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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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A Protocol for Computer-Based Protein Structure and Function Prediction
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RNA Function Prediction.

Yongsheng Li1, Juan Xu1, Tingting Shao1

  • 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.

Methods in Molecular Biology (Clifton, N.J.)
|October 8, 2017
PubMed
Summary

Discovering the functions of noncoding RNAs (ncRNAs), like microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), is crucial. This chapter reviews computational methods to predict ncRNA functions and identify their targets.

Keywords:
Co-epigenetic modificationCo-expressionCompeting endogenous RNAGenomic co-locationGuilt-by-association principleRNA functionTarget geneslncRNA function predictionmRNA function predictionmiRNA function prediction

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • A significant portion of eukaryotic genomes are transcribed into noncoding RNAs (ncRNAs).
  • Regulatory ncRNAs, particularly microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), are of great research interest.
  • The precise identification of ncRNA targets remains a major challenge, hindering functional characterization.

Purpose of the Study:

  • To summarize recent advancements in noncoding RNA (ncRNA) research and their functions.
  • To review state-of-the-art computational methods for predicting RNA functions.
  • To highlight the importance of these computational approaches for future ncRNA studies.

Main Methods:

  • Review of current literature on ncRNA research and function.
  • Categorization of computational methods for RNA function prediction.
  • Discussion of miRNA function prediction via target genes.
  • Explanation of lncRNA function prediction using the guilt-by-association principle.
  • Overview of RNA function prediction based on competing endogenous RNA interactions.

Main Results:

  • Noncoding RNAs (ncRNAs) constitute a substantial part of eukaryotic genomes.
  • Computational methods offer diverse approaches to predict ncRNA functions.
  • Three main categories of computational methods are identified: target gene-based (miRNA), guilt-by-association (lncRNA), and competing RNA-based.

Conclusions:

  • Accurate target identification is key to understanding ncRNA functions.
  • Computational methods provide valuable insights into ncRNA mechanisms.
  • These predictive techniques are essential for advancing future ncRNA functional studies.