MPRAnalyze: Statistical framework for massively parallel reporter assays

  • Tal Ashuach
  • , David S. Fischer
  • , Anat Kreimer
  • , Nadav Ahituv
  • , Fabian J. Theis
  • , Nir Yosef

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

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.

Original languageEnglish (US)
Article number183
JournalGenome biology
Volume20
Issue number1
DOIs
StatePublished - Sep 2 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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