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MPRAnalyze: statistical framework for massively parallel reporter assays.

Tal AshuachDavid S FischerAnat KreimerNadav AhituvFabian J TheisNir Yosef
Published in: Genome biology (2019)
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.
Keyphrases
  • electronic health record
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  • transcription factor
  • crispr cas
  • high throughput
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  • single molecule
  • cell free
  • single cell
  • deep learning
  • case control
  • circulating tumor