FORECAST

FORECAST simulates and evaluates Massively Parallel Reporter Assay (MPRA) experimental designs and infers genotype-to-phenotype mappings to optimize data quality and predictive accuracy.

Key Features:

• Simulation Capabilities: Performs Python-based simulations of cell-sorting and sequencing-based MPRAs to assess how experimental design parameters affect data quality and mapping accuracy.
• Maximum Likelihood-Based Inference: Implements maximum likelihood estimation to infer genetic design functions from MPRA readout data.
• Experimental Design Optimization: Simulates alternative experimental scenarios to identify design strategies that maximize data quality and the accuracy of genotype-to-phenotype inference.
• Deep Learning Integration: Supports simulation-based evaluation of deep learning-based classifiers to determine limits of prediction accuracy on MPRA-derived datasets.

Scientific Applications:

• MPRA Experimental Planning: Optimizing parameters for cell-sorting and sequencing-based MPRAs to improve phenotype measurement across large genetic libraries.
• Genotype-to-Phenotype Mapping: Deriving and validating genotype-to-phenotype relationships from MPRA data using maximum likelihood inference and simulation.
• Machine Learning Model Evaluation: Assessing feasibility and accuracy limits of deep learning models trained on MPRA datasets through simulation.

Methodology:

Python-based simulations of cell-sorting and sequencing processes, maximum likelihood estimation to infer genetic design functions, and simulation of MPRA experiments to evaluate prediction limits of deep learning-based classifiers.