The autocorrelation function analysing the occurrence probability of the (i)-motif (Y R Y(N){i} Y R Y) in genes allows the identification of mainly two periodicities modulo 2, 3 and the preferential occurrence of the motif (Y R Y(N){6} Y R Y(R=) purine (=) adenine or guanine, (Y=) pyrimidine (=) cytosine or thymine, (N=R) or (Y) ). These non-random genetic statistical properties can be simulated by an independent mixing of the three oligonucleotides (Y R Y R Y R, Y R Y Y R Y) and (Y R Y(N)_{6}) (Arquès & Michel, 1990b). The problem investigated in this study is whether new properties can be identified in genes with other autocorrelation functions and also simulated with an oligonucleotide mixing model.
The two autocorrelation functions analysing the occurrence probability of the (i) motifs (R R R(N){i} R R R) and (Y Y Y(N){i} Y Y Y) simultaneously identify three new nonrandom genetic statistical properties: a short linear decrease, local maxima for (i \equiv 3[6]) ((i=3,9), etc) and a large exponential decrease. Furthermore, these properties are common to three different populations of eukaryotic non-coding genes: (5^{\prime}) regions, introns and (3^{\prime}) regions (see section 2).
These three non-random properties can also be simulated by an independent mixing of the four oligonucleotides (R^{8}, Y^{8}, R R R Y R Y R R R, Y Y Y R Y R Y Y Y) and large alternating (R / Y) series. The short linear decrease is a result of (R^{8}) and (Y^{8}), the local maxima for (i \equiv 3[6]), of (R R R Y R Y R R) and (Y Y Y R Y R Y Y Y), and the large exponential decrease, of large alternating (R / Y) series (section 3).
The biological meaning of these results and their relation to the previous oligonucleotide mixing model are presented in the Discussion.