Computational prediction of origin of replication in bacterial genomes using correlated entropy measure (CEM)

• The GC skew method can predict replication origin for only 75% of bacterial genomes.
• We have carried out our analysis on 500 bacterial genomes.
• We propose a new method called correlated entropy measure (CEM).
• CEM is able to predict replication origin for all 500 bacterial genomes.

We have carried out an analysis on 500 bacterial genomes and found that the de-facto GC skew method could predict the replication origin site only for 376 genomes. We also found that the auto-correlation and cross-correlation based methods have a similar prediction performance. In this paper, we propose a new measure called correlated entropy measure (CEM) which is able to predict the replication origin of all these 500 bacterial genomes. The proposed measure is context sensitive and thus a promising tool to identify functional sites. The process of identifying replication origins from the output of CEM and other methods has been automated to analyze a large number of genomes in a faster manner. We have also explored the applicability of SVM based classification of the workability of each of these methods on all the 500 bacterial genomes based on its length and GC content.