by Scientists from Northwestern Medicine have made a significant breakthrough by developing a deep learning algorithm that can identify the location of polyadenylation on the human genome. The findings, published in Nature Communications, have the potential to accelerate research into diseases and disorders that are caused by disruptions in DNA transcription.
Polyadenylation is a crucial step in gene expression, where nucleotides are added to RNA molecules to stabilize them and prepare them for translation into proteins. It also plays a role in controlling transcription termination, preventing incorrect gene expression. However, little is currently known about the specific location of polyadenylation sites on the genome and the factors that influence them.
The research team, led by Dr. Zhe Ji, focused on understanding how termination is controlled. They aimed to identify the localization of polyadenylation sites and understand the signals that trigger this process across the human genome. Emily Stroup, a Ph.D. candidate in the Driskill Graduate Program and the first author of the study, developed deep learning models to analyze and predict the occurrence of polyadenylation sites, the cleavage of DNA around those sites, and the strength of these sites compared to others in the same gene.
The advantage of having multiple models is that researchers can learn from the patterns identified during training and gain a better understanding of how polyadenylation site usage is regulated. The models also revealed that polyadenylation sites are influenced by various signals in their proximity. Furthermore, the team was able to identify human genome sequences where polyadenylation occurs efficiently, providing a roadmap for future research.
By understanding the mechanisms behind polyadenylation and its localization on the human genome, the researchers hope to develop therapeutic approaches for correcting the process in disease contexts. This breakthrough allows them to predict polyadenylation sites across the human genome with single-nucleotide resolution, as the sites are determined by multiple signals.
Moving forward, the team plans to develop similar models for other species, including zebrafish, fruit flies, and yeast, to compare the localization of polyadenylation sites in different genomes. Preliminary results have already shown differences in signals across species, which will help researchers understand the evolution of these signals and their implications in diseases such as muscular dystrophy, neuronal disorders, and cancers.
This research marks a crucial step forward in our understanding of the human genome and opens up opportunities for targeted therapies and precision medicine. By deciphering the complex processes involved in gene expression, researchers can shed light on the development and progression of various diseases, ultimately leading to improved diagnostics and treatments.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
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