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.