PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Vitale D., Kuruoglu E. E., Abul O. Long range cross correlations between nucleotide triplets in human chromosomes. In: BITS 2011 - Bioinformatics Italian Society VIII Annual Meeting (Pisa, Italy, 20-22 Giugno 2011). Abstract, vol. 1 pp. 159 - 159. F. Geraci, R. Marangoni, M. Pellegrini, M.E. Renda (eds.). Edizioni ETS, 2011.
 
 
Abstract
(English)
Long range autocorrelations of nucleotide pairs in human chromosomes have been studied widely in the literature. Long-range correlations differ from usual correlations in that they indicate underlying fractal nature. Despite the wide spread acceptance of this observation for nucleotide pairs, its biological significance has not been completely explored. Motivated by the fact that the basic unit for aminoacid synthesis are codons, composed of three nucleotides, in this paper, we calculate the correlations between pairs of nucleotide triplets. We utilise an approximation to the mutual information function suggested by Beier, which is approximately equal to correlation. We underline that we do not consider only autocorrelations but also crosscorrelations. We study the human choromosomes 22 and 23. We demonstrate that among 4096 such triplet pairs, 23 show long range dependence. Considering a Gaussian distribution for the crosscorrelation of such pairs, these 23 pairs stand out at 3 sigma distance from the mean. The list of these pairs are: AAA-AAT, AAA-ATA, AAA-ATT, AAA-TAA, AAA-TAT, AAA-TTT, AAA-ATA, AAT-ATT, AAT-TAT, AAT-TTT, ATA-ATT, ATA-TAA, ATA-TAT, ATA-TTA, ATA-TTT, ATT-TAA, ATT-TAT, ATT-TTT, TAA-TTT, TAA-TTA, TAT-TTT, TTA-TTT. The biological significance of this result is yet not clear and we believe that this conference would be an ideal platform for discussion on the interpretation of these observations.
Subject DNA
Long-range dependence
Cross-correlation between nucleotide triplets
J.3 LIFE AND MEDICAL SCIENCES. Biology and genetics
G.3 PROBABILITY AND STATISTICS. Correlation and regression analysis
92D20 Protein sequences, DNA sequences
62M10 Time series, auto-correlation, regression, etc.


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