A Modified Statistically Optimal Null Filter Method for Recognizing Protein-coding Regions  

作　　者：Lei Zhang Fengchun Tian Shiyuan Wang 

机构地区：[1]College of Communication Engineering,Chongqing University,Chongqing 400044,China [2]School of Electronic and Information Engineering,Southwest University,Chongqing 400715,China

出　　处：《Genomics, Proteomics & Bioinformatics》2012年第3期166-173,共8页基因组蛋白质组与生物信息学报（英文版）

基　　金：supported by the Fundamental Research Funds for the Central Universities (Grant No.CDJXS10160001);the Central University Postgradu-ate’ Science and Innovation Funds of China (Grant No.CDJXS12160005)

摘　　要：Computer-aided protein-coding gene prediction in uncharacterized genomic DNA sequences is one of the most important issues of bio- logical signal processing. A modified filter method based on a statistically optimal null filter （SONF） theory is proposed for recognizing protein-coding regions. The square deviation gain （SDG） between the input and output of the model is used to identify the coding regions. The effective SDG amplification model with Class I and Class II enhancement is designed to suppress the non-coding regions. Also, an evaluation algorithm has been used to compare the modified model with most gene prediction methods currently available in terms of sensitivity, specificity and precision. The performance for identification of protein-coding regions has been evaluated at the nucleotide level using benchmark datasets and 91.4%, 96%, 93.7% were obtained for sensitivity, specificity and precision, respectively. These results suggest that the proposed model is potentially useful in gene finding field, which can help recognize protein-coding regions with higher precision and speed than present algorithms.Computer-aided protein-coding gene prediction in uncharacterized genomic DNA sequences is one of the most important issues of bio- logical signal processing. A modified filter method based on a statistically optimal null filter （SONF） theory is proposed for recognizing protein-coding regions. The square deviation gain （SDG） between the input and output of the model is used to identify the coding regions. The effective SDG amplification model with Class I and Class II enhancement is designed to suppress the non-coding regions. Also, an evaluation algorithm has been used to compare the modified model with most gene prediction methods currently available in terms of sensitivity, specificity and precision. The performance for identification of protein-coding regions has been evaluated at the nucleotide level using benchmark datasets and 91.4%, 96%, 93.7% were obtained for sensitivity, specificity and precision, respectively. These results suggest that the proposed model is potentially useful in gene finding field, which can help recognize protein-coding regions with higher precision and speed than present algorithms.

关 键 词：Gene prediction Biological signal processing Protein-coding region Square deviation gain 

分 类 号：Q78[生物学—分子生物学] TN713[电子电信—电路与系统]