Unraveling C-to-U RNA editing events from direct RNA sequencing

ABSTRACT

In mammals, RNA editing events involve the conversion of adenosine (A) in inosine (I) by ADAR enzymes or the hydrolytic deamination of cytosine (C) in uracil (U) by the APOBEC family of enzymes, mostly APOBEC1. RNA editing has a plethora of biological functions, and its deregulation has been associated with various human disorders. While the large-scale detection of A-to-I is quite straightforward using the Illumina RNAseq technology, the identification of C-to-U events is a non-trivial task. This difficulty arises from the rarity of such events in eukaryotic genomes and the challenge of distinguishing them from background noise. Direct RNA sequencing by Oxford Nanopore Technology (ONT) permits the direct detection of Us on sequenced RNA reads. Surprisingly, using ONT reads from wild-type (WT) and APOBEC1-knock-out (KO) murine cell lines as well as in vitro synthesized RNA without any modification, we identified a systematic error affecting the accuracy of the Cs call, thereby leading to incorrect identifications of C-to-U events. To overcome this issue in direct RNA reads, here we introduce a novel machine learning strategy based on the isolation Forest (iForest) algorithm in which C-to-U editing events are considered as sequencing anomalies. Using in vitro synthesized and human ONT reads, our model optimizes the signal-to-noise ratio improving the detection of C-to-U editing sites with high accuracy, over 90% in all samples tested. Our results suggest that iForest, known for its rapid implementation and minimal memory requirements, is a promising tool to denoise ONT reads and reliably identify RNA modifications.

KEYWORDS:

• Direct RNA sequencing
• RNA editing
• iForest
• RNA modifications
• C-to-U editing

Acknowledgments

We thank the Papavasiliou lab at the German Cancer Research Centre, Heidelberg, Germany, for supplying total RNA from WT and APOBEC1 KO cell line RAW 264.7.

The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Illumina data used for the analyses described in this manuscript were obtained from dbGaP accession number phs000424.

Authors are also grateful to the following National Research Centers: “High Performance Computing, Big Data and Quantum Computing” (Project no. CN_00000013) and “Gene Therapy and Drugs based on RNA Technology” (Project no. CN_00000041); and Extended Partnerships: MNESYS (Project no. PE_0000006) and Age-It (Project no. PE_00000015). This work was also supported by ELIXIR-IT thorugh the empowering project ELIXIRNextGenIT (Grant Code IR0000010) .

Finally, we warmly thank Dr Sharon N. Cox for improving the readability of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Illumina and Nanopore raw reads from mouse cells have been submitted to the SRA database under the BioProject PRJNA949094. Illumina and Nanopore raw reads from human cells, instead, are available under the accession PRJNA1050198. The Python code implementing the above described methodology, as well as the accessory jupyter notebooks used for data analysis, are available at the GitHub repository: https://github.com/F0nz0/C_to_U_classifier

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15476286.2023.2290843.

Additional information

Funding

This work was supported by: Elixir; National Research Centre for High Performance Computing, Big Data and Quantum Computing; National Research Centre for Gene Therapy and Drugs based on RNA Technology; MNESYS, a multiscale integrated approach to the study of the nervous system in health and disease; Age-It: A novel public-private alliance to generate socioeconomic, biomedical and technological solutions for an inclusive Italian ageing society. The research was also supported by a grant from AIRC [IG-17701]

Notes on contributors

Adriano Fonzino

A.F. and E.P. conceived the study. A.F. performed computational analyses and developed ML models. C.M., P.S., U.M. and S.T. carried out Illumina and Nanopore sequencing. A.F. and E.P. wrote the manuscript draft. S.C., E.P. and G.P. contributed to the interpretation of the data and manuscript revision. All authors read and approved the final manuscript.