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

Abstract

The ability to measure the phenotype of millions of different genetic designs using Massively Parallel Reporter Assays (MPRAs) has revolutionised 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. Here, we tackle this issue by developing FORE-CAST, a Python package that supports the accurate simulation of cell-sorting and sequencing based MPRAs and robust maximum like-lihood based inference of genetic design function from MPRA data. We use FORECASTs 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 is 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.