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MPRAnalyze: statistical framework for massively parallel reporter assays.

Authors :
Ashuach, Tal
Ashuach, Tal
Fischer, David S
Kreimer, Anat
Ahituv, Nadav
Theis, Fabian J
Yosef, Nir
Ashuach, Tal
Ashuach, Tal
Fischer, David S
Kreimer, Anat
Ahituv, Nadav
Theis, Fabian J
Yosef, Nir
Source :
Genome biology; vol 20, iss 1, 183; 1474-7596
Publication Year :
2019

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.

Details

Database :
OAIster
Journal :
Genome biology; vol 20, iss 1, 183; 1474-7596
Notes :
application/pdf, Genome biology vol 20, iss 1, 183 1474-7596
Publication Type :
Electronic Resource
Accession number :
edsoai.on1367407216
Document Type :
Electronic Resource