Predictive modeling of promoter SNP effects using starr-seq and machine learning
A novel framework combining saturating mutagenesis and machine learning to predict how promoter SNPs affect gene expression, using STARR-seq to assay 150 Arabidopsis promoters. Validated with dual-luciferase assays and Arabidopsis HEM population.
Overview
This solution offers a cutting-edge approach to understanding the impact of promoter SNPs on gene expression by integrating saturating mutagenesis with advanced predictive modeling. Utilizing a modified Self Transcribed Active Regulatory Region-sequencing (STARR-seq) platform, this research aims to create high-resolution maps of SNP impacts, enhancing the predictive power of machine learning models in plant regulatory genomics. The approach is validated using dual-luciferase assays and qPCR, providing a scalable framework for predictive promoter engineering, particularly in agriculture.
Technical specifications
Key features:
- STARR-seq Platform: Modified for high-throughput analysis of SNP impacts on gene expression.
- Machine Learning Model: Trained on datasets from ~150 Arabidopsis promoter fragments, predicting expression changes linked to SNPs.
- Validation Techniques: Dual-luciferase assays and qPCR tests against native promoter variation in the Arabidopsis HEM population.
- Saturation Mutagenesis: High-resolution mapping of SNP effects across transcription factor binding sites.
Technology readiness level
Currently at TRL 4, this technology has been validated in a laboratory setting through functional genomics and computational predictions. Future validations are planned using diverse promoter variants in agricultural contexts, aiming to refine and generalize the predictive model.
About University of Minnesota
The University of Minnesota is a flagship, comprehensive public research university spanning multiple campuses, with a large research enterprise and clinical integration. Industry engages through co-located labs on the Twin Cities campuses, access to an academic health system for clinical translation, and pilot and field-testing facilities that speed scale-up. A statewide extension network and outreach centers provide real-world sites and data partnerships across Minnesota, while proximity to a dense medtech and Fortune 500 corridor enables frequent collaboration. Research is supported by competitive federal funding, including NIH, NSF, DOE, USDA, and DoD. A dedicated technology transfer office manages IP, licensing, sponsored research agreements, and startup incubation to speed commercialization.
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