Abstract：
: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.
journal_name
Nat Methodsjournal_title
Nature methodsauthors
Selega A,Sirocchi C,Iosub I,Granneman S,Sanguinetti Gdoi
10.1038/nmeth.4068subject
Has Abstractpub_date
2017-01-01 00:00:00pages
83-89issue
1eissn
1548-7091issn
1548-7105pii
nmeth.4068journal_volume
14pub_type
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