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High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites.

ABSTRACT: In silico prediction of transcription factor binding sites (TFBSs) is central to the task of gene regulatory network elucidation. Genomic DNA sequence information provides a basis for these predictions, due to the sequence specificity of TF-binding events. However, DNA sequence alone is an impoverished source of information for the task of TFBS prediction in eukaryotes, as additional factors, such as chromatin structure regulate binding events. We show that incorporating high-throughput chromatin modification estimates can greatly improve the accuracy of in silico prediction of in vivo binding for a wide range of TFs in human and mouse. This improvement is superior to the improvement gained by equivalent use of either transcription start site proximity or phylogenetic conservation information. Importantly, predictions made with the use of chromatin structure information are tissue specific. This result supports the biological hypothesis that chromatin modulates TF binding to produce tissue-specific binding profiles in higher eukaryotes, and suggests that the use of chromatin modification information can lead to accurate tissue-specific transcriptional regulatory network elucidation.

SUBMITTER: Whitington T

PROVIDER: S-EPMC2662491 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites.

Whitington Tom T Perkins Andrew C AC Bailey Timothy L TL

Nucleic acids research 20081106 1

In silico prediction of transcription factor binding sites (TFBSs) is central to the task of gene regulatory network elucidation. Genomic DNA sequence information provides a basis for these predictions, due to the sequence specificity of TF-binding events. However, DNA sequence alone is an impoverished source of information for the task of TFBS prediction in eukaryotes, as additional factors, such as chromatin structure regulate binding events. We show that incorporating high-throughput chromati ...[more]

PMID: 18988630

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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

High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites.

Publications

High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites.

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