Abstract
Direct RNA sequencing holds great promise for the de novo identification of RNA modifications at single-coordinate resolution; however, interpretation of raw sequencing output to discover modified bases remains a challenge. Using Oxford Nanopore's direct RNA sequencing technology, we developed a random forest classifier trained using experimentally detected N6-methyladenosine (m6A) sites within DRACH motifs. Our software MINES (m6A Identification using Nanopore Sequencing) assigned m6A methylation status to more than 13,000 previously unannotated DRACH sites in endogenous HEK293T transcripts and identified more than 40,000 sites with isoform-level resolution in a human mammary epithelial cell line. These sites displayed sensitivity to the m6A writer, METTL3, and eraser, ALKBH5, respectively. MINES (https://github.com/YeoLab/MINES.git) enables m6A annotation at single coordinate–level resolution from direct RNA nanopore sequencing.
Highlights
Since the identification of the first RNA modification more than 60 years ago, more than 100 different RNA modifications have been identified (Davis and Allen 1957; Jonkhout et al 2017)
To overcome this limitation while simultaneously maintaining a low computational burden, we reasoned that filtering nanopore data based on the known m6A DRACH motifs would be a pragmatic strategy for m6A detection
Having generated a nanopore-enabled m6A detection algorithm, MINES, we evaluated the non-CLIP sites and preues, and the random forest model (RFM) generated for four motifs (AGACT, GGACA, GGACC, GGACT), MINES assigned m6A status to 42,116 sites
Summary
Since the identification of the first RNA modification more than 60 years ago, more than 100 different RNA modifications have been identified (Davis and Allen 1957; Jonkhout et al 2017). Second-generation polymerase-based sequencing has enabled transcriptome-wide studies of RNA biology, new third-generation sequencing is being developed to overcome limitations such as amplification biases, lack of single-molecule sensitivity, and isoform ambiguity. One of these methods, commercialized by Oxford Nanopore Technologies (ONT), uses nanopore-based sequencing to detect changes in electric current as a single strand of nucleic acid sequence transverses a pore protein. By deconvoluting these electrical signals, the specific nucleotide sequence can be reconstructed. As nanopore-based sequencing becomes ubiquitous in RNA-seq studies, our approach will facilitate new discoveries regarding m6A biology and serves as a useful framework for analyzing other RNA modifications using direct RNA sequencing
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