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

Motivation: The ability to measure the phenotype of millions of different genetic designs using Massively Parallel Reporter Assays (MPRAs) has revolutionized our understanding of genotype-to-phenotype relationships and opened avenues for data-centric approaches to biological design. However, our knowledge of how best to design these costly experiments and the effect that our choices have on the quality of the data produced is lacking.Results: In this article, we tackle the issues of data quality and experimental design by developing FORECAST, a Python package that supports the accurate simulation of cell-sorting and sequencing-based MPRAs and robust maximum likelihood-based inference of genetic design function from MPRA data. We use FORECAST's capabilities to reveal rules for MPRA experimental design that help ensure accurate genotype-to-phenotype links and show how the simulation of MPRA experiments can help us better understand the limits of prediction accuracy when this data are used for training deep learning-based classifiers. As the scale and scope of MPRAs grows, tools like FORECAST will help ensure we make informed decisions during their development and the most of the data produced.

Automated summary

This article develops a Python package called FORECAST that accurately simulates cell-sorting and sequencing-based MPRAs and performs robust inference of genetic design function from MPRA data. The capabilities of FORECAST reveal rules for accurate genotype-to-phenotype links in MPRA experimental design and demonstrate how simulation of MPRA experiments can improve prediction accuracy when training deep learning-based classifiers. As MPRAs continue to grow in scale and scope, tools like FORECAST will help inform decision-making during their development and maximize the utility of the data produced.