LAN低噪声 放大器

Prediction of Protein Coding Regions by Support Vector Machine GUO Shuo1,2 1. College of Information Engineering, Dalian Maritime University Dalian, China 2. College of Information Engineering, Shenyang Institute of Chemical Technology Shenyang, China guo_shuo1978@163.com

ZHU Yi-sheng College of Information Engineering, Dalian Maritime University Dalian, China yszhu@dlmu.edu.cn

Abstract—With the exponential growth of genomic sequences, there is an increasing demand to accurately identify protein coding regions from genomic sequences. Despite many progresses being made in the identification of protein coding regions by computational methods during recent years, the performances and efficiencies of the prediction methods still need to be improved. A novel method to predict the position of coding regions is proposed. First, a support vector machine is used as a classifier to recognize the first nucleotide of a codon in a coding region. Then, according to the difference of the time frequency characteristics of the output values of the classifier analyzed by Short Time Fourier Transform, the position of coding regions can be accurately determinate. The algorithm is not only can predict coding regions, but also can identify the first nucleotide of the codon in coding regions. This is very significant for accurate translation into a protein sequence. The simulation results show the proposed method is more effective for coding regions prediction than the existing coding region discovery tools.

Keywords-Coding Region in DNA Sequence; Codon; Support Vector Machine; Short Time Fourier Transform

I. INTRODUCTION An important goal in bioinformatics is to accurately annotate the genome sequence information within an acceptable timeframe. In most eukaryotic genes, there are introns, many of which interrupt an exon. During splicing, the introns, the non-coding regions of genes, are removed from the primary transcripts, and the exons, the coding regions, are joined to form a continuous sequence that specifies a functional polypeptide. An important part of gene prediction in eukaryotes is therefore to predict coding regions. An DNA sequence composed with four characters A,G,T,C, which denoted four nucleotides.The genetic code consists of 64 triplets of nucleotides. These triplets are called codons. With three exceptions, each codon encodes for one of the 20 amino acids used in the synthesis of proteins. That produces some redundancy in the code: most of the amino acids being encoded by more than one codon. Most of the exon-finding algorithms are based on statistics methods, neural networks [1, 2], hidden markov model (HMM) [3, 4]. But the large amount of DNA sequence data has brought great challenge to the computing

speed and prediction accuracy. Now signal processing approaches have been attracting significant attentions in genomic DNA research since the hidden periodicities and features cannot be revealed easily by conventional statistics methods. As examples, [5, 6] predict coding regions based on the fact that most of exon sequences have a 3-base periodicity, while intron sequences do not have this unique feature. However, current studies show many exons have no 3-base periodicity, especially for the short coding region sequences [7]. The signal processing approaches are only suitable for long exons prediction. Support Vector Machines(SVM) are powerful pattern recognition techniques that have been successfully applied to many machine learning tasks such as classification and regression[8~12]. They have outperformed many other machine learning methods. Applications of SVMs in bioinformatics include splice site prediction[13], promoter prediction[14], host-pathogen separation[15]. In this paper, SVM was applied to the protein coding region prediction. Then, the time-frequency characteristics of the output values of the SVM classification have been analyzed by short-time Fourier transform (STFT). Therefore the position of coding regions can be accurately identified.

II. BASIC PRINCIPLE OF SVM

Support vector machine (SVM) is a new machine learning method which is based on the statistical learning theory, the structural risk minimization principle, and small studying samples. It is a powerful tool for solving highly nonlinear classification, function estimation, and density estimation. We only provide a brief introduction of SVMs here. Suppose the given set of examples are Xi (i=1,…,N), with corresponding labels {1,1}iy∈+− (i=1,…,N), where -1 and