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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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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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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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The nucleolus is the most prominent substructure of the nucleus. When it was first discovered, it was considered to be an isolated organelle that forms fibrils and granules. In 1931, the relationship between the nucleolus and chromosomes was first described by Heitz. He observed that the appearance and size of nucleolus varies depending on the stage of the cell cycle. He also noticed constricted regions on different chromosomes clustered together at definite cell cycle stages. These regions,...
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lncRNA localization and feature interpretability analysis.

Jing Li1,2, Ying Ju3, Quan Zou4

  • 1Department of Microbiology, University of Hong Kong, Hong Kong, China.

Molecular Therapy. Nucleic Acids
|February 10, 2025
PubMed
Summary
This summary is machine-generated.

We developed LncDNN, a novel computational model to accurately predict the subcellular localization of long non-coding RNAs (lncRNAs) in the nucleolus and nucleoplasm, enhancing our understanding of their biological roles.

Keywords:
MT: BioinformaticsSHAP analysisfeature selectionlncRNAsmachine learningnucleolusnucleoplasmsubcellular localization

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Subcellular localization of biomolecules, including long non-coding RNAs (lncRNAs), is critical for understanding cellular functions and regulatory mechanisms.
  • lncRNAs play diverse roles in cellular processes, and their specific locations within compartments like the nucleolus and nucleoplasm offer insights into their functions and disease relevance.

Purpose of the Study:

  • To develop and validate a computational model, LncDNN, for accurate prediction of lncRNA localization within the nucleolus and nucleoplasm.
  • To enhance the interpretability of lncRNA localization prediction by analyzing key features influencing the model.

Main Methods:

  • Development of the LncDNN model utilizing three distinct encoding schemes.
  • Application of Shapley Additive Explanations (SHAP) for feature analysis and selection to ensure model interpretability.
  • Comparative performance evaluation against existing models for lncRNA localization prediction.

Main Results:

  • LncDNN demonstrated superior accuracy in predicting lncRNA localization compared to other existing models.
  • Feature analysis using SHAP provided insights into the key sequence and structural determinants of lncRNA localization.
  • The model's effectiveness was validated for identifying lncRNA localization in the nucleolus and nucleoplasm.

Conclusions:

  • LncDNN is a highly accurate and interpretable tool for predicting lncRNA subcellular localization.
  • This model facilitates a deeper understanding of lncRNA functions and their involvement in biological processes and diseases.
  • The findings contribute to advancing research in lncRNA biology and its implications in health and disease.