Decoding biology with massively parallel reporter assays and machine learning.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Massively parallel reporter assays (MPRAs) are essential for understanding gene expression regulation.
• High-throughput sequencing allows for large-scale analysis of sequence variation impacts.
• Machine learning (ML) models can integrate MPRA data for predictive insights.

Purpose of the Study:

• To review the application of cis-regulatory MPRAs in dissecting gene expression.
• To highlight the role of ML in interpreting MPRA data and predicting regulatory sequences.
• To discuss the utility of MPRAs and ML in synthetic biology and therapeutic development.

Main Methods:

• MPRAs utilize sequencing to measure molecular phenotypes.
• ML models are trained on MPRA datasets to learn cis-regulatory codes.
• This review synthesizes findings from studies employing MPRAs for various gene regulation processes.

Main Results:

• MPRAs enable interrogation of sequence variation effects on a genome-wide scale.
• ML models generalize MPRA findings to predict the function of novel sequences.
• Quantitative understanding of cis-regulatory codes is achieved through integrated MPRA and ML approaches.

Conclusions:

• MPRAs coupled with ML provide powerful tools for understanding gene regulation.
• These methods facilitate variant stratification and the design of synthetic regulatory elements.
• Applications span synthetic biology, mRNA therapeutics, and gene therapy.