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RNA Modification Level Estimation with pulseR

Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University Hospital Heidelberg, 69120 Heidelberg, Germany
German Centre for Cardiovascular Research (DZHK), Partner Site Heidelberg/Mannheim, 69120 Heidelberg, Germany
Authors to whom correspondence should be addressed.
Genes 2018, 9(12), 619;
Received: 9 November 2018 / Revised: 3 December 2018 / Accepted: 5 December 2018 / Published: 10 December 2018
(This article belongs to the Special Issue RNA Modifications)
RNA modifications regulate the complex life of transcripts. An experimental approach called LAIC-seq was developed to characterize modification levels on a transcriptome-wide scale. In this method, the modified and unmodified molecules are separated using antibodies specific for a given RNA modification (e.g., m6A). In essence, the procedure of biochemical separation yields three fractions: Input, eluate, and supernatent, which are subjected to RNA-seq. In this work, we present a bioinformatics workflow, which starts from RNA-seq data to infer gene-specific modification levels by a statistical model on a transcriptome-wide scale. Our workflow centers around the pulseR package, which was originally developed for the analysis of metabolic labeling experiments. We demonstrate how to analyze data without external normalization (i.e., in the absence of spike-ins), given high efficiency of separation, and how, alternatively, scaling factors can be derived from unmodified spike-ins. Importantly, our workflow provides an estimate of uncertainty of modification levels in terms of confidence intervals for model parameters, such as gene expression and RNA modification levels. We also compare alternative model parametrizations, log-odds, or the proportion of the modified molecules and discuss the pros and cons of each representation. In summary, our workflow is a versatile approach to RNA modification level estimation, which is open to any read-count-based experimental approach. View Full-Text
Keywords: RNA-seq; confidence interval; MeRIP; m6A; computational biology; software RNA-seq; confidence interval; MeRIP; m6A; computational biology; software
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MDPI and ACS Style

Boileau, E.; Dieterich, C. RNA Modification Level Estimation with pulseR. Genes 2018, 9, 619.

AMA Style

Boileau E, Dieterich C. RNA Modification Level Estimation with pulseR. Genes. 2018; 9(12):619.

Chicago/Turabian Style

Boileau, Etienne, and Christoph Dieterich. 2018. "RNA Modification Level Estimation with pulseR" Genes 9, no. 12: 619.

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