Common Analysis of Direct RNA SequencinG CUrrently Leads to Misidentification of 5-Methylcytosine Modifications at GCU Motifs
Watson, K.; Bromley, R.; Sparklin, B. C.; Gasser, M.; Bhattacharya, T.; Lebov, J. F.; Tyson, T.; Teigen, L.; Graf, K. T.; Michalski, M.; Bruno, V. M.; Lindsey, A. R. I.; Hardy, R. W.; Newton, I. L. G.; Dunning Hotopp, J. C.
Show abstract
RNA modifications, such as methylation, can be detected with Oxford Nanopore Technologies direct RNA sequencing. One commonly used tool for detecting 5-methylcytosine (m5C) modifications is Tombo, which uses an "Alternative Model" to detect putative modifications from a single sample. We examined direct RNA sequencing data from diverse taxa including virus, bacteria, fungi, and animals. The algorithm consistently identified a 5-methylcytosine at the central position of a GCU motif. However, it also identified a 5-methylcytosine in the same motif in fully unmodified in vitro transcribed RNA, suggesting that this a frequent false prediction. In the absence of further validation, several published predictions of 5-methylcytosine in human coronavirus and human cerebral organoid RNA in a GCU context should be reconsidered. IMPORTANCEThe detection of chemical modifications to RNA is a rapidly expanding field within epigenetics. Nanopore sequencing technology provides an attractive means of detecting these modifications directly on the RNA, but accurate modification predictions are dependent upon the software developed to interpret the sequencing results. One of these tools, Tombo, allows users to detect modifications using sequencing results from a single RNA sample. However, we find that this method falsely predicts modifications in a specific sequence context across a variety of RNA samples, including RNA that lacks modifications. Results from previous publications include predictions in human coronaviruses with this sequence context and should be reconsidered. Our results highlight the importance of using RNA modification detection tools with caution in the absence of a control RNA sample for comparison.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Co-variation of viral recombination with single nucleotide variants during virus evolution revealed by CoVaMa 94%
- Selective Ablation of 3' RNA ends and Processive RTs Facilitate Direct cDNA Sequencing of Full-length Host Cell and Viral Transcripts 94%
- Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA 93%
Similar papers in this journal
- High-resolution HIV-1 m6A epitranscriptome reveals isoform-dependent methylation clusters and unique 2-LTR transcript modifications 94%
- tRForest: a novel random forest-based algorithm for tRNA-derived fragment target prediction 93%
- FLYNC: A Machine Learning-Driven Framework for Discovering Long Non-Coding RNAs in Drosophila melanogaster 92%
Similar papers in this journal
- Identification of RNA 3' ends and termination sites in Haloferax volcanii 94%
- Intron-assisted, viroid-based production of insecticidal circular double-stranded RNA in Escherichia coli 93%
- Insights into the secondary and tertiary structure of the Bovine Viral Diarrhea Virus Internal Ribosome Entry Site 92%
Similar papers in this journal
- Synthesis of low immunogenicity RNA with high-temperature in vitro transcription. 93%
- Synthesis of modified nucleotide polymers by the poly(U) polymerase Cid1: Application to direct RNA sequencing on nanopores. 92%
- The m6A landscape of polyadenylated nuclear (PAN) RNA and its related methylome in the context of KSHV replication. 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.