Publications by authors named "Eren Can Eksi"
Article Synopsis
- Identifying cell type-specific enhancers in the brain is crucial for developing genetic tools to study mammalian brains, particularly in the context of mouse models.
- The 'Brain Initiative Cell Census Network (BICCN) Challenge' aimed to evaluate machine learning methods for predicting these enhancers based on data from multi-omics studies.
- Key findings included the importance of open chromatin as a predictor of functional enhancers, the role of sequence models in distinguishing non-functional enhancers, and the recognition of specific transcription factor codes to aid in the design of enhancers, ultimately advancing our understanding of gene regulation in the mammalian brain.
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution.
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