KEGG Metabolic Pathways Research Articles

Gene expression profile of hepatopancreas from grass shrimp Palaemonetes pugio exposed to cyclic hypoxia

Estuarine organisms often experience periods of cyclic hypoxia characterized by hypoxia in the early morning and normoxia in the afternoon. Here we examine the genomic responses of grass shrimp, Palaemonetes pugio, exposed to cyclic hypoxia in the laboratory. Differentially expressed genes in the hepatopancreas were determined in cyclic hypoxic vs. normoxic control groups after 1, 2, 5 and 10days of exposure to cyclic hypoxia using microarrays printed with 661 annotated transcripts obtained from multiple EST (expressed sequence tag) libraries. Sampling on each day was conducted at two different time series, one in the morning (representing low concentration of dissolved oxygen (DO), designated C-AM) and one in the afternoon (representing high DO concentration, designated C-PM). Distinct differences were observed between the number and identity of specific genes that were significantly down- or up-regulated in shrimp collected at the low DO and high DO points of the cyclic DO cycle. However, cluster analysis showed that the overall response patterns of high (C-PM) and low DO (C-AM) exposures were in the same cluster at 1, 2, and 5days. In contrast, the response patterns on different days were in different clusters. Day 1 was characterized by up-regulation of 17 unknown genes in the morning and a transient down-regulation of several hemocyanin genes, which returned to normoxic control levels in the afternoon. Days 2 and 5 showed significant down-regulation of 10 (C-AM) and 15 (C-PM) unknown genes, respectively. On day 10 the high DO samples showed a dramatic increase in the number of up-regulated genes, including several distinct hemocyanin genes, and this profile did not cluster with any of the other treatment groups. Vitellogenin, cathepsin L, cytochrome c oxidase subunit III, and fatty acid binding protein 10 were the signature down-regulated genes at day 10 (C-AM). According to GO annotation, the most abundant group of genes for both cyclic low (C-AM) and high (C-PM) DO exposure was associated with transport, defense response, and metabolic process. The differentially expressed genes were mapped to KEGG metabolic and regulatory pathways according to the gene distribution in Drosophila pathway database. Cyclic high (C-PM) DO affected a broad range of pathways compared to cyclic low (C-AM) DO.

VIP DB — A viral protein domain usage and distribution database

During the viral infection and replication processes, viral proteins are highly regulated and may interact with host proteins. However, the functions and interaction partners of many viral proteins have yet to be explored. Here, we compiled a VIral Protein domain DataBase (VIP DB) to associate viral proteins with putative functions and interaction partners. We systematically assign domains and infer the functions of proteins and their protein interaction partners from their domain annotations. A total of 2,322 unique domains that were identified from 2,404 viruses are used as a starting point to correlate GO classification, KEGG metabolic pathway annotation and domain–domain interactions. Of the unique domains, 42.7% have GO records, 39.6% have at least one domain–domain interaction record and 26.3% can also be found in either mammals or plants. This database provides a resource to help virologists identify potential roles for viral protein. All of the information is available at http://vipdb.cgu.edu.tw.

Insight into trichomonas vaginalis genome evolution through metabolic pathways comparison.

Trichomonas vaginalis causes the trichomoniasis, in women and urethritis and prostate cancer in men. Its genome draft published by TIGR in 2007 presents many unusual genomic and biochemical features like, exceptionally large genome size, the presence of hydrogenosome, gene duplication, lateral gene transfer mechanism and the presence of miRNA. To understand some of genomic features we have performed a comparative analysis of metabolic pathways of the T. vaginalis with other 22 significant common organisms. Enzymes from the biochemical pathways of T. vaginalis and other selected organisms were retrieved from the KEGG metabolic pathway database. The metabolic pathways of T. vaginalis common in other selected organisms were identified. Total 101 enzymes present in different metabolic pathways of T. vaginalis were found to be orthologous by using BLASTP program against the selected organisms. Except two enzymes all identified orthologous enzymes were also identified as paralogous enzymes. Seventy-five of identified enzymes were also identified as essential for the survival of T. vaginalis, while 26 as non-essential. The identified essential enzymes also represent as good candidate for novel drug targets. Interestingly, some of the identified orthologous and paralogous enzymes were found playing significant role in the key metabolic activities while others were found playing active role in the process of pathogenesis. The N-acetylneuraminate lyase was analyzed as the candidate of lateral genes transfer. These findings clearly suggest the active participation of lateral gene transfer and gene duplication during evolution of T. vaginalis from the enteric to the pathogenic urogenital environment.

Analysis of common substructures of metabolic compounds within the different organism groups.

With the increase in available post-genomic data and metabolic pathway information, we have been focusing on revealing the biological meaning of higher phenomena such as relationships of metabolic systems in different organisms. Metabolism plays an essential role in all cellular organisms, e.g. energy transportation, signal transduction and structural formation of cell components. The metabolic pathway of each organism has a different landscape from all others because of the different sets of enzymes encoded in the genome. The organisms that are incapable of producing their own essential chemical compounds should acquire them in some way from other organisms that can produce them. For example, several vitamins are required by animals to survive. In this manner we can assume that the different availabilities of metabolites may influence the relationship between organisms in nature. In this study, we focus on the differences in available metabolites among organisms. First, we divided 239 species with complete genomes into 9 organism groups in accordance with phylogeny and averaged out the annotation quality and the phylogenetic sparsity. Then, we calculated the commonly used chemical compounds between organism groups and the uniquely used chemical compounds in an organism group. The total number of metabolites we consider in this study is 1,074, which is about one-third of all metabolites that appear in the KEGG metabolic pathways. Finally we show the differences and the similarities between organism groups on every metabolic pathway map, illustrating the commonly observed substructures within the uniquely used metabolites. These results will help us to better comprehend the architecture of metabolic pathways and the relationships between organisms.

A Closer Look at Bacteroides: Phylogenetic Relationship and Genomic Implications of a Life in the Human Gut

The human gut is extremely densely inhabited by bacteria mainly from two phyla, Bacteroidetes and Firmicutes, and there is a great interest in analyzing whole-genome sequences for these species because of their relation to human health and disease. Here, we do whole-genome comparison of 105 Bacteroidetes/Chlorobi genomes to elucidate their phylogenetic relationship and to gain insight into what is separating the gut living Bacteroides and Parabacteroides genera from other Bacteroidetes/Chlorobi species. A comprehensive analysis shows that Bacteroides species have a higher number of extracytoplasmic function σ factors (ECF σ factors) and two component systems for extracellular signal transduction compared to other Bacteroidetes/Chlorobi species. A whole-genome phylogenetic analysis shows a very little difference between the Parabacteroides and Bacteroides genera. Further analysis shows that Bacteroides and Parabacteroides species share a large common core of 1,085 protein families. Genome atlases illustrate that there are few and only small unique areas on the chromosomes of four Bacteroides/Parabacteroides genomes. Functional classification to clusters of othologus groups show that Bacteroides species are enriched in carbohydrate transport and metabolism proteins. Classification of proteins in KEGG metabolic pathways gives a detailed view of the genome's metabolic capabilities that can be linked to its habitat. Bacteroides pectinophilus and Bacteroides capillosus do not cluster together with other Bacteroides species, based on analysis of 16S rRNA sequence, whole-genome protein families and functional content, 16S rRNA sequences of the two species suggest that they belong to the Firmicutes phylum. We have presented a more detailed and precise description of the phylogenetic relationships of members of the Bacteroidetes/Chlorobi phylum by whole genome comparison. Gut living Bacteroides have an enriched set of glycan, vitamin, and cofactor enzymes important for diet digestion.

Comparison of the mesophilic cellulosome‐producing Clostridium cellulovorans genome with other cellulosome‐related clostridial genomes

SummaryClostridium cellulovorans, an anaerobic and mesophilic bacterium, degrades native substrates in soft biomass such as corn fibre and rice straw efficiently by producing an extracellular enzyme complex called the cellulosome. Recently, we have reported the whole‐genome sequence of C. cellulovorans comprising 4220 predicted genes in 5.10 Mbp [Y. Tamaru et al., (2010) J. Bacteriol., 192: 901–902]. As a result, the genome size of C. cellulovorans was about 1 Mbp larger than that of other cellulosome‐producing clostridia, mesophilic C. cellulolyticum and thermophilic C. thermocellum. A total of 57 cellulosomal genes were found in the C. cellulovorans genome, and they coded for not only carbohydrate‐degrading enzymes but also a lipase, peptidases and proteinase inhibitors. Interestingly, two novel genes encoding scaffolding proteins were found in the genome. According to KEGG metabolic pathways and their comparison with 11 Clostridial genomes, gene expansion in the C. cellulovorans genome indicated mainly non‐cellulosomal genes encoding hemicellulases and pectin‐degrading enzymes. Thus, by examining genome sequences from multiple Clostridium species, comparative genomics offers new insight into genome evolution and the way natural selection moulds functional DNA sequence evolution. Our analysis, coupled with the genome sequence data, provides a roadmap for constructing enhanced cellulosome‐producing Clostridium strains for industrial applications such as biofuel production.

Modeling Oncogenic Signaling in Colon Tumors by Multidirectional Analyses of Microarray Data Directed for Maximization of Analytical Reliability

BackgroundClinical progression of colorectal cancers (CRC) may occur in parallel with distinctive signaling alterations. We designed multidirectional analyses integrating microarray-based data with biostatistics and bioinformatics to elucidate the signaling and metabolic alterations underlying CRC development in the adenoma-carcinoma sequence.Methodology/Principal FindingsStudies were performed on normal mucosa, adenoma, and carcinoma samples obtained during surgery or colonoscopy. Collections of cryostat sections prepared from the tissue samples were evaluated by a pathologist to control the relative cell type content. The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data was generated using two normalization algorithms: MAS5.0 and GCRMA with least-variant set (LVS). The data was evaluated using pair-wise comparisons and data decomposition into singular value decomposition (SVD) modes. The method selected for the functional analysis used the Kolmogorov-Smirnov test. Expressional profiles obtained in 105 samples of whole tissue sections were used to establish oncogenic signaling alterations in progression of CRC, while those representing 40 microdissected specimens were used to select differences in KEGG pathways between epithelium and mucosa. Based on a consensus of the results obtained by two normalization algorithms, and two probe set sorting criteria, we identified 14 and 17 KEGG signaling and metabolic pathways that are significantly altered between normal and tumor samples and between benign and malignant tumors, respectively. Several of them were also selected from the raw microarray data of 2 recently published studies (GSE4183 and GSE8671).Conclusion/SignificanceAlthough the proposed strategy is computationally complex and labor–intensive, it may reduce the number of false results.

GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms

BackgroundAn important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases.ResultsHere we describe GeneMesh, a web-based program that facilitates analysis of DNA microarray gene expression data. GeneMesh relates genes in a query set to categories available in the Medical Subject Headings (MeSH) hierarchical index. The interface enables hypothesis driven relational analysis to a specific MeSH subcategory (e.g., Cardiovascular System, Genetic Processes, Immune System Diseases etc.) or unbiased relational analysis to broader MeSH categories (e.g., Anatomy, Biological Sciences, Disease etc.). Genes found associated with a given MeSH category are dynamically linked to facilitate tabular and graphical depiction of Entrez Gene information, Gene Ontology information, KEGG metabolic pathway diagrams and intermolecular interaction information. Expression intensity values of groups of genes that cluster in relation to a given MeSH category, gene ontology or pathway can be displayed as heat maps of Z score-normalized values. GeneMesh operates on gene expression data derived from a number of commercial microarray platforms including Affymetrix, Agilent and Illumina.ConclusionsGeneMesh is a versatile web-based tool for testing and developing new hypotheses through relating genes in a query set (e.g., differentially expressed genes from a DNA microarray experiment) to descriptors making up the hierarchical structure of the National Library of Medicine controlled vocabulary thesaurus, MeSH. The system further enhances the discovery process by providing links between sets of genes associated with a given MeSH category to a rich set of html linked tabular and graphic information including Entrez Gene summaries, gene ontologies, intermolecular interactions, overlays of genes onto KEGG pathway diagrams and heatmaps of expression intensity values. GeneMesh is freely available online at http://proteogenomics.musc.edu/genemesh/.

Structural and Functional Analysis of Giant Strong Component of Bacillus thuringiensis Metabolic Network

The purpose of this work was to study the giant strong component (GSC) of B. thuringiensis metabolic network by structural and functional analysis. Based on so-called "bow tie" structure, we extracted and studied GSC with its functional significance. Global structural properties such as degree distribution and average path length were computed and indicated that the GSC is also a small-world and scale-free network. Furthermore, the GSC was decomposed and functional significant for metabolism of these divisions were investigated by comparing to KEGG metabolic pathways.

Gene profiling of MAPK pathway in human osteosarcoma

To explore the functional effects of MAPK pathway in the pathogenesis of human osteosarcoma. Gene microarray (Human Genome U133A, Affymetrix) was used to screen the differential expression of genes involved in MAPK pathway between osteosarcoma cell lines and 3 osteoblastic cell lines. KEGG metabolic pathway analysis was performed among significantly increased or decreased genes using the MATLAB software. Immunohistochemical technique was used to detect the expressions of ERK1/2, JNK and p38 proteins among 48 osteosarcoma and benign 24 osteoblastic tumor samples. Using an entrance limit of > or = 2.0, 18 differentially expressed MAPK pathway-related genes were selected (10 up-regulated, 8 down-regulated) to mapped to the MAPK pathway of KEGG which are all important node genes. The positive rates of ERK1/2, JNK and p38 proteins were 83.3% (40/48), 72.9% (35/48) and 85.4% (41/48) in osteosarcomas,and 12.5% (3/24), 8.3% (2/24) and 16.7% (4/24) in the control group, respectively. The positive rates and expression intensities were statistically different between the 2 groups (P<0.01). MAPK pathway plays an important role in the pathogenesis of osteosarcoma. ERK, JNK and p38 form an intercoordinating network and regulate the cell proliferation, differentiation, apoptosis, invasion and migration in osteosarcoma.

Transcriptome Analysis and Identification of Genes Related to Immune Function in Skin of the Chinese Brown Frog

The Chinese brown frog (Rana chensinensis) is a special amphibian in northern China, as it has been used widely in traditional Chinese medicine. The skin of the Chinese brown frog is also a promising resource for producing diverse antimicrobial peptides. To obtain a more comprehensive view of the metabolism and effective pharmacological components of Chinese brown frog skin, we constructed a non-normalized cDNA library from the skin. By sequencing cDNA clones at the 5' end, we obtained 5,976 high-quality EST sequences, which clustered into 512 contigs and 1379 singletons (in all 1,891 clusters). After BLAST searches of the protein and nucleic acid databases in GenBank, we found 46.7% of clusters to have significant similarity to known sequences; 28% matched Xenopus tropicalis ESTs and 29.1% matched Xenopus laevis ESTs. Gene annotation results indicated that genes related to secretion and defensive function, such as ubiquitin, lectin, and proteinase inhibitors, are highly expressed in the skin. Whey acidic-domain proteins are also highly abundant in the skin. Furthermore, both a beta-defensin and a lysozyme are transcribed in the frog skin, providing antibacterial protection. Analyses of gene ontology and KEGG metabolic pathways indicated the physiological roles of Chinese brown frog skin.

An Emergent Self-Organizing Map Based Analysis Pipeline for Comparative Metabolome Studies

Modern high-throughput techniques allow for the identification and quantification of hundreds of metabolites ofa biological system which cover central parts of the metabolome. Due to the amount and complexity of obtained data there is an increasing need for the development of appropriate computational interpretation methods. A novel data analysis pipeline designed for high-throughput determined metabolomic data is presented. The combination of principal component analysis (PCA) with emergent self-organizing maps (ESOM) and hierarchical cluster analysis (HCA)algorithms is used to unravel the structure underlying metabolomic data sets, including the detection of outliers. Observed differences between various analyzed metabolomes are automatically mapped and visualized using KEGG metabolic pathway maps. This way typical metabolic biomarker for data sets from various analyzed growth conditions and genetic backgrounds become visible. In order to validate the described methods we analyzed time resolved metabolomic datasets obtained for Corynebacterium glutamicum cells grown on various carbon sources consisting of 126 different metabolic patterns. The analysis pipeline was implemented in the user-friendly Java software eSOMet. The software was successfully used for the clustering of the metabolome data mentioned above. Metabolic biomarkers typical for the utilized carbon sources and analyzed growth phases were identified.

Structural and Functional Analysis of Giant Strong Component of Bacillus thuringiensis Metabolic Network

The purpose of this work was to study the giant strong component (GSC) of B. thuringiensis metabolic network by structural and functional analysis. Based on so-called “bow tie” structure, we extracted and studied GSC with its functional significance. Global structural properties such as degree distribution and average path length were computed and indicated that the GSC is also a small-world and scale-free network. Furthermore, the GSC was decomposed and functional significant for metabolism of these divisions were investigated by comparing to KEGG metabolic pathways.

Systems biology approaches and pathway tools for investigating cardiovascular disease

Systems biology aims to understand the nonlinear interactions of multiple biomolecular components that characterize a living organism. One important aspect of systems biology approaches is to identify the biological pathways or networks that connect the differing elements of a system, and examine how they evolve with temporal and environmental changes. The utility of this method becomes clear when applied to multifactorial diseases with complex etiologies, such as inflammatory-related diseases, herein exemplified by atherosclerosis. In this paper, the initial studies in this discipline are reviewed and examined within the context of the development of the field. In addition, several different software tools are briefly described and a novel application for the KEGG database suite called KegArray is presented. This tool is designed for mapping the results of high-throughput omics studies, including transcriptomics, proteomics and metabolomics data, onto interactive KEGG metabolic pathways. The utility of KegArray is demonstrated using a combined transcriptomics and lipidomics dataset from a published study designed to examine the potential of cholesterol in the diet to influence the inflammatory component in the development of atherosclerosis. These data were mapped onto the KEGG PATHWAY database, with a low cholesterol diet affecting 60 distinct biochemical pathways and a high cholesterol exposure affecting 76 biochemical pathways. A total of 77 pathways were differentially affected between low and high cholesterol diets. The KEGG pathways "Biosynthesis of unsaturated fatty acids" and "Sphingolipid metabolism" evidenced multiple changes in gene/lipid levels between low and high cholesterol treatment, and are discussed in detail. Taken together, this paper provides a brief introduction to systems biology and the applications of pathway mapping to the study of cardiovascular disease, as well as a summary of available tools. Current limitations and future visions of this emerging field are discussed, with the conclusion that combining knowledge from biological pathways and high-throughput omics data will move clinical medicine one step further to individualize medical diagnosis and treatment.

EcID. A database for the inference of functional interactions in E. coli

The EcID database (Escherichia coli Interaction Database) provides a framework for the integration of information on functional interactions extracted from the following sources: EcoCyc (metabolic pathways, protein complexes and regulatory information), KEGG (metabolic pathways), MINT and IntAct (protein interactions). It also includes information on protein complexes from the two E. coli high-throughput pull-down experiments and potential interactions extracted from the literature using the web services associated to the iHOP text-mining system. Additionally, EcID incorporates results of various prediction methods, including two protein interaction prediction methods based on genomic information (Phylogenetic Profiles and Gene Neighbourhoods) and three methods based on the analysis of co-evolution (Mirror Tree, In Silico 2 Hybrid and Context Mirror). EcID associates to each prediction a specifically developed confidence score. The two main features that make EcID different from other systems are the combination of co-evolution-based predictions with the experimental data, and the introduction of E. coli-specific information, such as gene regulation information from EcoCyc. The possibilities offered by the combination of the EcID database information are illustrated with a prediction of potential functions for a group of poorly characterized genes related to yeaG. EcID is available online at http://ecid.bioinfo.cnio.es.

DbNEI2.0: building multilayer network for drug–NEI–disease

The neuro-endocrine-immune (NEI) system plays a critical regulatory role in modulating host homeostasis and optimizing health. We created the dbNEI 2 years ago to collect NEI molecules and interactions. For transferring the conceptual NEI to the systematic NEI network and uncovering the NEI's medical function, we updated the dbNEI 2.0 in three ways: (i) extended NEI molecules to 2242 genes and 7657 chemical compounds by using gene ontology-based (GO-based) data mining strategy, (ii) added multilayer interactions of NEI molecules including KEGG signal transduction and metabolic pathways, HPRD protein-protein interactions (PPI), transcription factor and microRNA regulations and (iii) connected 611 drugs and 823 diseases through multilayer NEI interactions. The reconstructed drug-NEI-disease network will facilitate the systematic study of NEI system.

KEGG Atlas mapping for global analysis of metabolic pathways

KEGG Atlas is a new graphical interface to the KEGG suite of databases, especially to the systems information in the PATHWAY and BRITE databases. It currently consists of a single global map and an associated viewer for metabolism, covering about 120 KEGG metabolic pathway maps and about 10 BRITE hierarchies. The viewer allows the user to navigate and zoom the global map under the Ajax technology. The mapping of high-throughput experimental data onto the global map is the main use of KEGG Atlas. In the global metabolism map, the node (circle) is a chemical compound and the edge (line) is a set of reactions linked to a set of KEGG Orthology (KO) entries for enzyme genes. Once gene identifiers in different organisms are converted to the K number identifiers in the KO system, corresponding line segments can be highlighted in the global map, allowing the user to view genome sequence data as organism-specific pathways, gene expression data as up- or down-regulated pathways, etc. Once chemical compounds are converted to the C number identifiers in KEGG, metabolomics data can also be displayed in the global map. KEGG Atlas is available at http://www.genome.jp/kegg/atlas/.

Visualizing Genome Expression and Regulatory Network Dynamics in Genomic and Metabolic Context

AbstractDNA microarrays are used to measure the expression levels of thousands of genes simultaneously. In a time series experiment, the gene expressions are measured as a function of time. We present an application for integrated visualization of genome expression and network dynamics in both regulatory networks and metabolic pathways. Integration of these two levels of cellular processes is necessary, since it provides the link between the measurements at the transcriptional level (gene expression levels approximated from microarray data) and the phenotype (the observable characteristics of an organism) at the functional and behavioral level. The integration requires visualization approaches besides traditional clustering and statistical analysis methods. Our application can (i) visualize the data from time series experiments in the context of a regulatory network and KEGG metabolic pathways; (ii) identify and visualize active regulatory subnetworks from the gene expression data; (iii) perform a statistical test to identify and subsequently visualize pathways that are affected by differentially expressed genes. We present a case study, which demonstrates that our approach and application both facilitates and speeds up data analysis tremendously in comparison to a more traditional approach that involves many manual, laborious, and error‐prone steps.

METABOLIC PATHWAY ANALYSIS OFS. PNEUMONIAE: ANIN SILICOAPPROACH TOWARDS DRUG-DESIGN

The emergence of multidrug resistant varieties of Streptococcus pneumoniae (S. pneumoniae) has led to a search for novel drug targets. An in silico comparative analysis of metabolic pathways of the host Homo sapiens (H. sapiens) and the pathogen S. pneumoniae have been performed. Enzymes from the biochemical pathways of S. pneumoniae from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. Five pathways unique to the pathogen S. pneumoniae when compared to the host H. sapiens have been identified. Potential drug targets from these pathways could be useful for the discovery of broad-spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 161 distinct targets identified from these pathways, many are in various stages of progress at the Microbial Genome Database. However, 44 of the targets are new and can be considered for rational drug design. The study was successful in listing out potential drug targets from the S. pneumoniae proteome involved in vital aspects of the pathogen's metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatics approach can be extended to other pathogens of clinical interest.

EGassembler: online bioinformatics service for large-scale processing, clustering and assembling ESTs and genomic DNA fragments

Expressed sequence tag (EST) sequencing has proven to be an economically feasible alternative for gene discovery in species lacking a draft genome sequence. Ongoing large-scale EST sequencing projects feel the need for bioinformatics tools to facilitate uniform EST handling. This brings about a renewed importance for a universal tool for processing and functional annotation of large sets of ESTs. EGassembler () is a web server, which provides an automated as well as a user-customized analysis tool for cleaning, repeat masking, vector trimming, organelle masking, clustering and assembling of ESTs and genomic fragments. The web server is publicly available and provides the community a unique all-in-one online application web service for large-scale ESTs and genomic DNA clustering and assembling. Running on a Sun Fire 15K supercomputer, a significantly large volume of data can be processed in a short period of time. The results can be used to functionally annotate genes, to facilitate splice alignment analysis, to link the transcripts to genetic and physical maps, design microarray chips, to perform transcriptome analysis and to map to KEGG metabolic pathways. The service provides an excellent bioinformatics tool to research groups in wet-lab as well as an all-in-one-tool for sequence handling to bioinformatics researchers.