Journal
NATURE METHODS
Volume 14, Issue 1, Pages 83-89Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.4068
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Funding
- Wellcome Trust [091549, 102334]
- European Research Council [MLC306999]
- Wellcome Trust Centre for Cell Biology core grant [092076]
- UK Engineering and Physical Sciences Research Council
- UK Medical Research Council [EP/F500385/1, BB/F529254/1]
- NERC [R8/H10/56]
- MRC [MR/K001744/1]
- BBSRC [BB/J004243/1]
- Biological Sciences Research Council
- BBSRC [BBS/E/D/20310000] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BBS/E/D/20310000] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [1359607] Funding Source: researchfish
- Medical Research Council [MR/K001744/1] Funding Source: researchfish
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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.
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