AIMC Topic: RNA Processing, Post-Transcriptional

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TransCNN: A novel architecture combining transformer and TextCNN for detecting N4-acetylcytidine sites in human mRNA.

Analytical biochemistry Apr 29, 2025
N4-acetylcytidine (ac4C), a pivotal post-transcriptional RNA modification, is central to understanding transcriptional regulation and diverse biological processes. As a key determinant of RNA structural stability and functional regulation, ac4C has b...
RNA, Messenger Cytidine RNA Processing, Post-Transcriptional Humans Neural Networks, Computer
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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...
Computational Biology RNA Cytidine Humans Neural Networks, Computer Deep Learning RNA Processing, Post-Transcriptional
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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 Deep Learning Computational Biology RNA, Messenger Cytidine RNA Processing, Post-Transcriptional
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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...
RNA Methylation HEK293 Cells Algorithms Sequence Analysis, RNA Workflow RNA Methylation Adenosine RNA Processing, Post-Transcriptional Nanopore Sequencing Humans Machine Learning
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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...
Nanopore Sequencing Nanopores RNA Processing, Post-Transcriptional Computational Biology RNA, Messenger Sequence Analysis, RNA HeLa Cells Methylation HEK293 Cells Machine Learning Software Transcriptome Humans
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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...
Humans Machine Learning RNA 5-Methylcytosine RNA Processing, Post-Transcriptional Algorithms Computational Biology
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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...
Support Vector Machine Nucleotides RNA RNA Processing, Post-Transcriptional Computational Biology 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...
Humans Neural Networks, Computer Computational Biology DNA Methylation 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 Adenosine Genome, Fungal RNA Processing, Post-Transcriptional Methylation ROC Curve Saccharomyces cerevisiae Algorithms Computational Biology Deep Learning Datasets as Topic
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Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

Nature genetics May 27, 2019
We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,7...
Transcription, Genetic Genomics RNA Processing, Post-Transcriptional Genetic Predisposition to Disease Phenotype RNA, Untranslated Alleles Genetic Association Studies Genes, Reporter Autism Spectrum Disorder Computational Biology Gene Expression Regulation Humans Gene Expression Genome, Human Deep Learning Algorithms Mutation
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