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K Orieux

Publications and source records attributed to K Orieux.

2 recordsLinked to original sources

Identification and simulation of new non-random statistical properties common to different populations of eukaryotic non-coding genes.

The autocorrelation function analysing the occurrence probability of the i-motif YRY(N)iYRY in genes allows the identification of mainly two periodicities modulo 2, 3 and the preferential occurrence of the motif YRY(N)6YRY (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 YRYRYR, YRYYRY and YRY(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 RRR(N)iRRR and YYY(N)iYYY simultaneously identify three new non-random genetic statistical properties: a short linear decrease, local maxima for i identical to 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' regions, introns and 3' regions (see section 2). These three non-random properties can also be simulated by an independent mixing of the four oligonucleotides R8, Y8, RRRYRYRRR, YYYRYRYYY and large alternating R/Y series. The short linear decrease is a result of R8 and Y8, the local maxima for i identical to 3[6], of RRRYRYRRR and YYYRYRYYY, 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.

Animals↗

Analysis of gene evolution: the software AGE.

The software AGE (Analysis of Gene Evolution) has been developed both to study a genetic reality, i.e. the identification of statistical properties in genes (e.g. periodicities), and to simulate this observed genetic reality, by models of molecular evolution. AGE has two types of models: (i) models of sequence creation from oligonucleotides: concatenation model in series of an oligonucleotide, independent (or Markov) mixing model of oligonucleotides according to given probabilities (or a Markov matrix); (ii) models of sequence evolution from created sequences: insertion/deletion process of (mono,di,tri)nucleotides, base mutation process. The study of a reality and the development of simulation models are based on several new algorithms: approximated simulation and exact calculus to compute various autocorrelation functions, Fourier transformation of autocorrelation curves, recognition of a curve form, etc. AGE is implemented on IBM or compatible microcomputers and can be used by biologists without any computer knowledge to identify statistical properties in their newly determined DNA sequence and to explain them by models of molecular evolution.

Algorithms↗