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Related Concept Videos

Prokaryotic Gene Structure and Organization01:28

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Prokaryotic genomes exhibit a streamlined organization of coding and non-coding regions essential for gene expression and protein synthesis. While coding regions contain the genetic instructions for proteins or functional RNAs, non-coding regions regulate the precise transcription and translation of these genes.Coding Regions: Proteins and RNAsThe primary coding regions, known as structural genes, include sequences transcribed into messenger RNA (mRNA) and ultimately translated into...
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The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
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The organization of prokaryotic genes in their genome is notably different from that of eukaryotes. Prokaryotic genes are organized, such that the genes for proteins involved in the same biochemical process or function are located together in groups. This group of genes, along with their regulatory elements, are collectively known as an operon. The functional genes in an operon are transcribed together to give a single strand of mRNA known as polycistronic mRNA.
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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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Updated: May 2, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.

Kai Song1, Tuopong Tong, Fang Wu

  • 1School of Chemical Engineering and Technology, Tianjin University, 92 Weijin Road, Nankai district, Tianjin, 300072, China. ksong@tju.edu.cn tptong@tju.edu.cn wufang@tju.edu.cn.

Integrative Biology : Quantitative Biosciences From Nano to Macro
|March 8, 2014
PubMed
Summary

A new method, ZUPLS, accurately and rapidly identifies prokaryotic essential genes using only sequence data, simplifying the process for new species. It outperforms existing approaches, demonstrating broad applicability across diverse bacterial types.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Identifying essential genes is crucial for understanding prokaryotic biology and developing targeted therapies.
  • Existing methods often rely on complex biological network knowledge, limiting their application to well-studied organisms.

Purpose of the Study:

  • To develop an effective and efficient method for prokaryotic essential gene identification.
  • To create a tool that relies solely on sequence-based features, reducing the need for prior biological network data.

Main Methods:

  • Developed ZUPLS, a linear method utilizing Z-curve and other sequence-derived features.
  • Integrated an uninformative variable elimination tool for automatic feature selection within a partial least squares classifier.
  • Tested ZUPLS on 12 diverse prokaryotes, using E. coli and B. subtilis as training sets for cross-organism predictions.

Main Results:

  • ZUPLS achieved high Area Under the Curve (AUC) scores, ranging from 0.8042 to 0.9319 (E. coli training) and 0.8111 to 0.9371 (B. subtilis training).
  • Demonstrated significant improvements over existing methods, with AUC score increases of up to 0.13 and average accuracy improvements of 14.7%.
  • Showcased reliable prediction capabilities for both Gram-positive and Gram-negative bacteria, irrespective of culture media richness.

Conclusions:

  • ZUPLS offers a simplified yet powerful approach to essential gene identification, particularly beneficial for newly sequenced prokaryotes.
  • The method's reliance on sequence features and automatic selection capabilities make it robust and broadly applicable.
  • ZUPLS represents a significant advancement in predicting essential genes, enhancing our understanding of prokaryotic biology.