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Optimizing sequence design strategies for perturbation MPRAs: a computational evaluation framework.

Jiayi Liu^1,2,3, Tal Ashuach⁴, Fumitaka Inoue⁵

¹Graduate Program in Cell & Developmental Biology, Rutgers, The State University of New Jersey, 604 Allison Rd, Piscataway, NJ 08854, USA.

Nucleic Acids Research

|January 31, 2024

Summary

This summary is machine-generated.

We developed a computational framework to assess sequence design for perturbation massively parallel reporter assays (MPRAs). Randomly shuffling nucleotides in the perturbed site offers the most robust predictive modeling for regulatory motif activity.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Massively parallel reporter assays (MPRAs) are crucial for understanding gene regulation.
Perturbation-based MPRAs (perturbation-MPRAs) help identify non-coding regulatory elements.
Limited computational guidelines exist for designing perturbation-MPRA sequences.

Purpose of the Study:

To establish a computational framework for evaluating perturbation strategies in MPRAs.
To benchmark different sequence design approaches for perturbation-MPRAs.
To provide guidelines for optimizing perturbation-MPRA experiments.

Main Methods:

Developed a framework to evaluate MPRA perturbation strategies.
Benchmarked three perturbation approaches: motif profile alteration, MPRA output consistency, and predictive model robustness.
Assessed sequence designs based on random nucleotide shuffling and coherence checks.

Main Results:

All evaluated perturbation approaches yielded similar results across multiple metrics.
Predictive modeling showed significantly higher robustness with random nucleotide shuffling.
Random nucleotide shuffling, followed by a coherence check, is recommended for sequence design.

Conclusions:

The proposed framework offers valuable computational pipelines for perturbation-MPRA analysis.
Random nucleotide shuffling is a robust strategy for designing perturbation-MPRA sequences.
Perturbation-MPRA holds significant potential for predicting non-coding regulatory activities.