An Exploration of the Triplet Periodicity in Nucleotide Sequences with a Mature Self-Adaptive Spectral Rotation Approach

Previously, for predicting coding regions in nucleotide sequences, a self-adaptive spectral rotation (SASR) method has been developed, based on a universal statistical feature of the coding regions, named triplet periodicity (TP). It outputs a random walk, that is, TP walk, in the complex plane for the query sequence. Each step in the walk is corresponding to a position in the sequence and generated from a long-term statistic of the TP in the sequence. The coding regions (TP intensive) are then visually discriminated from the noncoding ones (without TP), in the TP walk. In this paper, the behaviors of the walks for random nucleotide sequences are further investigated qualitatively. A slightly leftward trend (a negative noise) in such walks is observed, which is not reported in the previous SASR literatures. An improved SASR, named the mature SASR, is proposed, in order to eliminate the noise and correct the TP walks. Furthermore, a potential sequence pattern opposite to the TP persistent pattern, that is, the TP antipersistent pattern, is explored. The applications of the algorithms on simulated datasets show their capabilities in detecting such a potential sequence pattern.