Journal of Mathematical Biology

, Volume 46, Issue 6, pp 479-503

Words in DNA sequences: some case studies based on their frequency statistics

  • Srabashi BasuAffiliated withTheoretical Statistics and Mathematics unit, Indian Statistical Institute, 203 B.T. Road, Calcutta 700108, India. e-mail: srabashi@isical.ac.in; probal@isical.ac.in
  • , Debi Prosad BurmaAffiliated withMolecular Biology Unit, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, India
  • , Probal ChaudhuriAffiliated withTheoretical Statistics and Mathematics unit, Indian Statistical Institute, 203 B.T. Road, Calcutta 700108, India. e-mail: srabashi@isical.ac.in; probal@isical.ac.in

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Abstract.

 One of the critical requirements of data analysis involving large DNA sequences is an effective statistical summarization of those sequences. In this article DNA sequences have been analyzed based on word frequencies. Our analysis focuses on the detection of structural signature of a genome reflected in word frequencies and identification of phylogenetic relationships among different species reflected in the variation of word distributions in their DNA sequences. We have carried out a statistical study of the complete genome of baker's yeast, of various ribosomal RNA sequences from different prokaryotic and eukaryotic organisms and of the full genomes of some bacteriophages. Our exploratory analysis amply demonstrates the usefulness of DNA word frequencies in reducing the dimensionality of large sequences while retaining some of the structural information there that can have biological significance. Some conceptual issues that arise in course of our investigation have been addressed. A few interesting problems related to the statistics of DNA words have been pointed out with some indication of their possible solutions. The work has been partially motivated by the fact that sequence alignment and homology techniques that are quite popular for comparing and analyzing relatively smaller DNA sequences of nearly equal sizes are not applicable to data consisting of large sequences with widely varying sizes, which may contain segments with unknown or no biological functions, and consequently their comparison through functional homology is either impossible or extremely difficult.