NCYPred: A Bidirectional LSTM Network With Attention for Y RNA and Short Non-Coding RNA Classification

Short non-coding RNAs (sncRNAs) are involved in multiple cellular processes and can be divided into dozens of classes. Among such classes, Y RNAs have been gaining attention, being essential factors for the initiation of DNA replication on vertebrates, as well as potential tumor biomarkers. Homologs...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2023-01, Vol.20 (1), p.557-565
Main Authors: Lima, Diego de S., Amichi, Luiz J. A., Fernandez, Maria A., Constantino, Ademir A., Seixas, Flavio A. V.
Format: Article
Language:English
Subjects:
Animals
Bacteria - genetics
Biological system modeling
Biomarkers
Classification
Classification algorithms
Computers
DNA biosynthesis
Encoding
Feature extraction
Gene sequencing
Homology
Insects
Nematodes
Non-coding RNA
Nucleotides
Performance evaluation
Predictive models
recurrent neural network
Replication initiation
Ribonucleic acid
RNA
RNA, Small Untranslated - genetics
Sequence analysis
Sequence Analysis, RNA
sequence classification
Training
Vertebrates
web server
Y RNA
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Summary:Short non-coding RNAs (sncRNAs) are involved in multiple cellular processes and can be divided into dozens of classes. Among such classes, Y RNAs have been gaining attention, being essential factors for the initiation of DNA replication on vertebrates, as well as potential tumor biomarkers. Homologs have also been described in nematodes and insects, as well as related sequences in bacteria. Methods capable of accurately predicting Y RNA transcripts are lacking. In this work, we developed an attention-based LSTM network and built a classification model able to classify sncRNAs (including Y RNA) directly from nucleotide sequences. A dataset consisting of 45,447 sncRNA sequences, from a wide range of organisms, obtained from Rfam 14.3 was built. Performance evaluation demonstrated that our proposed method, NCYPred ( N on -C oding/ Y RNA Pred iction ), can accurately predict Y RNA sequences and their homologs, as well as 11 additional classes, achieving results comparable with state-of-the-art methods. We also demonstrate that applying t-SNE on learned sequence representations could be useful for sequence analysis. Our model is freely available as a web-server ( https://www.gpea.uem.br/ncypred/ ).
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2021.3131136