NmRF: identification of multispecies RNA 2’-O-methylation modification sites from RNA sequences

Abstract 2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 23; no. 1
Main Authors: Ao, Chunyan, Zou, Quan, Yu, Liang
Format: Journal Article
Language:English
Published: England Oxford University Press 17.01.2022
Oxford Publishing Limited (England)
Subjects:
Algorithms
Base Sequence
Biological activity
Computational Biology - methods
Gene sequencing
Humans
Hydroxyl groups
Learning algorithms
Machine Learning
Methylation
Methyltransferase
miRNA
Post-transcription
Prediction models
Ribonucleic acid
RNA
RNA - genetics
RNA modification
tRNA
light gradient boosting
random forest
RNA
feature extraction
2’-O-methylation sites
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary:Abstract 2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbab480