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Related Concept Videos

Riboswitches01:56

Riboswitches

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Riboswitches are non-coding mRNA domains that regulate the transcription and translation of downstream genes without the help of proteins. Riboswitches bind directly to a metabolite and can form unique stem-loop or hairpin structures in response to the amount of the metabolite present. They have two distinct regions – a metabolite-binding aptamer and an expression platform.
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Transcriptional Regulation: Riboswitches01:23

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Riboswitches are RNA elements that regulate gene expression by altering their secondary structures in response to specific effector molecules. These elements, located in the leader regions of certain mRNAs, act as transcriptional regulators by toggling between alternative conformations to control downstream gene expression. Riboswitch-mediated regulation is a precise mechanism for modulating biosynthetic pathways, as exemplified by the riboflavin biosynthesis pathway in Bacillus...
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Ribosome Profiling02:24

Ribosome Profiling

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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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Types of RNA01:23

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Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
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Translational Regulation01:29

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Translational regulation in prokaryotes ensures efficient protein synthesis by controlling ribosome access to mRNA. This regulation is mediated by secondary RNA structures, including translational riboswitches, RNA thermometers, and small RNAs (sRNAs), which respond to intracellular and environmental signals to modulate gene expression.Translational RiboswitchesRiboswitches in the leader region of mRNAs can regulate translation by altering the accessibility of the Shine-Dalgarno (SD) sequence,...
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Ribozymes02:47

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The term ribozyme is used for RNA that can act as an enzyme. Ribozymes are mainly found in selected viruses, bacteria, plant organelles, and lower eukaryotes. Ribozymes were first discovered in 1982 when Tom Cech’s laboratory observed Group I introns acting as enzymes. This was shortly followed by the discovery of another ribozyme, Ribonulcease P, by Sid Altman’s laboratory. Both Cech and Altman received the Nobel Prize in chemistry in 1989 for their work on ribozymes.
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Classification of Riboswitch Families Using Block Location-Based Feature Extraction (BLBFE) Method.

Faegheh Golabi1,2, Mousa Shamsi1, Mohammad Hosein Sedaaghi3

  • 1Genomic Signal Processing Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.

Advanced Pharmaceutical Bulletin
|February 1, 2020
PubMed
Summary

A novel bioinformatics method, block location-based feature extraction (BLBFE), effectively classifies riboswitches. This approach achieves high accuracy and sensitivity, aiding in the study of these gene-regulating non-coding RNA sequences.

Keywords:
BLBFEBlock location-based feature extractionClassificationNon-coding RNAPerformance measuresRiboswitchSequential blocks

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Riboswitches are non-coding RNA elements regulating gene expression.
  • Their interaction with antibiotics is a growing area of research.
  • Development of bioinformatics tools for riboswitch analysis is crucial.

Purpose of the Study:

  • To develop and employ a novel block location-based feature extraction (BLBFE) method.
  • To classify seven specific riboswitch families.
  • To evaluate the performance of BLBFE in conjunction with various classifiers.

Main Methods:

  • Utilized a sequential block finding (SBF) algorithm to identify family-specific blocks without alignment.
  • Applied the BLBFE method for feature extraction from identified blocks.
  • Classified riboswitches using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Decision Tree, and K-Nearest Neighbors (KNN).

Main Results:

  • The BLBFE method achieved an average correct classification rate (CCR) of 87.87% for riboswitches.
  • Average accuracies ranged from 93.98% to 96.1% across classifiers.
  • Average sensitivities, specificities, and f-scores demonstrated high classification performance.

Conclusions:

  • The proposed BLBFE method is effective for classifying and discriminating riboswitch families.
  • The method yields high values for CCR, accuracy, sensitivity, specificity, and f-score.
  • BLBFE offers a valuable tool for advancing riboswitch studies.