nDNA-prot: identification of DNA-binding proteins based on unbalanced classification

BMC Bioinformatics

Quan Zou³  Li Guo²  Yunfeng Wu³  Xiangxiang Zeng³  Dapeng Li¹  Li Song³ 

[1]Department of Internal Medicine-Oncology, The Fourth Hospital in Qinhuangdao, Qinhuangdao, Hebei 066000, China

[2]Department of Epidemiology and Biostatistics and Ministry of Education Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 210029, China

[3]School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China

关键词: Bioinformatics;    Unbalanced dataset;    Ensemble classifier;    DNA-binding protein;   

Others  :  1086103
DOI  :  10.1186/1471-2105-15-298

 received in 2014-06-01, accepted in 2014-09-03,  发布年份 2014

【 摘 要 】

Background

DNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods.

Results

In this paper, we explored the practicality of modelling DNA binding identification and simultaneously employed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot.

Conclusions

Our method can help to accurately identify DNA-binding proteins, and the web server is accessible at http://datamining.xmu.edu.cn/~songli/nDNA webcite. In addition, we also predicted possible DNA-binding protein sequences in all of the sequences from the UniProtKB/Swiss-Prot database.

【 授权许可】

   
2014 Song et al.; licensee BioMed Central Ltd.

【 图 表 】

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