Abstract
BackgroundThe binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq). The resulting chromatin feature data has been successfully adopted for genome-wide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type and do not utilise the pattern of the ChIP-seq coverage profiles efficiently.ResultsIn this work, we propose a PRobabilistic Enhancer PRedictIoN Tool (PREPRINT) that assumes characteristic coverage patterns of chromatin features at enhancers and employs a statistical model to account for their variability. PREPRINT defines probabilistic distance measures to quantify the similarity of the genomic query regions and the characteristic coverage patterns. The probabilistic scores of the enhancer and non-enhancer samples are utilised to train a kernel-based classifier. The performance of the method is demonstrated on ENCODE data for two cell lines. The predicted enhancers are computationally validated based on the transcriptional regulatory protein binding sites and compared to the predictions obtained by state-of-the-art methods.ConclusionPREPRINT performs favorably to the state-of-the-art methods, especially when requiring the methods to predict a larger set of enhancers. PREPRINT generalises successfully to data from cell type not utilised for training, and often the PREPRINT performs better than the previous methods. The PREPRINT enhancers are less sensitive to the choice of prediction threshold. PREPRINT identifies biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.
Highlights
The binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq)
The performances of PREPRINT and the state-of-the-art methods Random Forest-based Enhancer identification from Chromatin States (RFECS) and ChromHMM were compared on chromatin feature data from the ENCODE first data production phase Tier 1 cell lines K562 and GM12878
The lengths of the PREPRINT enhancer predictions were less sensitive to changes in the prediction threshold than the lengths of the RFECS predictions, the prediction threshold was demonstrated to influence the final number of predicted enhancers and their validation rates
Summary
The binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq). The resulting chromatin feature data has been successfully adopted for genome-wide enhancer identification by several unsupervised and supervised machine learning methods. The current methods predict different numbers and different sets of enhancers for the same cell type and do not utilise the pattern of the ChIP-seq coverage profiles efficiently. The methods have adopted the chromatin feature data produced by the next-generation sequencing technologies. Enhancers have been shown to possess certain molecular and structural chromatin features, which can be utilised to locate them genome-wide. The Chromatin Immunoprecipitation coupled with sequencing (ChIP-seq) can quantify the chromosomal locations for tens to hundreds of individual TRFs and histone modifications [9, 10]. Various combinations of the chromatin features have been adopted in several studies to locate enhancers [5, 11,12,13,14,15,16,17,18]
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