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Evaluation of Nanopore direct RNA sequencing updates for modification detection

Esfahani, N. G.; Stein, A. J.; Akeson, S.; Tzadikario, T.; Jain, M.

2025-05-04 genomics
10.1101/2025.05.01.651717 bioRxiv
Show abstract

Nanopore technology can directly sequence intact RNA molecules, offering a unique capability to read native modifications. Oxford Nanopore Technologies recently updated its direct RNA sequencing technology from RNA002 to RNA004 chemistry. This update included an improved basecaller (Dorado) for increased sequencing accuracy, and ionic current models for de novo detection of four RNA modifications. Using a single RNA extraction from GM12878 B-lymphocyte cell line, we compared RNA002 and RNA004 sequencing chemistries and evaluated Dorado modification calling accuracy. We computed U-to-C mismatches, previously used to identify putative pseudouridine sites, and ran m6anet for identifying putative N6-methyladenosine sites. Dorado results for each respective modification showed both global and site-specific differences when compared to RNA002 results. We used DRS data from in vitro transcription of GM12878 genomic DNA as well as synthetic oligonucleotides to evaluate Dorado modification calling performance. Dorados pseudouridine model achieved 96-98% for both accuracy and F1-score. Similarly, Dorados N6-methyladenosine model achieved 94-98% accuracy, 96-99% F1-score. Our results demonstrate that Nanopore Direct RNA sequencing could simultaneously detect pseudouridine, N6-methyladenosine, 5-methylcytosine, and inosine modifications on individual mRNA strands. It is critical to validate these calls using orthogonal methods (e.g., Liquid Chromatography with Tandem Mass Spectrometry) for increased confidence. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/651717v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1215534org.highwire.dtl.DTLVardef@160f38forg.highwire.dtl.DTLVardef@164a9corg.highwire.dtl.DTLVardef@17c72e5_HPS_FORMAT_FIGEXP M_FIG C_FIG

Published in Genome Biology (predicted rank #1) · training set

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