MPRAnalyze: Statistical framework for massively parallel reporter assays

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

doi:https://doi.org/10.1186/s13059-019-1787-z

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 journal › Article › peer-review

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 language	English
Article number	183
Journal	GENOME BIOLOGY
Volume	20
Issue number	1
DOIs	https://doi.org/10.1186/s13059-019-1787-z
State	Published - 2 Sep 2019
Externally published	Yes

All Science Journal Classification (ASJC) codes

Genetics
Ecology, Evolution, Behavior and Systematics
Cell Biology

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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.",

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T2 - Statistical framework for massively parallel reporter assays

AU - Ashuach, Tal

AU - Fischer, David S.

AU - Kreimer, Anat

AU - Ahituv, Nadav

AU - Theis, Fabian J.

AU - Yosef, Nir

PY - 2019/9/2

Y1 - 2019/9/2

N2 - 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.

AB - 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.

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DO - https://doi.org/10.1186/s13059-019-1787-z

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SN - 1474-7596

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JO - GENOME BIOLOGY

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ER -

MPRAnalyze: Statistical framework for massively parallel reporter assays

Abstract

All Science Journal Classification (ASJC) codes

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