Abstract
BackgroundChromosomal replication is the central event in the bacterial cell cycle. Identification of replication origins (oriCs) is necessary for almost all newly sequenced bacterial genomes. Given the increasing pace of genome sequencing, the current available software for predicting oriCs, however, still leaves much to be desired. Therefore, the increasing availability of genome sequences calls for improved software to identify oriCs in newly sequenced and unannotated bacterial genomes.ResultsWe have developed Ori-Finder, an online system for finding oriCs in bacterial genomes based on an integrated method comprising the analysis of base composition asymmetry using the Z-curve method, distribution of DnaA boxes, and the occurrence of genes frequently close to oriCs. The program can also deal with unannotated genome sequences by integrating the gene-finding program ZCURVE 1.02. Output of the predicted results is exported to an HTML report, which offers convenient views on the results in both graphical and tabular formats.ConclusionA web-based system to predict replication origins of bacterial genomes has been presented here. Based on this system, oriC regions have been predicted for the bacterial genomes available in GenBank currently. It is hoped that Ori-Finder will become a useful tool for the identification and analysis of oriCs in both bacterial and archaeal genomes.
Highlights
Chromosomal replication is the central event in the bacterial cell cycle
To identify oriC regions of unannotated bacterial genomes, we have developed an online tool, Ori-Finder, based on an integrated method comprising gene identification, analysis of base composition asymmetry using the Z-curve method, distribution of DnaA boxes, occurrence of genes frequently close to oriCs and phylogenetic relationships
DnaA and its binding sites are well conserved throughout the bacterial kingdom, replication origins from different species show considerable diversity in terms of the number, arrangement and even the consensus sequence of DnaA boxes
Summary
Based on this online system, we have predicted oriC regions for the bacterial genomes available in GenBank currently. Using the three methods (GC-skew, location of the dnaA gene and distribution of DnaA boxes) resulted in better prediction of oriC regions by Mackiewicz et al [8]. For another example, applying the methods based on GC-skew, dnaA gene location and E. coli perfect DnaA box distribution to the genome of Synechococcus sp. For the convenience of users' query, the predicted oriC regions have been organized into a MySQL database, called DoriC, which is freely available online [15]
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