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Computational prediction of origin of replication (ORI) has been of greatinterest in bioinformatics and several methods including GC-skew,auto-correlation etc. have been explored in the past. In this paper, we haveextended the auto-correlation method to predict ORI location with much higherresolution for prokaryotes and eukaryotes, which can be very helpful inexperimental validation of the computational predictions. The proposed complexcorrelation method (iCorr) converts the genome sequence into a sequence ofcomplex 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, theiCorr method exploits the complete spatial information about the positions ofall the four nucleotides unlike the earlier auto-correlation method which usesthe positional information of only one nucleotide. Also, the earlierauto-correlation method required visual inspection of the obtained graphs toidentify the location of origin of replication. The proposed iCorr method doesaway with this need and is able to identify the origin location simply bypicking the peak in the iCorr graph.

Computational prediction of replication sites in DNA sequences using complex number representation