Penguin: A tool for predicting pseudouridine sites in direct RNA nanopore sequencing data

Hassan, Doaa; Acevedo, Daniel; Daulatabad, Swapna Vidhur; Mir, Quoseena; Janga, Sarath Chandra

doi:10.1016/j.ymeth.2022.02.005

Cited by 19 publications

(9 citation statements)

References 27 publications

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“…Great efforts are made to implement tools for the detection of RNA modifications, including machine learning classifiers (neural network, random forest, logistic regression, and naive Bayes classifiers) or statistical test-based tools which can detect de novo modifications without training using modified and unmodified samples ( Xu and Seki, 2020 ). Among the latest bioinformatic tools, promising appear to be ELIGOS (epitranscriptional landscape inferring from glitches of ONT signals) ( Jenjaroenpun et al, 2021 ) NanoPsu ( Huang et al, 2021 ) and Penguin ( Hassan et al, 2022 ). However, at the moment the overall precision of pseudouridine detection with this sequencing method remains low and not quantitative ( Begik et al, 2021 ) for native full-length molecules extensively modified such as human rRNA.…”

Section: Pseudouridylation Detection Methodsmentioning

confidence: 99%

Ribosomal RNA Pseudouridylation: Will Newly Available Methods Finally Define the Contribution of This Modification to Human Ribosome Plasticity?

et al. 2022

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In human rRNA, at least 104 specific uridine residues are modified to pseudouridine. Many of these pseudouridylation sites are located within functionally important ribosomal domains and can influence ribosomal functional features. Until recently, available methods failed to reliably quantify the level of modification at each specific rRNA site. Therefore, information obtained so far only partially explained the degree of regulation of pseudouridylation in different physiological and pathological conditions. In this focused review, we provide a summary of the methods that are now available for the study of rRNA pseudouridylation, discussing the perspectives that newly developed approaches are offering.

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Section: Pseudouridylation Detection Methodsmentioning

confidence: 99%

Ribosomal RNA Pseudouridylation: Will Newly Available Methods Finally Define the Contribution of This Modification to Human Ribosome Plasticity?

et al. 2022

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“…Next, eventalign module of the Nanopolish software that performs signal extraction is launched, which produces a dataset of Nanopore signal samples. Therefore, the structure of Nm-Nano’s pipeline emphasizises that it has some common parts with the pipeline of Penguin [35], our early developed tool for detecting Pseudouridine sites in long reads RNA sequence. However, our Nm-Nano’s pipeline (Figure 1.a) is different from Penguin’s pipeline in three phases: the benchmark dataset generation, the feature extraction and ML models construction phases.…”

Section: Methodsmentioning

confidence: 99%

Nm-Nano: A Machine Learning Framework for Transcriptome-Wide Single Molecule Mapping of 2´-O-Methylation (Nm) Sites in Nanopore Direct RNA Sequencing Datasets

Hassan

Ariyur

Daulatabad

et al. 2022

Preprint

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Nm (2′-O-methylation) is one of the most abundant modifications of mRNAs and non-coding RNAs occurring when a methyl group (–CH3) is added to the 2′ hydroxyl (–OH) of the ribose moiety. This modification can appear on any nucleotide (base) regardless of the type of nitrogenous base, because each ribose sugar has a hydroxyl group and so 2′-O-methyl ribose can occur on any base. Nm modification has a great contribution in many biological processes such as the normal functioning of tRNA, the protection of mRNA against degradation by DXO, and the biogenesis and specificity of rRNA. Recently, the single-molecule sequencing techniques for long reads of RNA sequences data offered by Oxford Nanopore technologies have enabled the direct detection of RNA modifications on the molecule that is being sequenced, but to our knowledge there was only one research attempt that applied this technology to predict the stoichiometry of Nm-modified sites in RNA sequence of yeast cells. To this end, in this paper, we extend this research direction by proposing a bio-computational framework, Nm-Nano for predicting Nm sites in Nanopore direct RNA sequencing reads of human cell lines. Nm-Nano framework integrates two supervised machine learning models for predicting Nm sites in Nanopore sequencing data, namely Xgboost and Random Forest (RF). Each model is trained with set of features that are extracted from the raw signal generated by the Oxford Nanopore MinION device, as well as the corresponding basecalled k-mer resulting from inferring the RNA sequence reads from the generated Nanopore signals. The results on two benchmark data sets generated from RNA Nanopore sequencing data of Hela and Hek293 cell lines show a great performance of Nm-Nano. In independent validation testing, Nm-Nano has been able to identify Nm sites with a high accuracy of 93% and 88% using Xgboost and RF models respectively by training each model with Hela benchmark dataset and testing it for identifying Nm sites on Hek293 benchmark dataset. Thus, Nm-Nano outperforms the Nm sites predictors existing in the literature (not relying on Nanopore technology) that were only limited to predict Nm sites on short reads of RNA sequences and unable to predict Nm sites on long RNA sequence reads. By deploying Nm-Nano to predict Nm sites in Hela cell line, it was revealed that a total of 196 genes was identified to have the most abundance of Nm modification among all other genes that have been modified by Nm in this cell line. Similarly, deploying Nm-Nano to predict Nm sites in Hek393 cell line revealed that a total of 196 genes line was identified to have the most abundance of Nm modification among all other genes that have been modified by Nm in this cell line. According to this, a significant enrichment of a wide range of functional processes like high confidences (adjusted p-val < 0.05) enriched ontologies that were more representative of Nm modification role in immune response and cellular homeostasis were revealed in Hela cell line, and "MHC class 1 protein complex", "mitotic spindle assembly", "response to glucocorticoid", and "nucleocytoplasmic transport" were revealed in Hek293 cell line. The source code of Nm-Nano can be freely accessed https://github.com/Janga-Lab/Nm-Nano.

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“…"trace") signals produced at 2′-O-methyl (Nm) sites in S. cerevisiae rRNA, but note that the signals produced by 2′-O-methylation are less reproducible across different sequence contexts (Begik et al, 2021). A similar detection approach has also been used to identify inducible Ψ positions in interferon responsive genes (Huang S. et al, 2021), and a third pseudoU detection pipeline, Penguin, claims ~93% accuracy in Ψ detection on mRNAs from HEK293 cells (Hassan et al, 2022).…”

Section: Direct Detection Of Endogenous Rna Modificationsmentioning

confidence: 99%

“…“trace”) signals produced at 2′-O-methyl (Nm) sites in S. cerevisiae rRNA, but note that the signals produced by 2′-O-methylation are less reproducible across different sequence contexts ( Begik et al, 2021 ). A similar detection approach has also been used to identify inducible Ψ positions in interferon responsive genes ( Huang S. et al, 2021 ), and a third pseudoU detection pipeline, Penguin, claims ∼93% accuracy in Ψ detection on mRNAs from HEK293 cells ( Hassan et al, 2022 ). Stephenson and others also profiled Nm sites on yeast rRNA, noting that like RNA acylation, methylation of the 2′-hydroxyl position on RNA produces increases in dwell time at a registration distance consistent with interaction(s) with the nanopore motor protein ( Stephenson et al, 2022 ).…”

Section: Direct Detection Of Endogenous Rna Modificationsmentioning

confidence: 99%

Modification mapping by nanopore sequencing

White

Hesselberth

2022

Front. Genet.

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Next generation sequencing (NGS) has provided biologists with an unprecedented view into biological processes and their regulation over the past 2 decades, fueling a wave of development of high throughput methods based on short read DNA and RNA sequencing. For nucleic acid modifications, NGS has been coupled with immunoprecipitation, chemical treatment, enzymatic treatment, and/or the use of reverse transcriptase enzymes with fortuitous activities to enrich for and to identify covalent modifications of RNA and DNA. However, the majority of nucleic acid modifications lack commercial monoclonal antibodies, and mapping techniques that rely on chemical or enzymatic treatments to manipulate modification signatures add additional technical complexities to library preparation. Moreover, such approaches tend to be specific to a single class of RNA or DNA modification, and generate only indirect readouts of modification status. Third generation sequencing technologies such as the commercially available “long read” platforms from Pacific Biosciences and Oxford Nanopore Technologies are an attractive alternative for high throughput detection of nucleic acid modifications. While the former can indirectly sense modified nucleotides through changes in the kinetics of reverse transcription reactions, nanopore sequencing can in principle directly detect any nucleic acid modification that produces a signal distortion as the nucleic acid passes through a nanopore sensor embedded within a charged membrane. To date, more than a dozen endogenous DNA and RNA modifications have been interrogated by nanopore sequencing, as well as a number of synthetic nucleic acid modifications used in metabolic labeling, structure probing, and other emerging applications. This review is intended to introduce the reader to nanopore sequencing and key principles underlying its use in direct detection of nucleic acid modifications in unamplified DNA or RNA samples, and outline current approaches for detecting and quantifying nucleic acid modifications by nanopore sequencing. As this technology matures, we anticipate advances in both sequencing chemistry and analysis methods will lead to rapid improvements in the identification and quantification of these epigenetic marks.

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Penguin: A tool for predicting pseudouridine sites in direct RNA nanopore sequencing data

Cited by 19 publications

References 27 publications

Ribosomal RNA Pseudouridylation: Will Newly Available Methods Finally Define the Contribution of This Modification to Human Ribosome Plasticity?

Ribosomal RNA Pseudouridylation: Will Newly Available Methods Finally Define the Contribution of This Modification to Human Ribosome Plasticity?

Nm-Nano: A Machine Learning Framework for Transcriptome-Wide Single Molecule Mapping of 2´-O-Methylation (Nm) Sites in Nanopore Direct RNA Sequencing Datasets

Modification mapping by nanopore sequencing

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