title: Genome-wide characterization and prediction of Arabidopsis thaliana replication origins.
abstract: Identification of replication origins is crucial for the faithful duplication of genomic DNA. The frequencies of single nucleotides and dinucleotides, GC/AT bias and GC/AT profile in the vicinity of Arabidopsis thaliana replication origins were analyzed in the present work. The guanine content or cytosine content is higher in origin of replication (Ori) than in non-Ori. The SS (S=G or C) dinucleotides are favoured in Ori whereas WW (W=A or T) dinucleotides are favoured in non-Ori. GC/AT bias and GC/AT profile in Ori are significantly different from that in non-Ori. Furthermore, by inputting DNA sequence features into support vector machine, we distinguished between the Ori and non-Ori regions in A. thaliana. The total prediction accuracy is about 69.5% as evaluated by the 10-fold cross-validation. This result suggested that apart from DNA sequence, deciphering the selection of replication origin must integrate many other factors including nucleosome positioning, DNA methylation, histone modification, etc. In addition, by comparing predictive performance we found that the predictive accuracy of SVM using sequence features on the context of WS language is significantly better than that of RY language. Furthermore, the same conclusion was also obtained in S. cerevisiae and D. melanogaster.
authors: School of Mathematics, Physics and Biological Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China; School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China; The Institute of Bioengineering and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
authors: Yong-Qiang
authors: YQ
authors: Xing
authors: School of Mathematics, Physics and Biological Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China; The Institute of Bioengineering and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
authors: Guo-Qing
authors: GQ
authors: Liu
authors: School of Mathematics, Physics and Biological Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China; The Institute of Bioengineering and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
authors: Xiu-Juan
authors: XJ
authors: Zhao
authors: School of Mathematics, Physics and Biological Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China; The Institute of Bioengineering and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China; Inner Mongolia Key Laboratory of Biomass-Energy Conversion, Baotou, 014010, China.
authors: Hong-Yu
authors: HY
authors: Zhao
authors: School of Mathematics, Physics and Biological Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China; The Institute of Bioengineering and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China; Inner Mongolia Key Laboratory of Biomass-Energy Conversion, Baotou, 014010, China. Electronic address: nmcailu@163.com.
authors: Lu
authors: L
authors: Cai
mesh_terms: D017360:Arabidopsis / Q000235:genetics*; D018744:DNA, Plant / Q000096:biosynthesis*; D018745:Genome, Plant; D018741:Replication Origin; D060388:Support Vector Machine
chemical_list: D018744:DNA, Plant
keywords: A. thaliana; Compositional bias; Predictive performance; Replication origin; Sequence information; Support vector machine