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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)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Regulatory Elements; Dynamics
ISSN (print) / ISBN 1474-760X
e-ISSN 1465-6906
Zeitschrift Genome Biology
Quellenangaben Band: 20, Heft: 1, Seiten: , Artikelnummer: 183 Supplement: ,
Verlag BioMed Central
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Begutachtungsstatus