Machine learning tools for deciphering the regulatory logic of enhancers in health and disease
Transcriptional enhancers are DNA regulatory elements that control the levels and spatiotemporal patterns of gene expression during development, homeostasis, and pathophysiological processes. Enhancer identification and characterization at the genome-wide scale rely on their structural characteristi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1603687/full |
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| author | Spyros Foutadakis Vasiliki Bourika Ioanna Styliara Panagiotis Koufargyris Asimina Safarika Eleni Karakike |
| author_facet | Spyros Foutadakis Vasiliki Bourika Ioanna Styliara Panagiotis Koufargyris Asimina Safarika Eleni Karakike |
| author_sort | Spyros Foutadakis |
| collection | DOAJ |
| description | Transcriptional enhancers are DNA regulatory elements that control the levels and spatiotemporal patterns of gene expression during development, homeostasis, and pathophysiological processes. Enhancer identification and characterization at the genome-wide scale rely on their structural characteristics, such as chromatin accessibility, binding of transcription factors and cofactors, activating histone modifications, 3D interactions with other regulatory elements, as well as functional characteristics measured by massively parallel reporter assays and sequence conservation approaches. Recently, machine learning approaches and particularly deep learning models (Enformer, BPNet, DeepSTARR, etc.) allow the prediction of enhancers, the impact of variants on their activity and the inference of transcription factor binding sites, leading, among others, to the construction of the first completely synthetic enhancers. We present the above computational tools and discuss their diverse applications towards cracking the enhancer regulatory code, which could have far-reaching ramifications for uncovering essential regulatory mechanisms and diagnosing and treating diseases. With an emphasis on sepsis, a leading cause of morbidity and mortality in hospitalized patients, we discuss computational approaches to identify sepsis-associated endotypes, circuits, and immune cell states and signatures characteristic of this condition, which could aid in developing novel therapies. |
| format | Article |
| id | doaj-art-da96f8805f4a4e6f880f3c68be3da5d1 |
| institution | DOAJ |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-da96f8805f4a4e6f880f3c68be3da5d12025-08-20T03:05:41ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-08-011610.3389/fgene.2025.16036871603687Machine learning tools for deciphering the regulatory logic of enhancers in health and diseaseSpyros Foutadakis0Vasiliki Bourika1Ioanna Styliara2Panagiotis Koufargyris3Asimina Safarika4Eleni Karakike54th Department of Internal Medicine, Medical School, National and Kapodistrian University of Athens, Athens, GreeceNeonatal Unit, First Department of Pediatrics, National and Kapodistrian University of Athens, Athens, GreeceDepartment of Obstetrics and Gynaecology, School of Medicine, University of Patras, Patras, Greece4th Department of Internal Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece4th Department of Internal Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece4th Department of Internal Medicine, Medical School, National and Kapodistrian University of Athens, Athens, GreeceTranscriptional enhancers are DNA regulatory elements that control the levels and spatiotemporal patterns of gene expression during development, homeostasis, and pathophysiological processes. Enhancer identification and characterization at the genome-wide scale rely on their structural characteristics, such as chromatin accessibility, binding of transcription factors and cofactors, activating histone modifications, 3D interactions with other regulatory elements, as well as functional characteristics measured by massively parallel reporter assays and sequence conservation approaches. Recently, machine learning approaches and particularly deep learning models (Enformer, BPNet, DeepSTARR, etc.) allow the prediction of enhancers, the impact of variants on their activity and the inference of transcription factor binding sites, leading, among others, to the construction of the first completely synthetic enhancers. We present the above computational tools and discuss their diverse applications towards cracking the enhancer regulatory code, which could have far-reaching ramifications for uncovering essential regulatory mechanisms and diagnosing and treating diseases. With an emphasis on sepsis, a leading cause of morbidity and mortality in hospitalized patients, we discuss computational approaches to identify sepsis-associated endotypes, circuits, and immune cell states and signatures characteristic of this condition, which could aid in developing novel therapies.https://www.frontiersin.org/articles/10.3389/fgene.2025.1603687/fulldeep learningenhancersgenomicsmachine learningsepsistranscriptional regulation |
| spellingShingle | Spyros Foutadakis Vasiliki Bourika Ioanna Styliara Panagiotis Koufargyris Asimina Safarika Eleni Karakike Machine learning tools for deciphering the regulatory logic of enhancers in health and disease Frontiers in Genetics deep learning enhancers genomics machine learning sepsis transcriptional regulation |
| title | Machine learning tools for deciphering the regulatory logic of enhancers in health and disease |
| title_full | Machine learning tools for deciphering the regulatory logic of enhancers in health and disease |
| title_fullStr | Machine learning tools for deciphering the regulatory logic of enhancers in health and disease |
| title_full_unstemmed | Machine learning tools for deciphering the regulatory logic of enhancers in health and disease |
| title_short | Machine learning tools for deciphering the regulatory logic of enhancers in health and disease |
| title_sort | machine learning tools for deciphering the regulatory logic of enhancers in health and disease |
| topic | deep learning enhancers genomics machine learning sepsis transcriptional regulation |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1603687/full |
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