Bin Liu1,2, Fan Weng1, De-Shuang Huang3, Kuo-Chen Chou2,4. 1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China. 2. Gordon Life Science Institute, Belmont, MA, USA. 3. Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China. 4. Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
Abstract
Motivation: DNA replication is the key of the genetic information transmission, and it is initiated from the replication origins. Identifying the replication origins is crucial for understanding the mechanism of DNA replication. Although several discriminative computational predictors were proposed to identify DNA replication origins of yeast species, they could only be used to identify very tiny parts (250 or 300 bp) of the replication origins. Besides, none of the existing predictors could successfully capture the 'GC asymmetry bias' of yeast species reported by experimental observations. Hence it would not be surprising why their power is so limited. To grasp the CG asymmetry feature and make the prediction able to cover the entire replication regions of yeast species, we develop a new predictor called 'iRO-3wPseKNC'. Results: Rigorous cross validations on the benchmark datasets from four yeast species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Kluyveromyces lactis and Pichia pastoris) have indicated that the proposed predictor is really very powerful for predicting the entire DNA duplication origins. Availability and implementation: The web-server for the iRO-3wPseKNC predictor is available at http://bioinformatics.hitsz.edu.cn/iRO-3wPseKNC/, by which users can easily get their desired results without the need to go through the mathematical details. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: DNA replication is the key of the genetic information transmission, and it is initiated from the replication origins. Identifying the replication origins is crucial for understanding the mechanism of DNA replication. Although several discriminative computational predictors were proposed to identify DNA replication origins of yeast species, they could only be used to identify very tiny parts (250 or 300 bp) of the replication origins. Besides, none of the existing predictors could successfully capture the 'GC asymmetry bias' of yeast species reported by experimental observations. Hence it would not be surprising why their power is so limited. To grasp the CG asymmetry feature and make the prediction able to cover the entire replication regions of yeast species, we develop a new predictor called 'iRO-3wPseKNC'. Results: Rigorous cross validations on the benchmark datasets from four yeast species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Kluyveromyces lactis and Pichia pastoris) have indicated that the proposed predictor is really very powerful for predicting the entire DNA duplication origins. Availability and implementation: The web-server for the iRO-3wPseKNC predictor is available at http://bioinformatics.hitsz.edu.cn/iRO-3wPseKNC/, by which users can easily get their desired results without the need to go through the mathematical details. Supplementary information: Supplementary data are available at Bioinformatics online.