SUMMARY: DNA replication, a central event for cell proliferation, is the basis of biological inheritance. The identification of replication origins helps to reveal the mechanism of the regulation of DNA replication. However, only few eukaryotic replication origins were characterized not long ago; nevertheless, recent genome-wide approaches have boosted the number of mapped replication origins. To gain a comprehensive understanding of the nature of eukaryotic replication origins, we have constructed a Database of Eukaryotic ORIs (DeOri), which contains all the eukaryotic ones identified by genome-wide analyses currently available. A total of 16 145 eukaryotic replication origins have been collected from 6 eukaryotic organisms in which genome-wide studies have been performed, the replication-origin numbers being 433, 7489, 1543, 148, 348 and 6184 for humans, mice, Arabidopsis thaliana, Kluyveromyces lactis, Schizosaccharomyces pombe and Drosophila melanogaster, respectively. AVAILABILITY: Database of Eukaryotic ORIs (DeOri) can be accessed from http://tubic.tju.edu.cn/deori/

Abstract Of h‐type indices available now, the g‐index is an important one in that it not only keeps some advantages of the h‐index but also counts citations from highly cited articles. However, the g‐index has a drawback that one has to add fictitious articles with zero citation to calculate this index in some important cases. Based on an alternative definition without introducing fictitious articles, an analytical method has been proposed to calculate the g‐index based approximately on the h‐index and the e‐index. If citations for a scientist are ranked by a power law, it is shown that the g‐index can be calculated accurately by the h‐index, the e‐index, and the power parameter. The relationship of the h‐, g‐, and e‐indices presented here shows that the g‐index contains the citation information from the h‐index, the e‐index, and some papers beyond the h‐core.

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.

UNLABELLED: Replication origins (oriCs) of bacterial genomes currently available in GenBank have been predicted by using a systematic method comprising the Z-curve analysis for nucleotide distribution asymmetry, DnaA box distribution, genes adjacent to candidate oriCs and phylogenetic relationships. These oriCs are organized into a MySQL database, DoriC, which provides extensive information and graphical views of the oriC regions. In addition, users can Blast a query sequence or even a whole genome against DoriC to find a homologous one. DoriC will be updated timely and the latest version is DoriC 1.8, in which oriCs of 425 genomes (468 chromosomes) are identified. AVAILABILITY: DoriC can be accessed from http://tubic.tju.edu.cn/doric/. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://tubic.tju.edu.cn/doric/supplementary.htm.

BACKGROUND: It necessary to use highly accurate and statistics-based systems for viral and phage genome annotations. The GeneMark systems for gene-finding in virus and phage genomes suffer from some basic drawbacks. This paper puts forward an alternative approach for viral and phage gene-finding to improve the quality of annotations, particularly for newly sequenced genomes. RESULTS: The new system ZCURVE_V has been run for 979 viral and 212 phage genomes, respectively, and satisfactory results are obtained. To have a fair comparison with the currently available software of similar function, GeneMark, a total of 30 viral genomes that have not been annotated by GeneMark are selected to be tested. Consequently, the average specificity of both systems is well matched, however the average sensitivity of ZCURVE_V for smaller viral genomes (< 100 kb), which constitute the main parts of viral genomes sequenced so far, is higher than that of GeneMark. Additionally, for the genome of Amsacta moorei entomopoxvirus, probably with the lowest genomic GC content among the sequenced organisms, the accuracy of ZCURVE_V is much better than that of GeneMark, because the later predicts hundreds of false-positive genes. ZCURVE_V is also used to analyze well-studied genomes, such as HIV-1, HBV and SARS-CoV. Accordingly, the performance of ZCURVE_V is generally better than that of GeneMark. Finally, ZCURVE_V may be downloaded and run locally, particularly facilitating its utilization, whereas GeneMark is not downloadable. Based on the above comparison, it is suggested that ZCURVE_V may serve as a preferred gene-finding tool for viral and phage genomes newly sequenced. However, it is also shown that the joint application of both systems, ZCURVE_V and GeneMark, leads to better gene-finding results. The system ZCURVE_V is freely available at: http://tubic.tju.edu.cn/Zcurve_V/. CONCLUSION: ZCURVE_V may serve as a preferred gene-finding tool used for viral and phage genomes, especially for anonymous viral and phage genomes newly sequenced.

MOTIVATION: Since the early 1980s of the twentieth century, there has been great progress in the development of computational gene-finding algorithms. Some problems, however, have not yet been solved currently. Recognizing short genes in prokaryotes and short exons in eukaryotes is one of such problems. The paper is devoted to assessing various algorithms, including those currently available and the new ones proposed here, in order to find the best algorithm to solve the issue. RESULTS: The databases consisting of phase-specific coding and non-coding sequences of human genes with length of 192, 162, 129, 108, 87, 63 and 42 bp, respectively, have been established. Based on the databases and a standard benchmark, 19 algorithms were evaluated, which include the methods of Markov models with orders of 1 through 5, codon usage, hexamer usage, codon preference, amino acid usage, codon prototype, Fourier transform and 8 Z curve methods with various numbers of parameters. Consequently, the Z curve methods with 69 and 189 parameters are the best ones among them, based on the databases constructed here. In addition to the highest recognition accuracy confirmed by 10-fold cross-validation tests, the Z curve methods are much simpler computationally than the second best one, the fifth-order Markov chain model, in which 12 288 parameters are used. We hope that the Z curve methods presented in this paper would be beneficial to the further development of gene-finding algorithms. AVAILABILITY: The programs of various Z curve methods are available on request.

MOTIVATION: Some genomic islands contain horizontally transferred genes, which play critical roles in altering the genotypes and phenotypes of organisms, and horizontal gene transfer has been recognized as a universal event throughout bacterial evolution. A windowless method to display the distribution of genomic GC content, the cumulative GC profile, is proposed to identify genomic islands in genomes whose complete genome sequences are available. Two new indices are proposed to assess the codon usage bias and amino acid usage bias in genomic islands. RESULTS: A 211 kb genomic island (CGGI-1) has been identified in the genome of Corynebacterium glutamicum, and three genomic islands VVGI-1, VVGI-2 and VVGI-3, with lengths 167, 40 and 33 kb, respectively, have been identified in the genome of Vibrio vulnificus CMCP6 chromosome I. The CGGI-1 is flanked by two approximately 500 bp direct repeats, and utilizes a Val-tRNA as the integration site. For the VVGI-1 and VVGI-2, each has an integrase gene at 5' junction. All the identified genomic islands show unusual GC content, codon usage and amino acid usage, compared with the rest of the genomes. In addition, it is found that genomic islands are fairly homogenous in terms of GC content variation. An index, h, to quantify the homogeneity of GC content for genomic islands is proposed, and it is shown that h is less than 0.1 for all the genomic islands analyzed. The cumulative GC profile, as well as various indices to assess the codon usage bias, amino acid usage bias and homogeneity of the genomic islands, will be useful in the analysis of other genomes. AVAILABILITY: Programs used in this work and numerical results are available upon request.

MOTIVATION: Genome projects for many prokaryotic and eukaryotic species have been completed and more new genome projects are being underway currently. The availability of a large number of genomic sequences for researchers creates a need to find graphic tools to study genomes in a perceivable form. The Z curve is one of such tools available for visualizing genomes. The Z curve is a unique three-dimensional curve representation for a given DNA sequence in the sense that each can be uniquely reconstructed given the other. The Z curve database for more than 1000 genomes have been established here. RESULTS: The database contains the Z curves for archaea, bacteria, eukaryota, organelles, phages, plasmids, viroids and viruses, whose genomic sequences are currently available. All the 3-dimensional Z curves and their three component curves are stored in the database. The applications of the Z curve database on comparative genomics, gene prediction, computation of G+C content with a windowless technique, prediction of replication origins and terminations of bacterial and archaeal genomes and study of local deviations from the Chargaff Parity Rule 2 etc. are presented in detail. The Z curve database reported here is a treasure trove in which biologists could find useful biological knowledge.