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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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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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Author Spotlight: Decoding RNA Methylation's Role in Pancreatic Cancer - A Single-Base Resolution Study
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Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature

Sumaiya Noor1, Afshan Naseem2, Hamid Hussain Awan3

  • 1Business and Management Sciences Department, Purdue University, West Lafayette, IN, USA.

BMC Bioinformatics
|November 20, 2024
PubMed
Summary
This summary is machine-generated.

Deep-m5U accurately predicts RNA 5-methyluridine (m5U) modifications using a hybrid approach. This computational biology tool enhances m5U identification, offering higher accuracy than existing models for transcript and mRNA datasets.

Keywords:
Deep learningPseKNCRNA 5-methyluridineSHAPSequence-derived features

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Area of Science:

  • Computational biology
  • Molecular biology
  • Bioinformatics

Background:

  • RNA 5-methyluridine (m5U) modifications are critical for biological functions.
  • Accurate identification of m5U sites is essential in computational biology.
  • Existing methods for m5U prediction require enhancement.

Purpose of the Study:

  • To introduce Deep-m5U, a novel computational tool for predicting RNA m5U modifications.
  • To improve the accuracy and reliability of m5U site identification.
  • To provide a valuable resource for researchers in molecular biology and drug discovery.

Main Methods:

  • Utilized a hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence representation.
  • Employed the Shapley Additive exPlanations (SHAP) algorithm for feature selection.
  • Developed a deep neural network (DNN) classifier for prediction.

Main Results:

  • Deep-m5U achieved high accuracy on benchmark datasets: 91.47% (Full Transcript) and 95.86% (Mature mRNA) with 10-fold cross-validation.
  • Independent sample testing yielded accuracies of 92.94% (Full Transcript) and 95.17% (Mature mRNA).
  • Demonstrated superior performance over existing models, with accuracy improvements of up to 5.23% on training data and 3.95% on independent samples.

Conclusions:

  • Deep-m5U is a reliable and effective predictor for RNA m5U modifications.
  • The model offers significant accuracy improvements compared to current methods.
  • Deep-m5U serves as a valuable tool for scientific research and pharmaceutical development.