Ashuach T, Fischer DS, Kreimer A, Ahituv N, Theis FJ, Yosef N (2019)
Publication Type: Journal article
Publication year: 2019
Book Volume: 20
Article Number: 183
Journal Issue: 1
DOI: 10.1186/s13059-019-1787-z
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.
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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