AIMC Topic: RNA Processing, Post-Transcriptional

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Interpretability-guided RNA N-methyladenosine modification site prediction with invertible neural networks.

Communications biology Jul 8, 2025
As one of the most common and abundant post-transcriptional modifications, N-methyladenosine (mA) has been extensively studied for its essential regulatory role in gene expression and cell functions. The location of mA RNA modification sites, however...
Adenosine RNA Processing, Post-Transcriptional RNA Methylation Humans Neural Networks, Computer Animals Deep Learning Computational Biology
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Computational models for prediction of m6A sites using deep learning.

Methods (San Diego, Calif.) Apr 21, 2025
RNA modifications play a crucial role in enhancing the structural and functional diversity of RNA molecules and regulating various stages of the RNA life cycle. Among these modifications, N6-Methyladenosine (m6A) is the most common internal modificat...
Humans RNA, Messenger Computer Simulation Adenosine Deep Learning RNA Processing, Post-Transcriptional Computational Biology
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ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites.

Journal of molecular biology Feb 1, 2025
RNA modifications are known to play a critical role in gene regulation and cellular processes. Specifically, N4-acetylcytidine (ac4C) modification has emerged as a significant marker involved in mRNA translation efficiency, stability, and various dis...
Humans RNA Processing, Post-Transcriptional Computational Biology Neural Networks, Computer RNA Cytidine Deep Learning
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Caps-ac4C: An effective computational framework for identifying N4-acetylcytidine sites in human mRNA based on deep learning.

Journal of molecular biology Jan 28, 2025
N4-acetylcytidine (ac4C) is a crucial post-transcriptional modification in human mRNA, involving the acetylation of the nitrogen atom at the fourth position of cytidine. This modification, catalyzed by N-acetyltransferases such as NAT10, is primarily...
Humans Cytidine RNA Processing, Post-Transcriptional Deep Learning Computational Biology RNA, Messenger
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Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data.

Methods (San Diego, Calif.) May 18, 2024
With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devot...
Nanopore Sequencing RNA Methylation HEK293 Cells Adenosine Humans RNA Processing, Post-Transcriptional RNA Machine Learning Algorithms Methylation Sequence Analysis, RNA Workflow
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Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.

RNA biology May 17, 2024
2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping a...
Humans Methylation RNA, Messenger HEK293 Cells Machine Learning Sequence Analysis, RNA Software Nanopores Transcriptome HeLa Cells RNA Processing, Post-Transcriptional Nanopore Sequencing Computational Biology
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MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

Methods (San Diego, Calif.) May 8, 2024
RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated w...
5-Methylcytosine RNA Processing, Post-Transcriptional Computational Biology RNA Humans Machine Learning Algorithms
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BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities.

The Journal of biological chemistry Mar 5, 2024
RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding R...
RNA Processing, Post-Transcriptional Support Vector Machine Nucleotides Computational Biology RNA Sequence Analysis, RNA
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Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications.

Nature communications Jun 29, 2021
Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of...
Neural Networks, Computer Computational Biology DNA Methylation Humans RNA Base Sequence RNA Processing, Post-Transcriptional
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iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm.

Genes May 9, 2020
One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequen...
RNA, Fungal ROC Curve Methylation Adenosine Algorithms Computational Biology Saccharomyces cerevisiae Genome, Fungal Deep Learning Datasets as Topic RNA Processing, Post-Transcriptional
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