NLM IRP Seminar Schedule
UPCOMING SEMINARS
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April 30, 2024 Wenya Rowe
The conformal central charge of the spin-1/2 XX model derived from long-chain asymptotics -
May 2, 2024 OPEN
TBD -
May 7, 2024 OPEN
TBD -
May 9, 2024 Pascal Mutz
TBD -
May 14, 2024 Stanley Liang
TBD
RECENT SEMINARS
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April 25, 2024 Ermin Hodzic
Condition-Aware Cell Type Deconvolution of Bulk Tissues -
April 23, 2024 OPEN
TBD -
April 16, 2024 Jaya Srivastava
Regulatory plasticity of the human genome -
April 11, 2024 Sergey Shmakov
Comprehensive survey of the TnpB RNA-guided nucleases -
April 2, 2024 Yifan Yang
Fairness and Bias in Biomedical AI
Scheduled Seminars on March 14, 2024
Contact NLM_IRP_Seminar_Scheduling@mail.nih.gov with questions about this seminar.
Abstract:
Transcription factors (TFs) play pivotal roles in gene regulation by binding to regulatory DNA elements like enhancers, dictating the spatial and temporal expression of their target genes. Precise identification of transcription factor binding sites (TFBSs) is crucial for linking genetic variants to complex human traits or diseases. Traditional computational methods often over-annotate enhancers as binding sites, leading to a high rate of false positives. In this study, we apply a deep learning approach for accurately identifying TFBSs within liver enhancers, covering less than 10% of enhancer regions. Our model effectively captures TFBSs of key activator TFs specific to the liver, but ATAC-seq footprinting does not. Notably, we observe optimal clustering of TFBSs associated with activator TFs based on motif similarity. In contrast, TFBSs linked to repressor TFs do not exhibit such clustering, suggesting a diverse repertoire of TFs acting as enhancer repressors. These findings shed light on the nuanced regulatory landscape within enhancers and underscore the importance of advanced computational techniques in deciphering transcriptional regulatory networks.