Extracting information from RNA SHAPE data: Kalman filtering approach

RNA SHAPE experiments have become important and successful sources of information for RNA structure prediction. In such experiments, chemical reagents are used to probe RNA backbone flexibility at the nucleotide level, which in turn provides information on base pairing and therefore secondary struct...

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Bibliographic Details
Published in:PloS one 2018-11, Vol.13 (11), p.e0207029-e0207029
Main Authors: Vaziri, Sana, Koehl, Patrice, Aviran, Sharon
Format: Article
Language:English
Subjects:
Bias
Bioinformatics
Biology
Biology and life sciences
Computer science
Computer simulation
Data processing
Engineering and Technology
Experiments
Filtration
Gene expression
Genomes
Kalman filters
NMR
Noise
Nuclear magnetic resonance
Organic chemistry
Physical Sciences
Protein structure
Reagents
Representations
Research and Analysis Methods
Ribonucleic acid
RNA
RNA probes
RNA processing
Secondary structure
Signal processing
Statistical methods
Transformation
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Summary:RNA SHAPE experiments have become important and successful sources of information for RNA structure prediction. In such experiments, chemical reagents are used to probe RNA backbone flexibility at the nucleotide level, which in turn provides information on base pairing and therefore secondary structure. Little is known, however, about the statistics of such SHAPE data. In this work, we explore different representations of noise in SHAPE data and propose a statistically sound framework for extracting reliable reactivity information from multiple SHAPE replicates. Our analyses of RNA SHAPE experiments underscore that a normal noise model is not adequate to represent their data. We propose instead a log-normal representation of noise and discuss its relevance. Under this assumption, we observe that processing simulated SHAPE data by directly averaging different replicates leads to bias. Such bias can be reduced by analyzing the data following a log transformation, either by log-averaging or Kalman filtering. Application of Kalman filtering has the additional advantage that a prior on the nucleotide reactivities can be introduced. We show that the performance of Kalman filtering is then directly dependent on the quality of that prior. We conclude the paper with guidelines on signal processing of RNA SHAPE data.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0207029