MPRAnalyze: Statistical framework for massively parallel reporter assays

Ashuach T, Fischer DS, Kreimer A, Ahituv N, Theis FJ, Yosef N (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Genome Biology

Book Volume: 20

Article Number: 183

Journal Issue: 1

DOI: 10.1186/s13059-019-1787-z

Abstract

Massively parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. Despite growing popularity, MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. Here we present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences' activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods.

Involved external institutions

University of California, Berkeley US United States (USA) (US) Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) University of California San Francisco (UCSF) US United States (USA) (US)

How to cite

APA:

Ashuach, T., Fischer, D.S., Kreimer, A., Ahituv, N., Theis, F.J., & Yosef, N. (2019). MPRAnalyze: Statistical framework for massively parallel reporter assays. Genome Biology, 20(1). https://doi.org/10.1186/s13059-019-1787-z

MLA:

Ashuach, Tal, et al. "MPRAnalyze: Statistical framework for massively parallel reporter assays." Genome Biology 20.1 (2019).

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