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 n...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.展开更多
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)
文摘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.
