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Computational prediction of origin of replication (ORI) has been of greatinterest in bioinformatics and several methods including GC Skew, Z curve,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. The proposed complex correlation method (iCorr)converts the genome sequence into a sequence of complex numbers by mapping thenucleotides to {+1,-1,+i,-i} instead of {+1,-1} used in the auto-correlationmethod (here, 'i' is square root of -1). Thus, the iCorr method usesinformation about the positions of all the four nucleotides unlike the earlierauto-correlation method which uses the positional information of only onenucleotide. Also, this earlier method required visual inspection of theobtained graphs to identify the location of origin of replication. The proposediCorr method does away with this need and is able to identify the originlocation simply by picking the peak in the iCorr graph. The iCorr method alsoworks for a much smaller segment size compared to the earlier auto-correlationmethod, which can be very helpful in experimental validation of thecomputational predictions. We have also developed a variant of the iCorr methodto predict ORI location in eukaryotes and have tested it with theexperimentally known origin locations of S. cerevisiae with an average accuracyof 71.76%.

iCorr : Complex correlation method to detect origin of replication in prokaryotic and eukaryotic genomes