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DNA replication is a fundamental process in all organisms; this event initiates at sites termed origins of replication. The characteristics of eukaryotic replication origins are best understood in Saccharomyces cerevisiae. For this species, origin prediction algorithms or web servers have been developed based on the sequence features of autonomously replicating sequences (ARSs). However, their performances are far from satisfactory. By utilizing the Z-curve methodology, we present a novel pipeline, Ori-Finder 3, for the computational prediction of replication origins in S. cerevisiae at the genome-wide level based solely on DNA sequences. The ARS exhibiting both an AT-rich stretch and ARS consensus sequence element can be predicted at the single-nucleotide level. For the identified ARSs in the S. cerevisiae reference genome, 83 and 60% of the top 100 and top 300 predictions matched the known ARS records, respectively. Based on Ori-Finder 3, we subsequently built a database of the predicted ARSs identified in more than a hundred S. cerevisiae genomes. Consequently, we developed a user-friendly web server including the ARS prediction pipeline and the predicted ARSs database, which can be freely accessed at http://tubic.tju.edu.cn/Ori-Finder3.

DNA replication is a fundamental process in all organisms; this event initiates at sites termed origins of replication. The characteristics of eukaryotic replication origins are best understood in Saccharomyces cerevisiae. For this species, origin prediction algorithms or web servers have been developed based on the sequence features of autonomously replicating sequences (ARSs). However, their performances are far from satisfactory. By utilizing the Z-curve methodology, we present a novel pipeline, Ori-Finder 3, for the computational prediction of replication origins in S. cerevisiae at the genome-wide level based solely on DNA sequences. The ARS exhibiting both an AT-rich stretch and ARS consensus sequence element can be predicted at the single-nucleotide level. For the identified ARSs in the S. cerevisiae reference genome, 83 and 60% of the top 100 and top 300 predictions matched the known ARS records, respectively. Based on Ori-Finder 3, we subsequently built a database of the predicted ARSs identified in more than a hundred S. cerevisiae genomes. Consequently, we developed a user-friendly web server including the ARS prediction pipeline and the predicted ARSs database, which can be freely accessed at http://tubic.tju.edu.cn/Ori-Finder3.

DNA replication is one of the most basic processes in all three domains of cellular life. With the advent of the post-genomic era, the increasing number of complete archaeal genomes has created an opportunity for exploration of the molecular mechanisms for initiating cellular DNA replication by in vivo experiments as well as in silico analysis. However, the location of replication origins (oriCs) in many sequenced archaeal genomes remains unknown. We present a web-based tool Ori-Finder 2 to predict oriCs in the archaeal genomes automatically, based on the integrated method comprising the analysis of base composition asymmetry using the Z-curve method, the distribution of origin recognition boxes identified by FIMO tool, and the occurrence of genes frequently close to oriCs. The web server is also able to analyze the unannotated genome sequences by integrating with gene prediction pipelines and BLAST software for gene identification and function annotation. The result of the predicted oriCs is displayed as an HTML table, which offers an intuitive way to browse the result in graphical and tabular form. The software presented here is accurate for the genomes with single oriC, but it does not necessarily find all the origins of replication for the genomes with multiple oriCs. Ori-Finder 2 aims to become a useful platform for the identification and analysis of oriCs in the archaeal genomes, which would provide insight into the replication mechanisms in archaea. The web server is freely available at http://tubic.tju.edu.cn/Ori-Finder2/.

BACKGROUND: Chromosomal 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. RESULTS: We 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. CONCLUSION: A 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.