Binary auto-regressive geometric modelling in a DNA context

Abstract

Symbolic or categorical sequences occur in any contexts and can be characterized, for example, by integer-valued intersymbol distances or binary-valued indicator sequences. The analysis of these numerical sequences often sheds light on the properties of the original symbolic sequences. This work introduces new statistical tools for exploring auto-correlation structure in the indicator sequences, for the specific case of deoxyribonucleic acid (DNA) sequences. It is known that the probability distribution of internucleotide distances of DNA sequences deviates significantly from the distribution obtained by assuming independent random placement (i.e. the geometric distribution) and that the deviations can be used either to discriminate between species or to build phylogenetic trees. To investigate the extent to which auto-correlation structure explains these deviations, the 0–1 indicator sequence of each nucleotide (A, C, G and T) is endowed with a binary auto-regressive (AR) model of optimum order. The corresponding binary AR geometric distribution is derived analytically and compared with the observed internucleotide distance distribution by appropriate goodness-of-fit testing. Results in 34 mitochondrial DNA sequences show that the hypothesis of equal observed/expected frequencies is seldom rejected when a binary AR model is considered instead of independence (76/136 versus 125/136 rejections at the 1% level), in spite of chi-square testing tending to reject for large samples, regardless of how close observed/expected values are. Furthermore, binary AR structure also leads to a median discrepancy reduction of 90% for G, 80% for C, 60% for T and 30% for nucleotide A. Therefore, these models are useful to describe the dependences within a given nucleotide and encourage the development of a model-based framework to compact internucleotide distance information and to understand DNA differences among species further.