MPRAscore: robust and non-parametric analysis of massively parallel reporter assays

Abhishek Niroula; Ram Ajore; Björn Nilsson

doi:10.1093/bioinformatics/btz591

MPRAscore: robust and non-parametric analysis of massively parallel reporter assays

Abhishek Niroula, Ram Ajore, Björn Nilsson

Hematogenomics

Broad Institute

Research output: Contribution to journal › Article › peer-review

Abstract

MOTIVATION: Massively parallel reporter assays (MPRA) enable systematic screening of DNA sequence variants for effects on transcriptional activity. However, convenient analysis tools are still needed. RESULTS: We introduce MPRAscore, a novel tool to infer allele-specific effects on transcription from MPRA data. MPRAscore uses a weighted, variance-regularized method to calculate variant effect sizes robustly, and a permutation approach to test for significance without assuming normality or independence. AVAILABILITY AND IMPLEMENTATION: Source code (C++), precompiled binaries and data used in the paper at https://github.com/abhisheknrl/MPRAscore and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA554195. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original language	English
Pages (from-to)	5351-5353
Number of pages	3
Journal	Bioinformatics
Volume	35
Issue number	24
DOIs	https://doi.org/10.1093/bioinformatics/btz591
Publication status	Published - 2019 Dec 15

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Bioinformatics and Systems Biology
Medical Genetics

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10.1093/bioinformatics/btz591

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title = "MPRAscore: robust and non-parametric analysis of massively parallel reporter assays",

abstract = "MOTIVATION: Massively parallel reporter assays (MPRA) enable systematic screening of DNA sequence variants for effects on transcriptional activity. However, convenient analysis tools are still needed. RESULTS: We introduce MPRAscore, a novel tool to infer allele-specific effects on transcription from MPRA data. MPRAscore uses a weighted, variance-regularized method to calculate variant effect sizes robustly, and a permutation approach to test for significance without assuming normality or independence. AVAILABILITY AND IMPLEMENTATION: Source code (C++), precompiled binaries and data used in the paper at https://github.com/abhisheknrl/MPRAscore and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA554195. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",

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year = "2019",

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doi = "10.1093/bioinformatics/btz591",

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volume = "35",

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AU - Niroula, Abhishek

AU - Ajore, Ram

AU - Nilsson, Björn

PY - 2019/12/15

Y1 - 2019/12/15

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AB - MOTIVATION: Massively parallel reporter assays (MPRA) enable systematic screening of DNA sequence variants for effects on transcriptional activity. However, convenient analysis tools are still needed. RESULTS: We introduce MPRAscore, a novel tool to infer allele-specific effects on transcription from MPRA data. MPRAscore uses a weighted, variance-regularized method to calculate variant effect sizes robustly, and a permutation approach to test for significance without assuming normality or independence. AVAILABILITY AND IMPLEMENTATION: Source code (C++), precompiled binaries and data used in the paper at https://github.com/abhisheknrl/MPRAscore and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA554195. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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JF - Bioinformatics

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ER -

MPRAscore: robust and non-parametric analysis of massively parallel reporter assays

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