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Related Concept Videos

Phosphoinositides and PIPs01:42

Phosphoinositides and PIPs

Phosphoinositides are a group of phospholipids containing a glycerol backbone with two fatty acid chains and a phosphate attached to a myoinositol sugar ring. The inositol head group extends into the cytoplasm, where it is modified by adding phosphate groups to form phosphatidylinositol phosphates or PIPs.
Different phosphoinositides are synthesized and recruited on the cytosolic face of the plasma membrane. The localization of specific phosphoinositides concentrated in separate membrane...

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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
09:40

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Published on: June 11, 2015

PIPS: pathogenicity island prediction software.

Siomar C Soares1, Vinícius A C Abreu, Rommel T J Ramos

  • 1Department of General Biology, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.

Plos One
|February 23, 2012
PubMed
Summary
This summary is machine-generated.

Pathogenicity islands are key to bacterial adaptation. A new tool, PIPS, improves their detection by integrating multiple genomic features, offering higher accuracy for identifying virulence genes in pathogens.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Bacterial adaptability to hosts is driven by genomic plasticity, often enhanced by horizontal gene transfer.
  • Pathogenicity islands (PAIs) are large, horizontally acquired DNA regions containing virulence genes crucial for bacterial pathogenesis.
  • Current in silico methods for PAI identification rely on limited features like codon usage or flanking regions, with varying accuracy.

Purpose of the Study:

  • To develop a novel computational tool, Pathogenicity Island Prediction Software (PIPS), for more accurate PAI identification.
  • To enhance PAI detection by integrating multiple characteristic features in a comprehensive manner.

Main Methods:

  • Developed PIPS, a software suite utilizing multiple features for PAI prediction.
  • Compared PIPS performance against existing computational techniques for PAI identification.
  • Applied PIPS to analyze the genome of the veterinary pathogen Corynebacterium pseudotuberculosis.

Main Results:

  • PIPS demonstrated superior accuracy in predicting pathogenicity islands compared to existing software.
  • The study identified seven putative pathogenicity islands in Corynebacterium pseudotuberculosis using PIPS.
  • The integrative approach of PIPS allows for more robust detection of horizontally transferred virulence gene clusters.

Conclusions:

  • PIPS represents a significant advancement in the accurate and efficient identification of pathogenicity islands.
  • The tool aids in understanding the genetic basis of bacterial virulence and host adaptation.
  • Further application of PIPS can reveal novel insights into the pathogenicity mechanisms of various bacterial species.