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Ashuach, T.* ; Fischer, D.S. ; Kreimer, A.* ; Ahituv, N.* ; Theis, F.J. ; Yosef, N.*

MPRAnalyze: Statistical framework for massively parallel reporter assays.

Genome Biol. 20:183 (2019)
Publ. Version/Full Text DOI
Open Access Gold
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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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Regulatory Elements; Dynamics
ISSN (print) / ISBN 1474-760X
e-ISSN 1465-6906
Journal Genome Biology
Quellenangaben Volume: 20, Issue: 1, Pages: , Article Number: 183 Supplement: ,
Publisher BioMed Central
Publishing Place Campus, 4 Crinan St, London N1 9xw, England
Reviewing status