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RNA, Transfer

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tRNA-DL: A Deep Learning Approach to Improve tRNAscan-SE Prediction Results.

Human heredity
BACKGROUND: tRNAscan-SE is the leading tool for transfer RNA (tRNA) annotation, which has been widely used in the field. However, tRNAscan-SE can return a significant number of false positives when applied to large sequences. Recently, conventional m...

The first two mitochondrial genomes from Taeniopterygidae (Insecta: Plecoptera): Structural features and phylogenetic implications.

International journal of biological macromolecules
The complete mitochondrial genomes (mitogenomes) of Taeniopteryx ugola and Doddsia occidentalis (Plecoptera: Taeniopterygidae) were firstly sequenced from the family Taeniopterygidae. The 15,353-bp long mitogenome of T. ugola and the 16,020-bp long m...

Convolutional neural networks for classification of alignments of non-coding RNA sequences.

Bioinformatics (Oxford, England)
MOTIVATION: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successf...

Identification of D Modification Sites by Integrating Heterogeneous Features in .

Molecules (Basel, Switzerland)
As an abundant post-transcriptional modification, dihydrouridine (D) has been found in transfer RNA (tRNA) from bacteria, eukaryotes, and archaea. Nonetheless, knowledge of the exact biochemical roles of dihydrouridine in mediating tRNA function is s...

Fast and accurate microRNA search using CNN.

BMC bioinformatics
BACKGROUND: There are many different types of microRNAs (miRNAs) and elucidating their functions is still under intensive research. A fundamental step in functional annotation of a new miRNA is to classify it into characterized miRNA families, such a...

AI for the collective analysis of a massive number of genome sequences: various examples from the small genome of pandemic SARS-CoV-2 to the human genome.

Genes & genetic systems
In genetics and related fields, huge amounts of data, such as genome sequences, are accumulating, and the use of artificial intelligence (AI) suitable for big data analysis has become increasingly important. Unsupervised AI that can reveal novel know...

Identification of clinical trait-related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma.

Aging
Endocervical adenocarcinoma (EAC) is an aggressive type of endocervical cancer. At present, molecular research on EAC mainly focuses on the genome and mRNA transcriptome, the investigation of small RNAs in EAC has not been fully described. Here, we s...

Machine learning algorithm for precise prediction of 2'-O-methylation (Nm) sites from experimental RiboMethSeq datasets.

Methods (San Diego, Calif.)
Analysis of epitranscriptomic RNA modifications by deep sequencing-based approaches brings an essential contribution to the general knowledge on their precise locations and relative stoichiometry in cellular RNAs. To reveal RNA modifications, several...

RNADSN: Transfer-Learning 5-Methyluridine (mU) Modification on mRNAs from Common Features of tRNA.

International journal of molecular sciences
One of the most abundant non-canonical bases widely occurring on various RNA molecules is 5-methyluridine (m5U). Recent studies have revealed its influences on the development of breast cancer, systemic lupus erythematosus, and the regulation of stre...

Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection.

Nature communications
We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usuall...