Comprehensive Web Server Research Articles

TCRosetta: An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences.

T cells and T-cell receptors (TCRs) are essential components of the adaptive immune system. Characterization of the TCR repertoire offers a promising and highly informative source for understanding the functions of T cells in the immune response and immunotherapy. Although TCR repertoire studies have attracted much attention, there are few online servers available for TCR repertoire analysis, especially for TCR sequence annotation or advanced analyses. Therefore, we developed TCRosetta, a comprehensive online server that integrates analytical methods for TCR repertoire analysis and visualization. TCRosetta combines general feature analysis, large-scale sequence clustering, network construction, peptide-TCR binding prediction, generation probability calculation, and k-mer motif analysis for TCR sequences, making TCR data analysis as simple as possible. The TCRosetta server accepts multiple input data formats and can analyze ∼ 20,000 TCR sequences in less than 3 min. TCRosetta is the most comprehensive web server available for TCR repertoire analysis and is freely available at https://guolab.wchscu.cn/TCRosetta/.

OBMeta: a comprehensive web server to analyze and validate gut microbial features and biomarkers for obesity-associated metabolic diseases.

Gut dysbiosis is closely associated with obesity and related metabolic diseases including type 2 diabetes (T2D) and nonalcoholic fatty liver disease (NAFLD). The gut microbial features and biomarkers have been increasingly investigated in many studies, which require further validation due to the limited sample size and various confounding factors that may affect microbial compositions in a single study. So far, it lacks a comprehensive bioinformatics pipeline providing automated statistical analysis and integrating multiple independent studies for cross-validation simultaneously. OBMeta aims to streamline the standard metagenomics data analysis from diversity analysis, comparative analysis, and functional analysis to co-abundance network analysis. In addition, a curated database has been established with a total of 90 public research projects, covering three different phenotypes (Obesity, T2D, and NAFLD) and more than five different intervention strategies (exercise, diet, probiotics, medication, and surgery). With OBMeta, users can not only analyze their research projects but also search and match public datasets for cross-validation. Moreover, OBMeta provides cross-phenotype and cross-intervention-based advanced validation that maximally supports preliminary findings from an individual study. To summarize, OBMeta is a comprehensive web server to analyze and validate gut microbial features and biomarkers for obesity-associated metabolic diseases. OBMeta is freely available at: http://obmeta.met-bioinformatics.cn/.

Ori-Finder 2022: A Comprehensive Web Server for Prediction and Analysis of Bacterial Replication Origins

The replication of DNA is a complex biological process that is essential for life. Bacterial DNA replication is initiated at genomic loci referred to as replication origins (oriCs). Integrating the Z-curve method, DnaA box distribution, and comparative genomic analysis, we developed a web server to predict bacterial oriCs in 2008 called Ori-Finder, which is helpful to clarify the characteristics of bacterial oriCs. The oriCs of hundreds of sequenced bacterial genomes have been annotated in the genome reports using Ori-Finder and the predicted results have been deposited in DoriC, a manually curated database of oriCs. This has facilitated large-scale data mining of functional elements in oriCs and strand-biased analysis. Here, we describe Ori-Finder 2022 with updated prediction framework, interactive visualization module, new analysis module, and user-friendly interface. More species-specific indicator genes and functional elements of oriCs are integrated into the updated framework, which has also been redesigned to predict oriCs in draft genomes. The interactive visualization module displays more genomic information related to oriCs and their functional elements. The analysis module includes regulatory protein annotation, repeat sequence discovery, homologous oriC search, and strand-biased analyses. The redesigned interface provides additional customization options for oriC prediction. Ori-Finder 2022 is freely available at http://tubic.tju.edu.cn/Ori-Finder/ and https://tubic.org/Ori-Finder/.

FUNAGE-Pro: comprehensive web server for gene set enrichment analysis of prokaryotes.

Recent advances in the field of high throughput (meta-)transcriptomics and proteomics call for easy and rapid methods enabling to explore not only single genes or proteins but also extended biological systems. Gene set enrichment analysis is commonly used to find relations in a set of genes and helps to uncover the biological meaning in results derived from high-throughput data. The basis for gene set enrichment analysis is a solid functional classification of genes. Here, we describe a comprehensive database containing multiple functional classifications of genes of all (>55 000) publicly available complete bacterial genomes. In addition to the most common functional classes such as COG and GO, also KEGG, InterPro, PFAM, eggnog and operon classes are supported. As classification data for features is often not available, we offer fast annotation and classification of proteins in any newly sequenced bacterial genome. The web server FUNAGE-Pro enables fast functional analysis on single gene sets, multiple experiments, time series data, clusters, and gene network modules for any prokaryote species or strain. FUNAGE-Pro is freely available at http://funagepro.molgenrug.nl.

PCGA: a comprehensive web server for phenotype-cell-gene association analysis.

Most complex disease-associated loci mapped by genome-wide association studies (GWAS) are located in non-coding regions. It remains elusive which genes the associated loci regulate and in which tissues/cell types the regulation occurs. Here, we present PCGA (https://pmglab.top/pcga), a comprehensive web server for jointly estimating both associated tissues/cell types and susceptibility genes for complex phenotypes by GWAS summary statistics. The web server is built on our published method, DESE, which represents an effective method to mutually estimate driver tissues and genes by integrating GWAS summary statistics and transcriptome data. By collecting and processing extensive bulk and single-cell RNA sequencing datasets, PCGA has included expression profiles of 54 human tissues, 2,214 human cell types and 4,384 mouse cell types, which provide the basis for estimating associated tissues/cell types and genes for complex phenotypes. We develop a framework to sequentially estimate associated tissues and cell types of a complex phenotype according to their hierarchical relationships we curated. Meanwhile, we construct a phenotype-cell-gene association landscape by estimating the associated tissues/cell types and genes of 1,871 public GWASs. The association landscape is generally consistent with biological knowledge and can be searched and browsed at the PCGA website.

Aquaculture Molecular Breeding Platform (AMBP): a comprehensive web server for genotype imputation and genetic analysis in aquaculture.

It is of vital importance to understand the population structure, dissect the genetic bases of performance traits, and make proper strategies for selection in breeding programs. However, there is no single webserver covering the specific needs in aquaculture. We present Aquaculture Molecular Breeding Platform (AMBP), the first web server for genetic data analysis in aquatic species of farming interest. AMBP integrates the haplotype reference panels of 18 aquaculture species, which greatly improves the accuracy of genotype imputation. It also supports multiple tools to infer genetic structures, dissect the genetic architecture of performance traits, estimate breeding values, and predict optimum contribution. All the tools are coherently linked in a web-interface for users to generate interpretable results and evaluate statistical appropriateness. The webserver supports standard VCF and PLINK (PED, MAP) files, and implements automated pipelines for format transformation and visualization to simplify the process of analysis. As a demonstration, we applied the webserver to Pacific white shrimp and Atlantic salmon datasets. In summary, AMBP constitutes comprehensive resources and analytical tools for exploring genetic data and guiding practical breeding programs. AMBP is available at http://mgb.qnlm.ac.

ProfKin: A comprehensive web server for structure-based kinase profiling.

Protein kinases are central mediators of signal-transduction cascades and attractive drug targets for therapeutic intervention. Since kinases are structurally and mechanistically related to each other, kinase inhibitor selectivity is often investigated by kinase profiling and considered as an important index for drug discovery. We here describe a versatile web server termed ProfKin for structure-based kinase profiling, which is based on a kinase-ligand focused database (KinLigDB). It provides all ready-to-use 3D structure coordinates of 4219 kinase-ligand complex structures covering 297 human kinases and the associated information, particularly including binding site type, binding ligand type, interaction fingerprints, downstream molecules and related human diseases. The web server works via predicting possible binding modes for the query molecule, prioritizing the binding modes guided by an interaction fingerprint analysis method, and giving a list of ranked kinases by a comprehensive index. Users can freely select entire or part of the KinLigDB database, e.g. via subfamily and binding site type, to customize the profiling contents. The superimpositions of the predicted binding poses of the query molecule with reference binding modes can be visually inspected on the website. The additional classification attributes and phylogenetic tree are also given for each top-ranked kinase.

Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning.

Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users’ capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2.

Abstract 3217: An integrated web server for multi-omics data deconvolution

Abstract Tissue data deconvolution has been broadly utilized to characterize the variation of cell components and their relative abundance in cancer tumor microenvironment. Numerous deconvolution methods have been developed with different mathematical and biological considerations. Our previous studies have derived a rigorous mathematical condition for a less biased deconvolution. We have demonstrated a cell type can be reliably inferred from an omics data only of the cell type is with distinct signature genes, i.e., the cell type specific expression signature of the selected marker genes of all cell types are orthogonal or at least non-positively associated with each other, which is named as strong or weak “identifiable” conditions. Our analysis suggests the identifiability of a certain cell type is mostly determined by the significance of its gene markers, which is data set and tissue microenvironment dependent. However, such data set specificity has been commonly ignored in existing methods. In this study, we developed a comprehensive web server, namely ISMD (an Integrated web Server for Multi-omics data Deconvolution), to recommend a best deconvolution method and optimize the gene markers and cell types in analyzing a specific data set. The server solved to two fundamental challenges in the practical application of deconvolution methods, namely (1) method selection and (2) parameter optimization. Specifically, ISMD utilized a semi-supervised approach for method recommendation and parameter optimization, which first evaluate the species, sample size, and tissue type of a given data set and test the low rank structure of commonly used cell type specific markers for cell type and signature gene selection. ISMD can handle the blood, normal, inflammatory, and cancer tissue, and central nervous and hematopoietic systems of human and mouse. The semi-supervised knowledge transfer encoded in ISMD enables its application to multi-omics data including general transcriptomics, ATAC-seq and DNA-methylation data. The method has been validated by using a large collection of tissue samples. A user friend web-server has been released through https://github.com/xiaoyulu95/ISMD. We anticipate ISMD can help to increase the specificity and rigorousness in the application of tissue deconvolution methods, to bring more insights of the role of different cell types and their interactions and stimulate the discovery of new therapeutic targets in a tumor micro-environment. Citation Format: Xiaoyu Lu, Pengtao Dang, Szu-Wei Tu, Wennan Chang, Changlin Wan, Chi Zhang, Sha Cao. An integrated web server for multi-omics data deconvolution [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3217.

RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule.

By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook.Database URL: http://bioinfor.imu.edu.cn/raacbook

CircadiOmics: circadian omic web portal.

Circadian rhythms play a fundamental role at all levels of biological organization. Understanding the mechanisms and implications of circadian oscillations continues to be the focus of intense research. However, there has been no comprehensive and integrated way for accessing and mining all circadian omic datasets. The latest release of CircadiOmics (http://circadiomics.ics.uci.edu) fills this gap for providing the most comprehensive web server for studying circadian data. The newly updated version contains high-throughput 227 omic datasets corresponding to over 74 million measurements sampled over 24 h cycles. Users can visualize and compare oscillatory trajectories across species, tissues and conditions. Periodicity statistics (e.g. period, amplitude, phase, P-value, q-value etc.) obtained from BIO_CYCLE and other methods are provided for all samples in the repository and can easily be downloaded in the form of publication-ready figures and tables. New features and substantial improvements in performance and data volume make CircadiOmics a powerful web portal for integrated analysis of circadian omic data.

ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins.

BackgroundProinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1β, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response.ResultsA total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %.ConclusionThe amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-016-0928-3) contains supplementary material, which is available to authorized users.

DiseaseConnect: a comprehensive web server for mechanism-based disease–disease connections

The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms. Connecting diseases with similar pathological mechanisms could inspire novel strategies on the effective repositioning of existing drugs and therapies. Although there have been several studies attempting to generate disease connectivity networks, they have not yet utilized the enormous and rapidly growing public repositories of disease-related omics data and literature, two primary resources capable of providing insights into disease connections at an unprecedented level of detail. Our DiseaseConnect, the first public web server, integrates comprehensive omics and literature data, including a large amount of gene expression data, Genome-Wide Association Studies catalog, and text-mined knowledge, to discover disease–disease connectivity via common molecular mechanisms. Moreover, the clinical comorbidity data and a comprehensive compilation of known drug–disease relationships are additionally utilized for advancing the understanding of the disease landscape and for facilitating the mechanism-based development of new drug treatments.

DMINDA: an integrated web server for DNA motif identification and analyses.

DMINDA (DNA motif identification and analyses) is an integrated web server for DNA motif identification and analyses, which is accessible at http://csbl.bmb.uga.edu/DMINDA/. This web site is freely available to all users and there is no login requirement. This server provides a suite of cis-regulatory motif analysis functions on DNA sequences, which are important to elucidation of the mechanisms of transcriptional regulation: (i) de novo motif finding for a given set of promoter sequences along with statistical scores for the predicted motifs derived based on information extracted from a control set, (ii) scanning motif instances of a query motif in provided genomic sequences, (iii) motif comparison and clustering of identified motifs, and (iv) co-occurrence analyses of query motifs in given promoter sequences. The server is powered by a backend computer cluster with over 150 computing nodes, and is particularly useful for motif prediction and analyses in prokaryotic genomes. We believe that DMINDA, as a new and comprehensive web server for cis-regulatory motif finding and analyses, will benefit the genomic research community in general and prokaryotic genome researchers in particular.

MirTools: microRNA profiling and discovery based on high-throughput sequencing

miRNAs are small, non-coding RNA that negatively regulate gene expression at post-transcriptional level, which play crucial roles in various physiological and pathological processes, such as development and tumorigenesis. Although deep sequencing technologies have been applied to investigate various small RNA transcriptomes, their computational methods are far away from maturation as compared to microarray-based approaches. In this study, a comprehensive web server mirTools was developed to allow researchers to comprehensively characterize small RNA transcriptome. With the aid of mirTools, users can: (i) filter low-quality reads and 3/5′ adapters from raw sequenced data; (ii) align large-scale short reads to the reference genome and explore their length distribution; (iii) classify small RNA candidates into known categories, such as known miRNAs, non-coding RNA, genomic repeats and coding sequences; (iv) provide detailed annotation information for known miRNAs, such as miRNA/miRNA*, absolute/relative reads count and the most abundant tag; (v) predict novel miRNAs that have not been characterized before; and (vi) identify differentially expressed miRNAs between samples based on two different counting strategies: total read tag counts and the most abundant tag counts. We believe that the integration of multiple computational approaches in mirTools will greatly facilitate current microRNA researches in multiple ways. mirTools can be accessed at http://centre.bioinformatics.zj.cn/mirtools/ and http://59.79.168.90/mirtools.

The Use of Artificial Neural Networks in Analysis Cationic Trypsinogen Gene and Hepatitis B Surface Antigen

Problem statement: More and more severe hepatitis cases reported in China are infected with HBV and the immune response of HBV will be reduced if mutations occur in the TCR, therefore it is very important to investigate the relation of T-cellular function and clinical effect by studying the function of T-cell receptor. Approach: Artificial Neural Networks (ANN) was applied to analyze basic data (the three structural of HBsAg, the ligand of HBsAg and the clinical immunological characterizations, the laboratory data, the genetypes of cationic trypsinogen gene (PRSS1) derived from 78 patients with pancreatitis and 60 normal controls were also collected. What is more, we used T-cell culture with HBV and flow cytometry to check the result of ANN predict. To examine the characteristics of T-cells capable of coexisting with the secreted HBsAg, T-cell receptor from A121T, C139S, silent mutation and normal PRSS1 gene in the patients with pancreatitis were put into research. To ensure that PRSS1 gene would affect HBsAg-specific T-cells receptor, we compared the rate of multiplication and CD4/CD8 of T-cell after culture with HBV at 0H, 12H, 24H, 36H, 48H and 72H time point. Results: The protein’s structural predicted by the ANN could specifically explain the phenomenon that the turbulence and different of anti-HBs lever of the patients with pancreatitis. The three-dimensional of the protein that consist with PRSS1 gene would accord with HBsAg. It may be one of the HBsAg-specific T-cell receptor. Result of T-cell culture also showed that different genetypes of PRSS1 had different results. In the T-cell proliferation, the groups of PRSS1 mutation (A121T and C139S) were significant lower than the group of silent mutation and normal controls and it was the same to the result of CD4/CD8. Conclusion: The ANN had been integrated into a previously published comprehensive web server to support immunology analysis and the PRSS1 gene may be the unit for HBsAg immune response.

Combined differential gene expression profile and pathway enrichment analyses to elucidate the molecular mechanisms of uterine leiomyoma after gonadotropin-releasing hormone treatment

Composite regulatory signature database (CRSD), a self-developed comprehensive Web server for composite regulatory signature discovery, used to compare the published microarray data with our data on patients with uterine leiomyoma treated with or without GnRH analogue (GnRH-a), revealed that the focal adhesion, mitogen-activated protein kinase (MAPK), CXC chemokine receptor 4/stromal-derived factor-1 (CXCR4/SDF-1), T-cell receptor, integrin, vascular endothelial growth factor (VEGF), GnRH, and transforming growth factor-beta (TGF-beta) signaling pathways are highly expressed in uterine leiomyoma and significantly down-regulated after GnRH-a treatment. According to the results these signaling pathways could be involved in inflammation, proliferation, and remodeling processes of leiomyoma development and possibly in the regression of leiomyoma after GnRH-a treatment, which might improve our understanding of the mechanisms of leiomyoma formation and help us to find novel drug targets or specific markers for diagnosis and prognosis in uterine leiomyoma.

MYBS: a comprehensive web server for mining transcription factor binding sites in yeast

Correct interactions between transcription factors (TFs) and their binding sites (TFBSs) are of central importance to gene regulation. Recently developed chromatin-immunoprecipitation DNA chip (ChIP-chip) techniques and the phylogenetic footprinting method provide ways to identify TFBSs with high precision. In this study, we constructed a user-friendly interactive platform for dynamic binding site mapping using ChIP-chip data and phylogenetic footprinting as two filters. MYBS (Mining Yeast Binding Sites) is a comprehensive web server that integrates an array of both experimentally verified and predicted position weight matrixes (PWMs) from eleven databases, including 481 binding motif consensus sequences and 71 PWMs that correspond to 183 TFs. MYBS users can search within this platform for motif occurrences (possible binding sites) in the promoters of genes of interest via simple motif or gene queries in conjunction with the above two filters. In addition, MYBS enables users to visualize in parallel the potential regulators for a given set of genes, a feature useful for finding potential regulatory associations between TFs. MYBS also allows users to identify target gene sets of each TF pair, which could be used as a starting point for further explorations of TF combinatorial regulation. MYBS is available at http://cg1.iis.sinica.edu.tw/~mybs/.

CRSD: a comprehensive web server for composite regulatory signature discovery

Transcription factors (TFs) and microRNAs play important roles in the regulation of human gene expression, and the study of their combinatory regulations of gene expression is a new research field. We constructed a comprehensive web server, the composite regulatory signature database (CRSD), that can be applied in investigating complex regulatory behaviors involving gene expression signatures (GESs), microRNA regulatory signatures (MRSs) and TF regulatory signatures (TRSs). Six well-known and large-scale databases, including the human UniGene, mature microRNAs, putative promoter, TRANSFAC, pathway and Gene Ontology (GO) databases, were integrated to provide the comprehensive analysis in CRSD. Two new genome-wide databases, of MRSs and TRSs, were also constructed and further integrated into CRSD. To accomplish the microarray data analysis at one go, several methods, including microarray data pretreatment, statistical and clustering analysis, iterative enrichment analysis and motif discovery, were closely integrated in the web server, which has not been the case in previous studies. Our implementation showed that the published literature could demonstrate the results of genome-wide enrichment analysis. We conclude that CRSD is a powerful and useful bioinformatic web server and may provide new insights into gene regulation networks. CRSD and the online tutorial are publicly available at .

PlasMapper: a web server for drawing and auto-annotating plasmid maps

PlasMapper is a comprehensive web server that automatically generates and annotates high-quality circular plasmid maps. Taking only the plasmid/vector DNA sequence as input, PlasMapper uses sequence pattern matching and BLAST alignment to automatically identify and label common promoters, terminators, cloning sites, restriction sites, reporter genes, affinity tags, selectable marker genes, replication origins and open reading frames. PlasMapper then presents the identified features in textual form and as high-resolution, multicolored graphical output. The appearance and contents of the output can be customized in numerous ways using several supplied options. Further, PlasMapper images can be rendered in both rasterized (PNG and JPG) and vector graphics (SVG) formats to accommodate a variety of user needs or preferences. The images and textual output are of sufficient quality that they may be used directly in publications or presentations. The PlasMapper web server is freely accessible at http://wishart.biology.ualberta.ca/PlasMapper.