Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with deep learning

Zk, Atak; Taskiran, Ibrahim Ihsan; Flerin, Christopher; Mauduit, David; Minnoye, Liesbeth; Hulsemans, G; Christiaens,; Ghanem, G.; Wouters, Jasper; Aerts, Stein

doi:10.1101/2019.12.21.885806

Cited by 2 publications

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References 53 publications

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“…Understanding the enhancer logic and the mechanism by which TFs bind and direct active enhancers will become increasingly important, as it will be essential for the development of new therapies that influence cell state-specific enhancer functions in a targeted way (e.g. for enhancer therapy (Hamdan and Johnsen 2019;Johnson et al 2008)), or to prioritise non-coding variants in whole genome sequencing studies of personal or cancer genomes (Atak et al 2019). Predicting enhancers and determining their functional role within gene regulatory networks has been an active field for years.…”

Section: Discussionmentioning

confidence: 99%

Cross-species analysis of enhancer logic using deep learning

et al. 2020

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Deciphering the genomic regulatory code of enhancers is a key challenge in biology as this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of non-coding genome variation, and empower the generation of cell type specific drivers for gene therapy. Here we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study due to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species. We demonstrate the accuracy of DeepMEL predictions on the CAGI5 challenge, where it significantly outperforms existing models on the melanoma enhancer of IRF4. Next, we exploit DeepMEL to analyse enhancer architectures and identify accurate transcription factor binding sites for the core regulatory complexes in the two different melanoma states, with distinct roles for each transcription factor, in terms of nucleosome displacement

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Section: Discussionmentioning

confidence: 99%

Cross-species analysis of enhancer logic using deep learning

et al. 2020

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“…A critical subset of chromatin accessibility QTL could be explained by making or creating binding motifs for pioneer factors 328 . In an alternative approach, chromatin accessibility can also be compared between alleles, within the same individual, to identify allele-specific chromatin accessibility 329 .…”

Section: Variation Within Populationsmentioning

confidence: 99%

Chromatin accessibility profiling methods

Minnoye

Marinov

Krausgruber

et al. 2021

Nat Rev Methods Primers

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Chromatin accessibility refers to the level of physical compaction of chromatin, a complex formed by DNA and associated proteins consisting mainly of histones, transcription factors (TFs), chromatin-modifying enzymes and chromatin-remodelling complexes 1-3. Although eukaryotic genomes are generally packed into nucleosomes, which comprise ~147 bp of DNA wrapped around an octamer of histones 4,5 , nucleosome occupancy is not uniform in the genome, and varies across tissues and cell types. Nucleosomes are typically depleted at genomic locations that represent cis-regulatory elementsenhancers and promoters, among others-that interact with transcriptional regulators (for example, TFs), resulting in accessible chromatin 6-10. Profiling chromatin accessibility on a genome-wide scale is an excellent tool to map putative regulatory elements in a cell type or cell state. Post-translational chemical modifications of chromatin, including DNA methylation (in vertebrates) and histone methylation and acetylation, are dynamic and change between different cell states, similar to nucleosome positioning. These post-translational modifications are often correlated with chromatin accessibility and can reflect specific functionalities of genomic regions related to the regulation of gene expression 11,12. Changes in these post-translational modifications, such as increased or decreased histone methylation and acetylation, are affected by a large set of chromatin-modifying enzymes that can be recruited to chromatin regions by TFs. These modifications alter the physico-chemical properties of the chromatin, which in turn can influence the formation of transcriptional condensates 13,14. In addition, active chromatin remodelling impacts nucleosome occupancy; for example, the SWI/SNF complexes use ATP hydrolysis to alter histone-DNA contacts, thereby repositioning or removing nucleosomes 15. Dynamic changes in the chromatin structure, chemical modifications and nucleosome positioning form a crucial interplay with the TFs that drive differentiation of cells during development 16,17. Initial changes in chromatin accessibility are caused by the binding of TFs, which outcompete histones and recruit cofactors, including ATP-dependent chromatin remodellers 18,19 , or by TFs that preferentially bind to their recognition sequence in nucleosomal DNA 20,21. The binding of these initial TFs, known as pioneer factors, can recruit other TFs to co-bind and further stabilize the nucleosome-depleted

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Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with deep learning

Cited by 2 publications

References 53 publications

Cross-species analysis of enhancer logic using deep learning

Cross-species analysis of enhancer logic using deep learning

Chromatin accessibility profiling methods

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