome. Although GLECLUBS has achieved rather high prediction accuracy of TFBSs in a target genome, it is still not efficient enough to be applied to all the sequenced prokaryotic genomes.

Results

Here, we designed a new algorithm based on GLECLUBS called extended GLECLUBS (eGLECLUBS) for simultaneous prediction of TFBSs in a group of related prokaryotic genomes. When tested on a group of gamma-proteobacterial genomes including E. coli K12, a group of firmicutes genomes including B. subtilis and a group of cyanobacterial genomes using the same parameter settings, eGLECLUBS predicts more than 82% of known TFBSs in extracted inter-operonic sequences in both E. coli K12 and B. subtilis. Because each genome in a group is equally treated, it is highly likely that similar prediction accuracy has been achieved for each genome in the group.

Conclusions

We have developed a new algorithm for genome-wide de novo prediction of TFBSs in a group of related prokaryotic genomes. The algorithm has achieved the same level of accuracy and robustness as its predecessor GLECLUBS, but can work on dozens of genomes at the same time.

SUBMITTER: Zhang S

PROVIDER: S-EPMC2920276 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Publications

Simultaneous prediction of transcription factor binding sites in a group of prokaryotic genomes.

Zhang Shaoqiang S Li Shan S Pham Phuc T PT Su Zhengchang Z

BMC bioinformatics 20100723

<h4>Background</h4>Our current understanding of transcription factor binding sites (TFBSs) in sequenced prokaryotic genomes is very limited due to the lack of an accurate and efficient computational method for the prediction of TFBSs at a genome scale. In an attempt to change this situation, we have recently developed a comparative genomics based algorithm called GLECLUBS for de novo genome-wide prediction of TFBSs in a target genome. Although GLECLUBS has achieved rather high prediction accurac ...[more]

PMID: 20653963

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Project description:In computational methods, position weight matrices (PWMs) are commonly applied for transcription factor binding site (TFBS) prediction. Although these matrices are more accurate than simple consensus sequences to predict actual binding sites, they usually produce a large number of false positive (FP) predictions and so are impoverished sources of information. Several studies have employed additional sources of information such as sequence conservation or the vicinity to transcription start sites to distinguish true binding regions from random ones. Recently, the spatial distribution of modified nucleosomes has been shown to be associated with different promoter architectures. These aligned patterns can facilitate DNA accessibility for transcription factors. We hypothesize that using data from these aligned and periodic patterns can improve the performance of binding region prediction. In this study, we propose two effective features, "modified nucleosomes neighboring" and "modified nucleosomes occupancy", to decrease FP in binding site discovery. Based on these features, we designed a logistic regression classifier which estimates the probability of a region as a TFBS. Our model learned each feature based on Sp1 binding sites on Chromosome 1 and was tested on the other chromosomes in human CD4+T cells. In this work, we investigated 21 histone modifications and found that only 8 out of 21 marks are strongly correlated with transcription factor binding regions. To prove that these features are not specific to Sp1, we combined the logistic regression classifier with the PWM, and created a new model to search TFBSs on the genome. We tested the model using transcription factors MAZ, PU.1 and ELF1 and compared the results to those using only the PWM. The results show that our model can predict Transcription factor binding regions more successfully. The relative simplicity of the model and capability of integrating other features make it a superior method for TFBS prediction.

Dataset Information

Simultaneous prediction of transcription factor binding sites in a group of prokaryotic genomes.

Background

Results

Conclusions

Publications

Simultaneous prediction of transcription factor binding sites in a group of prokaryotic genomes.

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