Effective design and inference for cell sorting and sequencing based massively parallel reporter assays

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

• Massively Parallel Reporter Assays (MPRAs) are revolutionizing genotype-to-phenotype relationship studies.
• Current understanding of optimal MPRA experimental design and its impact on data quality is limited.

Purpose of the Study:

• To address data quality and experimental design challenges in MPRAs.
• To develop a tool for accurate simulation and robust inference of genetic design function from MPRA data.

Main Methods:

• Development of FORECAST, a Python package for simulating cell-sorting and sequencing-based MPRAs.
• Utilizing FORECAST for maximum likelihood-based inference of genetic design function.
• Applying simulations to establish MPRA experimental design rules and assess prediction accuracy limits for deep learning classifiers.

Main Results:

• FORECAST enables accurate simulation of MPRA experiments.
• Identified key rules for MPRA experimental design to ensure reliable genotype-to-phenotype links.
• Quantified the impact of MPRA experimental design on deep learning classifier accuracy.

Conclusions:

• FORECAST is a valuable tool for informed decision-making in MPRA development.
• Optimizing MPRA experimental design with FORECAST maximizes data utility.
• This approach supports data-centric biological design and enhances genotype-to-phenotype understanding.