Computational prediction of origin of replication (ORI) has been of great interest in bioinformatics and several methods including GC-skew, auto-correlation etc. have been explored in the past. In this paper, we have extended the auto-correlation method to predict ORI location with much higher resolution for prokaryotes and eukaryotes, which can be very helpful in experimental validation of the computational predictions. The proposed complex correlation method (iCorr) converts the genome sequence into a sequence of complex numbers by mapping the nucleotides to {+1,-1,+i,-i} instead of {+1,-1} used in the auto-correlation method (here, i is square root of -1). Thus, the iCorr method exploits the complete spatial information about the positions of all the four nucleotides unlike the earlier auto-correlation method which uses the positional information of only one nucleotide. Also, the earlier auto-correlation method required visual inspection of the obtained graphs to identify the location of origin of replication. The proposed iCorr method does away with this need and is able to identify the origin location simply by picking the peak in the iCorr graph.
Identification and characterization of unique to human regulatory sequences in embryonic stem cells reveal associations with transposable elements, distal enhancers, non-coding RNA, and DNA methylation-driven mechanisms of genome editing
The Stanford University School of Medicine（斯坦福大学医学院）；Sanford-Burnham Medical Research Institute（桑福德-伯纳姆医学研究所）；Genlight Technology Corporation（Genlight科技有限公司）
