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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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Modification of secretory and transmembrane proteins entering the rough ER begins in the ER lumen. These modifications aid in protein folding and stabilize the acquired tertiary structure. Protein modifications in the rough ER co-occur at different stages of protein folding.
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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
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Updated: Jan 4, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning.

Ping Ping Sun1, Yong Bing Chen1, Bo Liu2

  • 1School of Information Science and Technology, Northeast Normal University, Changchun, China.

Mathematical Biosciences and Engineering : MBE
|November 9, 2019
PubMed
Summary
This summary is machine-generated.

DeepMRMP is a new deep learning tool that accurately predicts multiple RNA modification sites. It uses bidirectional Gated Recurrent Unit (BGRU) and transfer learning to identify N1-methyladenosine (m1A), pseudouridine (Ψ), and 5-methylcytosine (m5C) sites.

Keywords:
5-methylcytosineN1-methyladenosinebidirectional gated recurrent unitmodificationpseudouridinetransfer learning

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • RNA modifications are crucial for regulating biological processes.
  • Predicting RNA modification sites is vital for understanding cellular functions.
  • Traditional methods struggle with feature engineering for diverse RNA properties.

Purpose of the Study:

  • To develop a deep learning-based predictor for multiple RNA modification sites.
  • To overcome limitations of traditional machine learning in feature selection.
  • To create a reliable computational tool for RNA modification site identification.

Main Methods:

  • Developed DeepMRMP (Multiple Types RNA Modification Sites Predictor).
  • Employed bidirectional Gated Recurrent Unit (BGRU) and transfer learning.
  • Utilized multiple RNA modification datasets and their correlations.

Main Results:

  • DeepMRMP effectively predicts multiple RNA modification sites.
  • The method leverages deep learning for optimal feature pattern detection.
  • Demonstrated reliability across H. sapiens, M. musculus, and S. cerevisiae RNA sequences.

Conclusions:

  • DeepMRMP is a robust computational tool for RNA modification site prediction.
  • The approach enhances understanding of N1-methyladenosine (m1A), pseudouridine (Ψ), and 5-methylcytosine (m5C) modifications.
  • Deep learning offers advantages over traditional methods in this field.