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

TL;DR

The paper introduces iCorr, a complex correlation method that improves the prediction of replication origins in genomes by utilizing complex number mapping, enabling higher resolution and automation in identifying ORI locations in both prokaryotic and eukaryotic genomes.

Contribution

The novel iCorr method extends auto-correlation by using complex numbers to incorporate all nucleotides, eliminating the need for visual inspection and allowing predictions on smaller genome segments.

Findings

iCorr accurately predicts ORI in prokaryotes with high resolution.

The method successfully predicts eukaryotic ORI with an average accuracy of 71.76%.

It requires smaller genome segments, facilitating experimental validation.

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%.