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

Pierre-Aurelien M A Gilliot, Thomas E Gorochowski*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)


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. 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 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.
Original languageEnglish
Article numberbtad277
Issue number5
Early online date21 Apr 2023
Publication statusPublished - 1 May 2023

Bibliographical note

Funding Information:
This work was supported by the EPSRC/BBSRC Centre for Doctoral Training in Synthetic Biology grant [EP/L016494/1] (P.-A.G.), BrisEngBio, a UKRI-funded Engineering Biology Research Centre grant [BB/W013959/1] (T.E.G.), UKRI grant [BB/W012448/1] (T.E.G.), a Turing Fellowship from The Alan Turing Institute under EPSRC grant [EP/N510129/1] (T.E.G.), and a Royal Society University Research Fellowship grants [UF160357] and [URF\R\221008] (T.E.G.)

Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press.

Structured keywords

  • Bristol BioDesign Institute
  • BrisEngBio


  • Massively Parallel Reporter Assay
  • FACS
  • Sequencing
  • Synthetic Biology


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