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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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Related Experiment Video

Updated: Jul 16, 2025

Visualizing and Tracking Endogenous mRNAs in Live Drosophila melanogaster Egg Chambers
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NN-RNALoc: Neural network-based model for prediction of mRNA sub-cellular localization using distance-based

Negin Sadat Babaiha1,2, Rosa Aghdam3,4, Shokoofeh Ghiam3

  • 1Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.

Plos One
|September 14, 2023
PubMed
Summary
This summary is machine-generated.

NN-RNALoc predicts messenger RNA (mRNA) cellular locations using a novel neural network approach. This method enhances accuracy and efficiency in RNA localization prediction, crucial for understanding gene expression and disease.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Messenger RNA (mRNA) localization is vital for gene expression regulation and protein targeting.
  • Aberrant mRNA localization is implicated in neuromuscular diseases and cancer.
  • Accurate prediction of mRNA cellular location is essential for biological research.

Purpose of the Study:

  • To introduce NN-RNALoc, a novel neural network-based method for predicting mRNA cellular localization.
  • To develop efficient and accurate sequence representation for mRNA.
  • To integrate protein-protein interaction data for improved prediction.

Main Methods:

  • Developed a distance-based subsequence profile for efficient RNA sequence representation.
  • Integrated protein-protein interaction data with novel mRNA sequence features.
  • Utilized a neural network architecture for predicting mRNA localization.

Main Results:

  • NN-RNALoc demonstrated superior accuracy compared to existing predictive models on benchmark datasets (CeFra-Seq and RNALocate).
  • The method significantly reduced computation time compared to previous approaches.
  • Achieved higher accuracy than established methods including mRNALoc, RNATracker, and DNN5-mer.

Conclusions:

  • NN-RNALoc offers a more accurate and computationally efficient solution for predicting mRNA cellular localization.
  • The integration of novel sequence features and protein interaction data enhances predictive performance.
  • This tool has implications for understanding gene regulation and disease mechanisms related to RNA localization.