A Predictive Model of Human Transcriptional Activators and Repressors
Abstract/Contents
- Abstract
- The ability to predict which protein sequences can act as transcriptional activators or repressors is important for understanding the function of human and viral transcription factors (TFs) inside human cells and for building synthetic biology tools for gene control. Here, I integrate multiple high-throughput data sets acquired using a recently developed method (HT-Recruit) that tests hundreds of thousands of protein sequences for their effect on reporter genes in live human cells. I first created a data processing pipeline using ground truth validations to regularize results from multiple HT-Recruit screens, allowing cross-screen comparisons as well as proper model training. After processing these datasets, I built and trained convolutional neural network machine learning models that predict both activation and repression for protein sequences across the human transcription factors. These are the first models to be trained on human TF data, as well as the first to predict repressors. Some protein sequences are bifunctional in that they both activate and repress, so it is important to be able to predict both.
Description
| Type of resource | text |
|---|---|
| Publication date | May 4, 2023 |
Creators/Contributors
| Author | Liongson, Ivan |
|---|---|
| Thesis advisor | Bintu, Lacramioara |
| Thesis advisor | Fraser, Hunter |
| Degree granting institution | Stanford University, Department of Biology |
Subjects
| Subject | Biology |
|---|---|
| Subject | Transcription factors |
| Subject | Genetic transcription |
| Genre | Text |
| Genre | Thesis |
Bibliographic information
Access conditions
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International license (CC BY-NC).
Preferred citation
- Preferred citation
- Liongson, I. and Bintu, L. (2024). A Predictive Model of Human Transcriptional Activators and Repressors. Stanford Digital Repository. Available at https://purl.stanford.edu/yp461md3013. https://doi.org/10.25740/yp461md3013.
Collection
Undergraduate Theses, Department of Biology, 2022-2023
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- Contact
- ivanlion@stanford.edu
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