A Novel Adaptive Filtering Approach for Genomic Signal Processing
With the enormous amount of biological data that is available in the public domain, signal processing plays an important role in genomic and proteomic data processing. Digital filters have been applied to predict genes and proteins, but the filters need to be redesigned when the periodic behavior or characteristic frequency is changed. In this paper, we propose a novel approach based on adaptive filtering theory which can identify genes or proteins in a unified framework. At first, we review the popular Voss representation which maps the alphabetic DNA sequence into the digital series. Secondly, a novel adaptive filtering scheme for genomic signal processing with the periodical behavior of biological sequences is proposed, which can analyze and predict the biological function regions that we are interested in. Thirdly, the adaptive filtering approach is applied to identify the exons in a DNA sequence according to period-3 property of protein coding regions. The prediction curves of the exons are obtained with the Least Mean Square (LMS), the Recursive Least Squares (RLS) and the Kalman filtering algorithm. It is shown that the proposed method is useful for genomic signal processing.
genomic signal processing Voss mapping period property protein coding region adaptive filter
Baoshan Ma Dongdong Qu Yi-Sheng Zhu
College of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, 116026, China
国际会议
2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)
北京
英文
1805-1808
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）