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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
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Information theoretic perspective on genome clustering.

Alaguraj Veluchamy1,2, Preeti Mehta1, K V Srividhya1

  • 1Centre of Excellence in Bioinformatics, School of Biotechnology, Madurai Kamaraj University, Madurai 625021, India.

Saudi Journal of Biological Sciences
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

Information theory reveals genome clustering and evolution of messaging strategies. Analysis of 937 prokaryotic and 238 eukaryotic genomes shows information content (Id) values can group chromosomes, highlighting genomic diversity and conservation.

Keywords:
Genome arrangementGenome clusteringGenome evolutionInformation theoryNucleotide distributionShannon redundancy

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Area of Science:

  • Genomics
  • Information Theory
  • Bioinformatics

Background:

  • Shannon's information theory provides a framework for understanding information storage and processing in biological sequences.
  • Genomic analysis requires methods to interpret the vast amount of sequence data.

Purpose of the Study:

  • To analyze the information content of prokaryotic and eukaryotic genomes using an information theoretic approach.
  • To investigate the potential of information content values for clustering genomes and understanding genomic evolution.

Main Methods:

  • Information content (Id) values were calculated for 937 prokaryotic genomes and 238 eukaryotic chromosomes.
  • A distance-based method was used to cluster the chromosomes based on their Id values.
  • Chargaff's second parity rule was assessed for compositional self-complementarity.

Main Results:

  • All analyzed genomes, except *Candidatus* Hodgkinia cicadicola, adhere to Chargaff's second parity rule.
  • High information content was observed in *Plasmodium falciparum* chromosomes and specific prokaryotic genomes (*Buchnera aphidicola*, *Candidatus* Carsonella ruddii).
  • Prokaryotic and eukaryotic genomes show minimal deviation from an equiprobable and random situation despite variations in size and composition.

Conclusions:

  • Information content analysis offers insights into genome clustering and the evolution of genomic messaging strategies.
  • Eukaryotic chromosomes from the same organism exhibit similar informational constraints, aiding in clustering.
  • A standalone tool for whole-genome analysis based on this information theory algorithm is available.