MPRAnalyze: statistical framework for massively parallel reporter assays

Tal Ashuach; David S Fischer; Anat Kreimer; Nadav Ahituv; Fabian J Theis; Nir Yosef

doi:10.1186/s13059-019-1787-z

MPRAnalyze: statistical framework for massively parallel reporter assays

Genome Biol. 2019 Sep 2;20(1):183. doi: 10.1186/s13059-019-1787-z.

Authors

Tal Ashuach^{1

2}, David S Fischer^{3

4}, Anat Kreimer^{1

5

6}, Nadav Ahituv^{5

6}, Fabian J Theis³, Nir Yosef^{7

8

9

10}

Affiliations

¹ Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, California, USA.
² Center for Computational Biology, University of California Berkeley, Berkeley, California, USA.
³ Institute of Computational Biology, Helmholz Zentrum München, Neuherberg, Germany.
⁴ TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
⁵ Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA.
⁶ Institute for Human Genetics, University of California San Francisco, San Francisco, California, USA.
⁷ Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, California, USA. niryosef@berkeley.edu.
⁸ Center for Computational Biology, University of California Berkeley, Berkeley, California, USA. niryosef@berkeley.edu.
⁹ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA. niryosef@berkeley.edu.
¹⁰ Chan Zuckerberg BioHub, San Francisco, California, USA. niryosef@berkeley.edu.

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.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Alleles
Biological Assay*
Databases, Genetic
Gene Expression Profiling
Genes, Reporter*
Hep G2 Cells
High-Throughput Nucleotide Sequencing / methods*
Humans
K562 Cells
Software*
Statistics as Topic*

Grants and funding

U01HG007910/HG/NHGRI NIH HHS/United States