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- Title
Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis ‐regulatory elements is essential, and recent research utilizing MPRAs, STARR‐seq, CRISPR‐Cas9, and machine learning has yielded valuable insights
- Authors
Moeckel, Camille; Mouratidis, Ioannis; Chantzi, Nikol; Uzun, Yasin; Georgakopoulos‐Soares, Ilias
- Abstract
Understanding the influence of cis‐regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell‐type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression‐based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR‐Cas9‐based screening, which have significantly contributed to understanding TF binding preferences and cis‐regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis‐regulatory logic is analyzed. These computational advances have far‐reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.
- Subjects
GENE regulatory networks; SUPERVISED learning; CRISPRS; TRANSCRIPTION factors; GENETIC variation; MACHINE learning; NANOMEDICINE
- Publication
BioEssays, 2024, Vol 46, Issue 7, p1
- ISSN
0265-9247
- Publication type
Article
- DOI
10.1002/bies.202300210