Base-resolution methylation patterns accurately predict transcription factor bindings in vivo

Abstract

Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF–DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF–DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods.

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

“…The anticorrelation of mCG and TF binding is predictive in inferring TFBS (27) and enhancers (23,28). These observations led us to take advantage of mCG depletion as a high-resolution (∼1 bp depending on density of CG sites) enhancer signature that is complementary to the lower-resolution histone modification data derived from ChIPseq experiments (with fragment size ranging from 200 to 600 bp after sonication) (29).…”

Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

“…However, for sequencing based DNA methylation profiling approach (BS-seq), the basic model assumption is completely different. Beta binomial model is widely used in analyzing BS-seq data (Feng et al 2014; Xu et al 2015). This paper mainly discusses the improvement of HM in array data, thus we only show one state-of-the-art beta binomial Bayesian hierarchical model (DSS) for differential methylated locus (DML) calling of BS-seq data (Feng et al 2014) and our stratified strategy (stDSS) to explore the possibility to borrow information across platforms.…”

Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods, and Challenges

“…It was found that the first principal component was able to predict a TF’s differential binding at its motif sites. Besides DH and HMs, Xu et al found that DNA methylation can also be used to predict TFBSs [55]. They found that DNA methylation levels around TFBSs and non-TF binding sites show distinct patterns.…”

“…This is an example of a shape feature. Two other examples of shape features are a DNA methylation valley surrounding a TFBS [55] and a bimodal distribution of histone modifications due to nucleosome displacement for TFBS prediction [80]. …”