Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments

Alina Selega, Christel Sirocchi, Ira Iosub, Sander Granneman, Guido Sanguinetti

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Structure probing coupled with high-throughput sequencing could revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite recent technological advances, intrinsic noise and high sequence coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline that accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome wide. Using two yeast data sets, we demonstrate that our method has increased sensitivity, and thus our pipeline identifies modified regions on many more transcripts than do existing pipelines. Our method also provides confident predictions at much lower sequence coverage levels than those recommended for reliable structural probing. Our results show that statistical modeling extends the scope and potential of transcriptome-wide structure probing experiments.
Original languageEnglish
Pages (from-to)83-89
Number of pages7
JournalNature Methods
Issue number1
Early online date7 Nov 2016
Publication statusPublished - 1 Jan 2017


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