Title: iCorr : Complex correlation method to detect origin of replication in prokaryotic and eukaryotic genomes,
Abstract: Computational prediction of origin of replication (ORI) has been of great
interest in bioinformatics and several methods including GC Skew, Z curve,
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. 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 uses
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, this earlier 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. The iCorr method also
works for a much smaller segment size compared to the earlier auto-correlation
method, which can be very helpful in experimental validation of the
computational predictions. We have also developed a variant of the iCorr method
to predict ORI location in eukaryotes and have tested it with the
experimentally known origin locations of S. cerevisiae with an average accuracy
of 71.76%.