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MPRAnalyze: statistical framework for massively parallel reporter assays

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 frame...

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Detalles Bibliográficos
Autores principales: Ashuach, Tal, Fischer, David S., Kreimer, Anat, Ahituv, Nadav, Theis, Fabian J., Yosef, Nir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717970/
https://www.ncbi.nlm.nih.gov/pubmed/31477158
http://dx.doi.org/10.1186/s13059-019-1787-z
Descripción
Sumario: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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1787-z) contains supplementary material, which is available to authorized users.