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Journal of Applied Mathematics
Volume 2014, Article ID 176943, 9 pages
http://dx.doi.org/10.1155/2014/176943
Research Article

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

Bo Chen1,2 and Ping Ji3

1College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
2Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou 350116, China
3Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Received 19 April 2014; Revised 20 July 2014; Accepted 25 July 2014; Published 12 August 2014

Academic Editor: Ning Hu

Copyright © 2014 Bo Chen and Ping Ji. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.