Dataset Information

Nanopore Dwell Time Analysis Permits Sequencing and Conformational Assignment of Pseudouridine in SARS-CoV-2.

ABSTRACT: Direct RNA sequencing for the epitranscriptomic modification pseudouridine (Ψ), an isomer of uridine (U), was conducted with a protein nanopore sensor using a helicase brake to slowly feed the RNA into the sensor. Synthetic RNAs with 100% Ψ or U in 20 different known human sequence contexts identified differences during sequencing in the base-calling, ionic current, and dwell time in the nanopore sensor; however, the signals were found to have a dependency on the context that would result in biases when sequencing unknown samples. A solution to the challenge was the identification that the passage of Ψ through the helicase brake produced a long-range dwell time impact with less context bias that was used for modification identification. The data analysis approach was employed to analyze publicly available direct RNA sequencing data for SARS-CoV-2 RNA taken from cell culture to locate five conserved Ψ sites in the genome. Two sites were found to be substrates for pseudouridine synthase 1 and 7 in an in vitro assay, providing validation of the analysis. Utilization of the helicase as an additional sensor in direct RNA nanopore sequencing provides greater confidence in calling RNA modifications.

SUBMITTER: Fleming AM

PROVIDER: S-EPMC8554835 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Nanopore Dwell Time Analysis Permits Sequencing and Conformational Assignment of Pseudouridine in SARS-CoV-2.

Fleming Aaron M AM Mathewson Nicole J NJ Howpay Manage Shereen A SA Burrows Cynthia J CJ

ACS central science 20210915 10

Direct RNA sequencing for the epitranscriptomic modification pseudouridine (Ψ), an isomer of uridine (U), was conducted with a protein nanopore sensor using a helicase brake to slowly feed the RNA into the sensor. Synthetic RNAs with 100% Ψ or U in 20 different known human sequence contexts identified differences during sequencing in the base-calling, ionic current, and dwell time in the nanopore sensor; however, the signals were found to have a dependency on the context that would result in bia ...[more]

PMID: 34729414

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Project description:Pseudouridine is one of the most abundant RNA modifications, occurring when uridines are catalyzed by Pseudouridine synthase proteins. It plays an important role in many biological processes and has been reported to have application in drug development. Recently, the single-molecule sequencing techniques such as the direct RNA sequencing platform offered by Oxford Nanopore technologies have enabled direct detection of RNA modifications on the molecule being sequenced. In this study, we introduce a tool called Penguin that integrates several machine learning (ML) models to identify RNA Pseudouridine sites on Nanopore direct RNA sequencing reads. Pseudouridine sites were identified on single molecule sequencing data collected from direct RNA sequencing resulting in 723 K reads in Hek293 and 500 K reads in Hela cell lines. Penguin extracts a set of features from the raw signal measured by the Oxford Nanopore and the corresponding basecalled k-mer. Those features are used to train the predictors included in Penguin, which in turn, can predict whether the signal is modified by the presence of Pseudouridine sites in the testing phase. We have included various predictors in Penguin, including Support vector machines (SVM), Random Forest (RF), and Neural network (NN). The results on the two benchmark data sets for Hek293 and Hela cell lines show outstanding performance of Penguin either in random split testing or in independent validation testing. In random split testing, Penguin has been able to identify Pseudouridine sites with a high accuracy of 93.38% by applying SVM to Hek293 benchmark dataset. In independent validation testing, Penguin achieves an accuracy of 92.61% by training SVM with Hek293 benchmark dataset and testing it for identifying Pseudouridine sites on Hela benchmark dataset. Thus, Penguin outperforms the existing Pseudouridine predictors in the literature by 16 % higher accuracy than those predictors using independent validation testing. Employing penguin to predict Pseudouridine sites revealed a significant enrichment of "regulation of mRNA 3'-end processing" in Hek293 cell line and 'positive regulation of transcription from RNA polymerase II promoter involved in cellular response to chemical stimulus' in Hela cell line. Penguin software and models are available on GitHub at https://github.com/Janga-Lab/Penguin and can be readily employed for predicting Ψ sites from Nanopore direct RNA-sequencing datasets.

Dataset Information

Nanopore Dwell Time Analysis Permits Sequencing and Conformational Assignment of Pseudouridine in SARS-CoV-2.

Publications

Nanopore Dwell Time Analysis Permits Sequencing and Conformational Assignment of Pseudouridine in SARS-CoV-2.

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