Nm-Nano: A Machine Learning Framework for Transcriptome-Wide Single Molecule Mapping of 2{acute}-O-Methylation (Nm) Sites in Nanopore Direct RNA Sequencing Datasets (Updated February 17, 2024).
In: Genomics & Genetics Weekly, 2024-03-08, S. 1220-1220
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Zugriff:
The article discusses the development of a machine learning framework called Nm-Nano for predicting the presence of 2'-O-methylation (Nm) sites in Nanopore direct RNA sequencing data of human cell lines. Nm is a common modification of mRNAs and non-coding RNAs that plays a role in various biological processes. The Nm-Nano framework integrates two supervised machine learning models, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to predict Nm sites. The framework achieved high accuracy in identifying Nm sites in benchmark datasets of Hela and Hek293 human cell lines. The identified Nm-modified genes in these cell lines were found to be involved in immune response, cellular processes, metabolic pathways, protein degradation, and localization. The source code for Nm-Nano is freely available. [Extracted from the article]
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Nm-Nano: A Machine Learning Framework for Transcriptome-Wide Single Molecule Mapping of 2{acute}-O-Methylation (Nm) Sites in Nanopore Direct RNA Sequencing Datasets (Updated February 17, 2024).
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Zeitschrift: | Genomics & Genetics Weekly, 2024-03-08, S. 1220-1220 |
Veröffentlichung: | 2024 |
Medientyp: | serialPeriodical |
ISSN: | 1531-6467 (print) |
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