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MPRAnalyze: statistical framework for massively parallel reporter assays
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 frame...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717970/ https://www.ncbi.nlm.nih.gov/pubmed/31477158 http://dx.doi.org/10.1186/s13059-019-1787-z |
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author | Ashuach, Tal Fischer, David S. Kreimer, Anat Ahituv, Nadav Theis, Fabian J. Yosef, Nir |
author_facet | Ashuach, Tal Fischer, David S. Kreimer, Anat Ahituv, Nadav Theis, Fabian J. Yosef, Nir |
author_sort | Ashuach, Tal |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1787-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6717970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-67179702019-09-06 MPRAnalyze: statistical framework for massively parallel reporter assays Ashuach, Tal Fischer, David S. Kreimer, Anat Ahituv, Nadav Theis, Fabian J. Yosef, Nir Genome Biol Method 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1787-z) contains supplementary material, which is available to authorized users. BioMed Central 2019-09-02 /pmc/articles/PMC6717970/ /pubmed/31477158 http://dx.doi.org/10.1186/s13059-019-1787-z Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Method Ashuach, Tal Fischer, David S. Kreimer, Anat Ahituv, Nadav Theis, Fabian J. Yosef, Nir MPRAnalyze: statistical framework for massively parallel reporter assays |
title | MPRAnalyze: statistical framework for massively parallel reporter assays |
title_full | MPRAnalyze: statistical framework for massively parallel reporter assays |
title_fullStr | MPRAnalyze: statistical framework for massively parallel reporter assays |
title_full_unstemmed | MPRAnalyze: statistical framework for massively parallel reporter assays |
title_short | MPRAnalyze: statistical framework for massively parallel reporter assays |
title_sort | mpranalyze: statistical framework for massively parallel reporter assays |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717970/ https://www.ncbi.nlm.nih.gov/pubmed/31477158 http://dx.doi.org/10.1186/s13059-019-1787-z |
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