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Hong-Yu Ou

Publications and source records attributed to Hong-Yu Ou.

10 recordsLinked to original sources

A novel strategy for the identification of genomic islands by comparative analysis of the contents and contexts of tRNA sites in closely related bacteria.

We devised software tools to systematically investigate the contents and contexts of bacterial tRNA and tmRNA genes, which are known insertion hotspots for genomic islands (GIs). The strategy, based on MAUVE-facilitated multigenome comparisons, was used to examine 87 Escherichia coli MG1655 tRNA and tmRNA genes and their orthologues in E.coli EDL933, E.coli CFT073 and Shigella flexneri Sf301. Our approach identified 49 GIs occupying approximately 1.7 Mb that mapped to 18 tRNA genes, missing 2 but identifying a further 30 GIs as compared with Islander [Y. Mantri and K. P. Williams (2004), Nucleic Acids Res., 32, D55-D58]. All these GIs had many strain-specific CDS, anomalous GC contents and/or significant dinucleotide biases, consistent with foreign origins. Our analysis demonstrated marked conservation of sequences flanking both empty tRNA sites and tRNA-associated GIs across all four genomes. Remarkably, there were only 2 upstream and 5 downstream deletions adjacent to the 328 loci investigated. In silico PCR analysis based on conserved flanking regions was also used to interrogate hotspots in another eight completely or partially sequenced E.coli and Shigella genomes. The tools developed are ideal for the analysis of other bacterial species and will lead to in silico and experimental discovery of new genomic islands.

Computational Biology↗

ArrayOme: a program for estimating the sizes of microarray-visualized bacterial genomes.

ArrayOme is a new program that calculates the size of genomes represented by microarray-based probes and facilitates recognition of key bacterial strains carrying large numbers of novel genes. Protein-coding sequences (CDS) that are contiguous on annotated reference templates and classified as 'Present' in the test strain by hybridization to microarrays are merged into ICs (ICs). These ICs are then extended to account for flanking intergenic sequences. Finally, the lengths of all extended ICs are summated to yield the 'microarray-visualized genome (MVG)' size. We tested and validated ArrayOme using both experimental and in silico-generated genomic hybridization data. MVG sizing of five sequenced Escherichia coli and Shigella strains resulted in an accuracy of 97-99%, as compared to true genome sizes, when the comprehensive ShE.coli meta-array gene sequences (6239 CDS) were used for in silico hybridization analysis. However, the E.coli CFT073 genome size was underestimated by 14% as this meta-array lacked probes for many CFT073 CDS. ArrayOme permits rapid recognition of discordances between PFGE-measured genome and MVG sizes, thereby enabling high-throughput identification of strains rich in novel genes. Gene discovery studies focused on these strains will greatly facilitate characterization of the global gene pool accessible to individual bacterial species.

Computational Biology↗

Coronavirus phylogeny based on a geometric approach.

A novel coronavirus has been identified as the cause of the outbreak of severe acute respiratory syndrome (SARS). Previous phylogenetic analyses based on sequence alignments show that SARS-CoVs form a new group distantly related to the other three groups of previously characterized coronaviruses. In this paper, a geometric approach based on the Z-curve representation of the whole genome sequence is proposed to analyze the phylogenetic relationships of coronaviruses. The evolutionary distances are obtained through measuring the differences among the three-dimensional Z-curves. The Z-curve is approximately described by its geometric center and the associated three eigenvectors, which indicate the center position and the trend of the Z-curve, respectively. Although some information is lost due to the approximate description of the Z-curve, the phylogenetic tree constructed based on these parameters is consistent with those of previous analyses. The present method has the merits of simplicity and intuitiveness, but it is still in its premature stage. Because the phylogenetic relationships are inferred from the whole genome, instead of some individual genes, the present method represents a new direction of phylogeny study in the post-genome era.

Algorithms↗

DEG: a database of essential genes.

Essential genes are genes that are indispensable to support cellular life. These genes constitute a minimal gene set required for a living cell. We have constructed a Database of Essential Genes (DEG), which contains all the essential genes that are currently available. The functions encoded by essential genes are considered a foundation of life and therefore are likely to be common to all cells. Users can BLAST the query sequences against DEG. If homologous genes are found, it is possible that the queried genes are also essential. Users can search for essential genes by their function or name. Users can also browse and extract all the records in DEG. Essential gene products comprise excellent targets for antibacterial drugs. Analysis of essential genes could help to answer the question of what are the basic functions necessary to support cellular life. DEG is freely accessible from the website http://tubic.tju.edu.cn/deg/.

Computational Biology↗

GS-Finder: a program to find bacterial gene start sites with a self-training method.

In this paper, a self-training method is proposed to recognize translation start sites in bacterial genomes without a prior knowledge of rRNA in the genomes concerned. Many features with biological meanings are incorporated, including mononucleotide distribution patterns near the start codon, the start codon itself, the coding potential and the distance from the most-left start codon to the start codon. The proposed method correctly predicts 92% of the translation start sites of 195 experimentally confirmed Escherichia coli CDSs, 96% of 58 reliable Bacillus subtilis CDSs and 82% of 140 reliable Synechocystis CDSs. Moreover, the self-training method presented might also be used to relocate the translation start sites of putative CDSs of genomes, which are predicted by gene-finding programs. After post-processing by the method presented, the improvement of gene start prediction of some gene-finding programs is remarkable, e.g., the accuracy of gene start prediction of Glimmer 2.02 increases from 63 to 91% for 832 E. coli reliable CDSs. An open source computer program to implement the method, GS-Finder, is freely available for academic purposes from http://tubic.tju.edu.cn/GS-Finder/.

Algorithms↗

Prediction of proteinase cleavage sites in polyproteins of coronaviruses and its applications in analyzing SARS-CoV genomes.

Recently, we have developed a coronavirus-specific gene-finding system, ZCURVE_CoV 1.0. In this paper, the system is further improved by taking the prediction of cleavage sites of viral proteinases in polyproteins into account. The cleavage sites of the 3C-like proteinase and papain-like proteinase are highly conserved. Based on the method of traditional positional weight matrix trained by the peptides around cleavage sites, the present method also sufficiently considers the length conservation of non-structural proteins cleaved by the 3C-like proteinase and papain-like proteinase to reduce the false positive prediction rate. The improved system, ZCURVE_CoV 2.0, has been run for each of the 24 completely sequenced coronavirus genomes in GenBank. Consequently, all the non-structural proteins in the 24 genomes are accurately predicted. Compared with known annotations, the performance of the present method is satisfactory. The software ZCURVE_CoV 2.0 is freely available at http://tubic.tju.edu.cn/sars/.

Amino Acid Sequence↗

ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes.

A new system to recognize protein coding genes in the coronavirus genomes, specially suitable for the SARS-CoV genomes, has been proposed in this paper. Compared with some existing systems, the new program package has the merits of simplicity, high accuracy, reliability, and quickness. The system ZCURVE_CoV has been run for each of the 11 newly sequenced SARS-CoV genomes. Consequently, six genomes not annotated previously have been annotated, and some problems of previous annotations in the remaining five genomes have been pointed out and discussed. In addition to the polyprotein chain ORFs 1a and 1b and the four genes coding for the major structural proteins, spike (S), small envelop (E), membrane (M), and nuleocaspid (N), respectively, ZCURVE_CoV also predicts 5-6 putative proteins in length between 39 and 274 amino acids with unknown functions. Some single nucleotide mutations within these putative coding sequences have been detected and their biological implications are discussed. A web service is provided, by which a user can obtain the annotated result immediately by pasting the SARS-CoV genome sequences into the input window on the web site (http://tubic.tju.edu.cn/sars/). The software ZCURVE_CoV can also be downloaded freely from the web address mentioned above and run in computers under the platforms of Windows or Linux.

Algorithms↗

Analysis of nucleotide distribution in the genome of Streptomyces coelicolor A3(2) using the Z curve method.

The nucleotide distribution of all 33,527 open reading frames (ORFs) (> or =300 bp) in the genome of Streptomyces coelicolor A3(2) has been analyzed using the Z curve method. Each ORF is mapped onto a point in a 9-dimensional space. To visualize the distribution of mapping points, the points are projected onto the principal plane based on principal component analysis. Consequently, the distribution pattern of the 33,527 points in the principal plane shows a flower-like shape, in which there are seven distinct regions. In addition to the central region, there are six petal-like regions around the center, one of which corresponds to 7172 coding sequences. The central region and the remaining five petal-like regions correspond to the intergenic sequences and out-of-frame non-coding ORFs, respectively. It is shown that selective pressure produces a remarkable bias of the G+C content among three codon positions, resulting in the interesting phenomenon observed. A similar phenomenon is also observed for other bacterial genomes with high genomic G+C content, such as Pseudomonas aeruginosa PA01 (G+C = 66.6%). However, for the genomes of Bacillus subtilis (G+C = 43.5%) and Clostridium perfringens (G+C = 28.6%), no similar phenomenon was observed. The finding presented here may be useful to improve the gene-finding algorithms for genomes with high G+C content. A set of supplementary materials including the plots displaying the base distribution patterns of ORFs in 12 prokaryotes is provided on the website http://tubic.tju.edu.cn/highGC/.

Genome, Bacterial↗

The Z curve database: a graphic representation of genome sequences.

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.

Animals↗

ZCURVE: a new system for recognizing protein-coding genes in bacterial and archaeal genomes.

A new system, ZCURVE 1.0, for finding protein- coding genes in bacterial and archaeal genomes has been proposed. The current algorithm, which is based on the Z curve representation of the DNA sequences, lays stress on the global statistical features of protein-coding genes by taking the frequencies of bases at three codon positions into account. In ZCURVE 1.0, since only 33 parameters are used to characterize the coding sequences, it gives better consideration to both typical and atypical cases, whereas in Markov-model-based methods, e.g. Glimmer 2.02, thousands of parameters are trained, which may result in less adaptability. To compare the performance of the new system with that of Glimmer 2.02, both systems were run, respectively, for 18 genomes not annotated by the Glimmer system. Comparisons were also performed for predicting some function-known genes by both systems. Consequently, the average accuracy of both systems is well matched; however, ZCURVE 1.0 has more accurate gene start prediction, lower additional prediction rate and higher accuracy for the prediction of horizontally transferred genes. It is shown that the joint applications of both systems greatly improve gene-finding results. For a typical genome, e.g. Escherichia coli, the system ZCURVE 1.0 takes approximately 2 min on a Pentium III 866 PC without any human intervention. The system ZCURVE 1.0 is freely available at: http://tubic. tju.edu.cn/Zcurve_B/.

Algorithms↗