iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition
Metrics: PDF 1578 views | HTML 1642 views | ?
Xuan Xiao1,2,5, Han-Xiao Ye1, Zi Liu3, Jian-Hua Jia1, Kuo-Chen Chou4,5
1Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, 333403, China
2Information School, ZheJiang Textile and Fashion College, NingBo, 315211, China
3School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
4Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, 21589, Saudi Arabia
5Gordon Life Science Institute, Boston, Massachusetts, 02478, USA
Keywords: origin of replication, position-specific dinucleotide propensity, general pseudo nucleotide composition, random forest, iROS-gPseKNC
Received: March 02, 2016 Accepted: April 09, 2016 Published: April 27, 2016
DNA replication, occurring in all living organisms and being the basis for biological inheritance, is the process of producing two identical replicas from one original DNA molecule. To in-depth understand such an important biological process and use it for developing new strategy against genetics diseases, the knowledge of duplication origin sites in DNA is indispensible. With the explosive growth of DNA sequences emerging in the postgenomic age, it is highly desired to develop high throughput tools to identify these regions purely based on the sequence information alone. In this paper, by incorporating the dinucleotide position-specific propensity information into the general pseudo nucleotide composition and using the random forest classifier, a new predictor called iROS-gPseKNC was proposed. Rigorously cross–validations have indicated that the proposed predictor is significantly better than the best existing method in sensitivity, specificity, overall accuracy, and stability. Furthermore, a user-friendly web-server for iROS-gPseKNC has been established at http://www.jci-bioinfo.cn/iROS-gPseKNC, by which users can easily get their desired results without the need to bother the complicated mathematics, which were presented just for the integrity of the methodology itself.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.