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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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SPMLMI: predicting lncRNA-miRNA interactions in humans using a structural perturbation method.

Mingmin Xu1, Yuanyuan Chen2, Wei Lu3

  • 1College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China.

Peerj
|May 31, 2021
PubMed
Summary

Predicting long non-coding RNA (lncRNA)-microRNA (miRNA) interactions is crucial for understanding RNA functions. A new bilayer network framework, SPMLMI, improves prediction accuracy and generalizability for these vital ncRNA interactions.

Keywords:
Bilayer networkPrediction algorithmStructural consistencyStructural perturbationlncRNA–miRNA interaction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are key regulators in biological processes.
  • Understanding lncRNA-miRNA interactions is essential for deciphering ncRNA functions.
  • Current prediction methods for lncRNA-miRNA interactions have limitations in accuracy.

Purpose of the Study:

  • To develop a novel computational framework for accurately predicting lncRNA-miRNA interactions.
  • To leverage structural consistency in a bilayer network for enhanced prediction.
  • To improve upon existing methods for identifying functional ncRNA interactions.

Main Methods:

  • Constructed a bilayer network integrating known lncRNA-miRNA interactions, lncRNA similarity, and miRNA similarity.
  • Employed the structural perturbation method to propose the SPMLMI framework.
  • Validated the model using three real-world datasets with 5-fold cross-validation.

Main Results:

  • The bilayer network demonstrated higher structural consistency than single networks.
  • SPMLMI achieved high prediction accuracy, with AUCs of 0.9512 ± 0.0034, 0.8767 ± 0.0033, and 0.8653 ± 0.0021.
  • SPMLMI exhibited superior generalizability compared to established prediction methods.
  • Case studies confirmed the method's effectiveness in identifying novel interactions.

Conclusions:

  • The SPMLMI framework offers a feasible and effective approach for predicting lncRNA-miRNA interactions.
  • Bilayer network construction enhances the prediction of these crucial RNA interactions.
  • This method aids in identifying novel ncRNA interactions relevant to complex biological processes.