Biomedical subjects

Chun-Ting Zhang

Publications and source records attributed to Chun-Ting Zhang.

35 records · Page 2Linked to original sources

PubMed2003

Identification of genomic islands in the genome of Bacillus cereus by comparative analysis with Bacillus anthracis.

Horizontal gene transfer has been recognized as a universal event throughout bacterial evolution. The availability of both complete genome sequences of Bacillus cereus and B. anthracis provides the possibility to perform comparative analysis based on their genomes. By using a windowless method to display the distribution of the genomic GC content of B. cereus and B. anthracis, we have found three genomic islands in the genome of B. cereus, i.e., BCGI-1, BCGI-2, and BCGI-3, respectively, which are absent in the genome of B. anthracis. All the genomic islands have abrupt changes in GC content compared with that of surrounding regions. BCGI-1 has many conserved features of genomic islands, e.g., a Val-tRNA gene is utilized as the integration site, and a site-specific recombinase gene is located at the 3' end. BCGI-2 has a large percentage of phage protein, suggesting a phage-related recombination is involved. BCGI-3 contains a ferric anguibactin transport system, which is likely to be involved in the iron transport that enables the bacterium to overcome the iron limitation in the host. In addition, BCGI-3 also contains a cluster of genes related to lantibiotics, which may play a role during the evolution of the genome. Furthermore, the integrations of the genomic islands, BCGI-1 and BCGI-3, result in deletions of DNA sequence fragments; therefore, such integrations lead to both gene gain and gene loss simultaneously.

Bacillus anthracis↗

PubMed2003

Identification of isochore boundaries in the human genome using the technique of wavelet multiresolution analysis.

Incorporated with the Z curve method, the technique of wavelet multiresolution (also known as multiscale) analysis has been proposed to identify the boundaries of isochores in the human genome. The human MHC sequence and the longest contigs of human chromosomes 21 and 22 are used as examples. The boundary between the isochores of Class III and Class II in the MHC sequence has been detected and found to be situated at the position 2,490,368bp. This result is in good agreement with the experimental evidence. An isochore with a length of about 7Mb in chromosome 21 has been identified and found to be gene- and Alu-poor. We have also found that the G+C content of chromosome 21 is more homogeneous than that of chromosome 22. Compared with the window-based methods, the present method has the highest resolution for identifying the boundaries of isochores, even at a scale of single base. Compared with the entropic segmentation method, the present method has the merits of more intuitiveness and less calculations. The important conclusion drawn in this study is that the segmentation points, at which the G+C content undergoes relatively dramatic changes, do exist in the human genome. These 'singularity' points may be considered to be candidates of isochore boundaries in the human genome. The method presented is a general one and can be used to analyze any other genomes.

Algorithms↗

PubMed2003

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↗

PubMed2003

An isochore map of the human genome based on the Z curve method.

The distribution of the G+C content in the human genome has been studied by using a windowless technique derived from the Z curve method. The most important findings presented in this paper are twofold. First, abrupt variations of the G+C content along human chromosome sequences are the main variation patterns of G+C content. It is found that at some sites, the G+C content undergoes abrupt changes from a G+C-rich region to a G+C-poor region alternatively and vice versa. Second, it is shown that long domains with relatively homogeneous G+C content along each chromosome do exist. These domains are thought to be isochores, which usually have sharp boundaries. Consequently, 56 isochores longer than 3 Mb have been identified in chromosomes 1-22, X and Y. Boundaries, size and G+C content of each isochore identified are listed in detail. As an example to demonstrate the power of the method, the boundary between the Classes III and II isochores of the MHC sequence has been determined and found to be at 2,477,936, which is in good agreement with the experimental evidence. A homogeneity index is introduced to measure the homogeneity of G+C content in isochores. We emphasize that the homogeneity of G+C content is relative. The isochores in which the G+C content keeps absolutely constant do not exist. Isochore structures appear to be a basic organization of the human genome. Due to the relevance to many important biological functions, the clarification of isochore structures will provide much insight into the understanding of the human genome.

Algorithms↗

PubMed2003

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↗

PubMed2003

Seven GC-rich microbial genomes adopt similar codon usage patterns regardless of their phylogenetic lineages.

Seven GC-rich (group I) and three AT-rich (group II) microbial genomes are analyzed in this paper. The seven microbes in group I belong to different phylogenetic lineages, even different domains of life. The common feature is that they are highly GC-rich organisms, with more than 60% genomic GC content. Group II includes three bacteria, which belong to the same subdivision as Pseudomonas aeruginosa in group I. The genomic GC content of the three bacteria is in the range of 26-50%. It is shown that although the phylogenetic lineages of the organisms in group I are remote, the common feature of highly genomic GC content forces them to adopt similar codon usage patterns, which constitutes the basis of an algorithm using a set of universal parameters to recognize known genes in the seven genomes. The common codon usage pattern of function known genes in the seven genomes is GGS type, where G, G, and S are the bases of G, non-G, and G/C, respectively. On the contrary, although the phylogenetic lineages of the three bacteria in group II are quite close, the codon usage patterns of function known genes in these genomes are obviously distinct. There are no universal parameters to identify known genes in the three genomes in group II. It can be deduced that the genomic GC content is more important than phylogenetic lineage in gene recognition programs. We hope that the work might be useful for understanding the common characteristics in the organization of microbial genomes.

Algorithms↗

PubMed2003

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↗

PubMed2003

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↗

PubMed2003

Multiple replication origins of the archaeon Halobacterium species NRC-1.

The genomic sequence of the halophilic archaeon Halobacterium NRC-1 has been analyzed by the Z curve method. The Z curve is a three-dimensional curve that uniquely represents a given DNA sequence. Based on the known behaviors of the Z curves for the archaea whose replication origins have been identified, the analysis of the Z curve for the genome of Halobacterium NRC-1 strongly suggests that the large genome has two replication origins, oriC1 (921,863-922,014) and oriC2 (1,806,444-1,807,229), which are located at two sharp peaks of the Z curve. These two regions are next to the cdc6 genes and contain multiple copies of stretches of G and C, i.e., ggggtgggg and ccccacccc, which may also be regarded as direct and inverted repeats. Based on the above analysis, a model of replication of Halobacterium NRC-1 with two replication origins and two termini has been proposed. The experimental confirmation of this model would constitute the first example of multiple replication origins of archaea, which will finally provide much insight into the understanding of replication mechanisms of eukaryotic organisms, including human. In addition, the potential multiple replication origins of the archaeon Sulfolobus solfataricus are suggested by the analysis based on the Z curve method.

Base Sequence↗

PubMed2003

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↗

PubMed2003

Q9, a content-balancing accuracy index to evaluate algorithms of protein secondary structure prediction.

A content-balancing accuracy index, called Q(9), has been proposed to evaluate algorithms of protein secondary structure prediction. Here the content-balancing means that the evaluation is independent of the contents of helix, strand and coil in the protein being predicted. It is shown that Q(9) is much superior to the widely used index Q(3). Therefore, algorithms are more objectively evaluated by Q(9) than Q(3). Based on 396 non-homologous proteins, five algorithms of secondary structure prediction were evaluated and compared by the new index Q(9). Of the five algorithms, PHD turned out to be the unique algorithm with an average Q(9) better than 60%. Based on the new index, it is shown that the performance of the consensus method based on a jury-decision from several algorithms is even worse than that of the best individual method. Rather than Q(3), we believe that Q(9) should be used to evaluate algorithms of protein secondary structure prediction in future studies in order to improve prediction quality.

Algorithms↗

PubMed2003

Gene recognition from questionable ORFs in bacterial and archaeal genomes.

The ORFs of microbial genomes in annotation files are usually classified into two groups: the first corresponds to known genes; whereas the second includes 'putative', 'probable', 'conserved hypothetical', 'hypothetical', 'unknown' and 'predicted' ORFs etc. Since the annotation is not 100% accurate, it is essential to confirm which ORF of the latter group is coding and which is not. Starting from known genes in the former, this paper describes an improved Z curve method to recognize genes in the latter. Ten-fold cross-validation tests show that the average accuracy of the algorithm is greater than 99% for recognizing the known genes in 57 bacterial and archaeal genomes. The method is then applied to recognize genes of the latter group. The likely non-coding ORFs in each of the 57 bacterial or archaeal genomes studied here are recognized and listed at the website http://tubic.tju.edu.cn/ZCURVE_C_html/noncoding.html. The working mechanism of the algorithm has been discussed in details. A computer program, called ZCURVE_C, was written to calculate a coding score called Z-curve score for ORFs in the above 57 bacterial and archaeal genomes. Coding/non-coding is simply determined by the criterion of Z-curve score > 0/ Z-curve score < 0. A website has been set up to provide the service to calculate the Z-curve score. A user may submit the DNA sequence of an ORF to the server at http://tubic.tju.edu.cn/ZCURVE_C/Default.cgi, and the Z-curve score of the ORF is calculated and returned to the user immediately.

Algorithms↗

PubMed2002

Single replication origin of the archaeon Methanosarcina mazei revealed by the Z curve method.

The genomic sequence of the archaeon Methanosarcina mazei has been analyzed by the Z curve method. The Z curve is a three-dimensional curve that uniquely represents the given DNA sequence. The three-dimensional Z curve and its x and y components for the genome of M. mazei show a sharp peak and relatively broad peak, respectively. The cdc6 gene is located exactly at the position of the sharp peak. Based on the known behavior of the Z curves for the archaea whose replication origins have been identified, we hypothesize that the replication origin and termination sites correspond to the positions of the sharp peak and broad peak, respectively. We have located an intergenic region that is between the cdc6 gene (MM1314) and the gene for an adjacent protein (MM1315), which shows strong characteristics of the known replication origins. This region is highly rich in AT and contains multiple copies of consecutive repeats. Our results strongly suggest that the single replication origin of M. mazei is situated at the intergenic region between the cdc6 gene and the gene for the adjacent protein, from 1,564,657 to 1,566,241 bp of the genome.

Base Sequence↗

PubMed2002

Recognizing shorter coding regions of human genes based on the statistics of stop codons.

With the quick progress of the Human Genome Project, a great amount of uncharacterized DNA sequences needs to be annotated copiously by better algorithms. Recognizing shorter coding sequences of human genes is one of the most important problems in gene recognition, which is not yet completely solved. This paper is devoted to solving the issue using a new method. The distributions of the three stop codons, i.e., TAA, TAG and TGA, in three phases along coding, noncoding, and intergenic sequences are studied in detail. Using the obtained distributions and other coding measures, a new algorithm for the recognition of shorter coding sequences of human genes is developed. The accuracy of the algorithm is tested based on a larger database of human genes. It is found that the average accuracy achieved is as high as 92.1% for the sequences with length of 192 base pairs, which is confirmed by sixfold cross-validation tests. It is hoped that by incorporating the present method with some existing algorithms, the accuracy for identifying human genes from unannotated sequences would be increased.

Algorithms↗

PubMed2002

Using a Euclid distance discriminant method to find protein coding genes in the yeast genome.

The Euclid distance discriminant method is used to find protein coding genes in the yeast genome, based on the single nucleotide frequencies at three codon positions in the ORFs. The method is extremely simple and may be extended to find genes in prokaryotic genomes or eukaryotic genomes with less introns. Six-fold cross-validation tests have demonstrated that the accuracy of the algorithm is better than 93%. Based on this, it is found that the total number of protein coding genes in the yeast genome is less than or equal to 5579 only, about 3.8-7.0% less than 5800-6000, which is currently widely accepted. The base compositions at three codon positions are analyzed in details using a graphic method. The result shows that the preference codons adopted by yeast genes are of the RGW type, where R, G and W indicate the bases of purine, non-G and A/T, whereas the 'codons' in the intergenic sequences are of the form NNN, where N denotes any base. This fact constitutes the basis of the algorithm to distinguish between coding and non-coding ORFs in the yeast genome. The names of putative non-coding ORFs are listed here in detail.

Algorithms↗

PubMed2002

A graphic representation of protein sequence and predicting the subcellular locations of prokaryotic proteins.

Zp curve, a three-dimensional space curve representation of protein primary sequence based on the hydrophobicity and charged properties of amino acid residues along the primary sequence is suggested. Relying on the Zp parameters extracted from the three components of the Zp curve and the Bayes discriminant algorithm, the subcellular locations of prokaryotic proteins were predicted. Consequently, an accuracy of 81.5% in the cross-validation test has been achieved using 13 parameters extracted from the curve for the database of 997 prokaryotic proteins. The result is slightly better than that of using the neural network method (80.9%) based on the amino acid composition for the same database. By jointing the amino acid composition and the Zp parameters, the overall predictive accuracy 89.6% can be achieved. It is about 3% higher than that of the Bayes discriminant algorithm based merely on the amino acid composition for the same database. The prediction is also performed with a larger dataset derived from the version 39 SWISS-PROT databank and two datasets with different sequence similarity. Even for the dataset of non-sequence similarity, the improvement can be of 4.4% in the cross-validation test. The results indicate that the Zp parameters are effective in representing the information within a protein primary sequence. The method of extracting information from the primary structure may be useful for other areas of protein studies.

Algorithms↗

PubMed2002

Evaluation of gene-finding algorithms by a content-balancing accuracy index.

A content-balancing accuracy index, called q(9), to evaluate gene-finding algorithms has been proposed. Here the concept of content-balancing means that the evaluation by this index is independent of the coding and non-coding composition of the sequence being evaluated. Since the coding and non-coding compositions are severely unbalanced in eukaryotic genomes, the performance of gene-finding algorithms is either over- or under-evaluated by the widely used accuracy indices, e.g., the correlation coefficient, due to the lack of content-balancing ability. Using the new accuracy index q(9), seven gene-finding algorithms, FGENES; Gene-Mark.hmm; Genie; Genescan; HMMgene; Morgan and MZEF, were compared and evaluated. It is shown that Genescan is still the best one, but with q(9)= 89%, averaged over the prediction for 195 sequences. In addition to the content-balancing ability, q(9) has the merit of having definition in all possible cases. It is also shown that the traditional specificity s(p) carries important information on the performance of the algorithm being evaluated. The set of sensitivity s(n), specificity s(p) and the accuracy q(9) constitutes a complete kit to evaluate gene-finding algorithms at nucleotide level. In addition, a graphic method to compare and evaluate gene-finding algorithms has been proposed, too. Its major advantage is that the overall performance of algorithms can be grasped quickly in a perceivable form. Additionally, the new accuracy index q(9) may be applied to evaluate the performance of weather forecast, clinical diagnosis, psychological examination and protein secondary structure prediction etc.

Algorithms↗