scholarly journals Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

2020 ◽  
Author(s):  
Pengyu Ni ◽  
Zhengchang Su
Keyword(s):  
Transcription Factor ◽  
Human Genome ◽  
Binding Sites ◽  
State Of The Art ◽  
Accurate Prediction ◽  
Evolutionary Constraints ◽  
Phase 3 ◽  
Regulatory Modules ◽  
Regulatory Genome ◽  
Encode Phase

AbstractAnnotating all cis-regulatory modules (CRMs) and transcription factor (TF) binding sites(TFBSs) in genomes remains challenging. We tackled the task by integrating putative TFBSs motifs found in available 6,092 datasets covering 77.47% of the human genome. This approach enabled us to partition the covered genome regions into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, like known enhancers, the predicted 1,404,973 CRMCs are under strong evolutionary constraints, suggesting that they might be cis-regulator. In contrast, the non-CRMCs are largely selectively neutral, suggesting that they might not be cis-regulatory. Our method substantially outperforms three state-of-the-art methods (GeneHancers, EnhancerAtlas and ENCODE phase 3) for recalling VISTA enhancers and ClinVar variants, as well as by measurements of evolutionary constraints. We estimated that the human genome might encode about 1.46 million CRMs and 67 million TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought.

scholarly journals Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Pengyu Ni ◽  
Zhengchang Su
Keyword(s):  
Binding Sites ◽  
State Of The Art ◽  
Cell Types ◽  
Evolutionary Constraints ◽  
Specific Cell ◽  
Cell Type ◽  
Phase 3 ◽  
Regulatory Modules ◽  
Regulatory Genome ◽  
Encode Phase

Abstract cis-regulatory modules(CRMs) formed by clusters of transcription factor (TF) binding sites (TFBSs) are as important as coding sequences in specifying phenotypes of humans. It is essential to categorize all CRMs and constituent TFBSs in the genome. In contrast to most existing methods that predict CRMs in specific cell types using epigenetic marks, we predict a largely cell type agonistic but more comprehensive map of CRMs and constituent TFBSs in the gnome by integrating all available TF ChIP-seq datasets. Our method is able to partition 77.47% of genome regions covered by available 6092 datasets into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, the predicted CRMCs are under strong evolutionary constraints, while the non-CRMCs are largely selectively neutral, strongly suggesting that the CRMCs are likely cis-regulatory, while the non-CRMCs are not. Our predicted CRMs are under stronger evolutionary constraints than three state-of-the-art predictions (GeneHancer, EnhancerAtlas and ENCODE phase 3) and substantially outperform them for recalling VISTA enhancers and non-coding ClinVar variants. We estimated that the human genome might encode about 1.47M CRMs and 68M TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought.

scholarly journals Transcription Factor Binding Sites Are Genetic Determinants of Retroviral Integration in the Human Genome

PLoS ONE ◽  
2009 ◽  
Vol 4 (2) ◽  
pp. e4571 ◽  
Author(s):  
Barbara Felice ◽  
Claudia Cattoglio ◽  
Davide Cittaro ◽  
Anna Testa ◽  
Annarita Miccio ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Human Genome ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Genetic Determinants ◽  
Retroviral Integration ◽  
Factor Binding

scholarly journals Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

2019 ◽  
Author(s):  
Maria Osmala ◽  
Harri Lähdesmäki
Keyword(s):  
Human Genome ◽  
Binding Sites ◽  
State Of The Art ◽  
Regulatory Protein ◽  
Distance Measures ◽  
Supervised Machine Learning ◽  
Cell Type ◽  
Enhancer Prediction ◽  
Art Methods ◽  
Chromatin Feature

AbstractBackgroundThe 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.Availabilityhttps://github.com/MariaOsmala/[email protected]

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