Nature CommunicationsGenomicsPeer-reviewed journal article
RNA modifications detection by comparative Nanopore direct RNA sequencing
Adrien LégeriD, Paulo AmaraliD, Luca PandolfiniiD, Charlotte CapitanchikiD, Federica CapraroiD, Valentina MianoiD, Valentina MiglioriiD, Patrick Toolan-KerriD, Theodora C. SideriiD, Anton James EnrightiD, Konstantinos TzelepisiD, Folkert Jacobus van WerveniD, Nicholas M. LuscombeiD, Isaia BarbieriiD, Jernej UleiD, Tomas W. FitzgeraldiD, Ewan BirneyiD, Tommaso LeonardiiD, Tony KouzaridesiD
Abstract
RNA molecules undergo a vast array of chemical post-transcriptional modifications (PTMs) that can affect their structure and interaction properties. To date, over 150 naturally occurring PTMs have been identified, however the overwhelming majority of their functions remain elusive. In recent years, a small number of PTMs have been successfully mapped to the transcriptome using experimental approaches relying on high-throughput sequencing. Oxford Nanopore direct-RNA sequencing (DRS) technology has been shown to be sensitive to RNA modifications. We developed and validated Nanocompore, a robust analytical framework to evaluate the presence of modifications in DRS data. To do so, we compare an RNA sample of interest against a non-modified control sample. Our strategy does not require a training set and allows the use of replicates to model biological variability. Here, we demonstrate the ability of Nanocompore to detect RNA modifications at single-molecule resolution in human polyA+ RNAs, as well as in targeted non-coding RNAs. Our results correlate well with orthogonal methods, confirm previous observations on the distribution of N6-methyladenosine sites and provide novel insights into the distribution of RNA modifications in the coding and non-coding transcriptomes. The latest version of Nanocompore can be obtained at https://github.com/tleonardi/nanocompore.
- organism: human
- assay: long-read sequencing
- research focus: differential expression
- approach: statistical model
- Citations
- 420
- FWCI
- 18.73
- Field percentile
- top 1%
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