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At least 1,495 records · Page 83Linked to original sources

PubMed2004

SeqVISTA: a new module of integrated computational tools for studying transcriptional regulation.

Transcriptional regulation is one of the most basic regulatory mechanisms in the cell. The accumulation of multiple metazoan genome sequences and the advent of high-throughput experimental techniques have motivated the development of a large number of bioinformatics methods for the detection of regulatory motifs. The regulatory process is extremely complex and individual computational algorithms typically have very limited success in genome-scale studies. Here, we argue the importance of integrating multiple computational algorithms and present an infrastructure that integrates eight web services covering key areas of transcriptional regulation. We have adopted the client-side integration technology and built a consistent input and output environment with a versatile visualization tool named SeqVISTA. The infrastructure will allow for easy integration of gene regulation analysis software that is scattered over the Internet. It will also enable bench biologists to perform an arsenal of analysis using cutting-edge methods in a familiar environment and bioinformatics researchers to focus on developing new algorithms without the need to invest substantial effort on complex pre- or post-processors. SeqVISTA is freely available to academic users and can be launched online at http://zlab.bu.edu/SeqVISTA/web.jnlp, provided that Java Web Start has been installed. In addition, a stand-alone version of the program can be downloaded and run locally. It can be obtained at http://zlab.bu.edu/SeqVISTA.

Algorithms↗

PubMed2005

BRIGEP--the BRIDGE-based genome-transcriptome-proteome browser.

The growing amount of information resulting from the increasing number of publicly available genomes and experimental results thereof necessitates the development of comprehensive systems for data processing and analysis. In this paper, we describe the current state and latest developments of our BRIGEP bioinformatics software system consisting of three web-based applications: GenDB, EMMA and ProDB. These applications facilitate the processing and analysis of bacterial genome, transcriptome and proteome data and are actively used by numerous international groups. We are currently in the process of extensively interconnecting these applications. BRIGEP was developed in the Bioinformatics Resource Facility of the Center for Biotechnology at Bielefeld University and is freely available. A demo project with sample data and access to all three tools is available at https://www.cebitec.uni-bielefeld.de/groups/brf/software/brigep/. Code bundles for these and other tools developed in our group are accessible on our FTP server at ftp.cebitec.uni-bielefeld.de/pub/software/.

Bacterial Proteins↗

PubMed2005

sRNAPredict: an integrative computational approach to identify sRNAs in bacterial genomes.

Small non-coding bacterial RNAs (sRNAs) play important regulatory roles in a variety of cellular processes. Nearly all known sRNAs have been identified in Escherichia coli and most of these are not conserved in the majority of other bacterial species. Many of the E.coli sRNAs were initially predicted through bioinformatic approaches based on their common features, namely that they are encoded between annotated open reading frames and are flanked by predictable transcription signals. Because promoter consensus sequences are undetermined for most species, the successful use of bioinformatics to identify sRNAs in bacteria other than E.coli has been limited. We have created a program, sRNAPredict, which uses coordinate-based algorithms to integrate the respective positions of individual predictive features of sRNAs and rapidly identify putative intergenic sRNAs. Relying only on sequence conservation and predicted Rho-independent terminators, sRNAPredict was used to search for sRNAs in Vibrio cholerae. This search identified 9 of the 10 known or putative V.cholerae sRNAs and 32 candidates for novel sRNAs. Small transcripts for 6 out of 9 candidate sRNAs were observed by Northern analysis. Our findings suggest that sRNAPredict can be used to efficiently identify novel sRNAs even in bacteria for which promoter consensus sequences are not available.

Algorithms↗

PubMed2006

ChimerDB--a knowledgebase for fusion sequences.

Chromosome translocation and gene fusion are frequent events in the human genome and are often the cause of many types of tumor. ChimerDB is the database of fusion sequences encompassing bioinformatics analysis of mRNA and expressed sequence tag (EST) sequences in the GenBank, manual collection of literature data and integration with other known database such as OMIM. Our bioinformatics analysis identifies the fusion transcripts that have non-overlapping alignments at multiple genomic loci. Fusion events at exon-exon borders are selected to filter out the cloning artifacts in cDNA library preparation. The result is classified into two groups--genuine chromosome translocation and fusion between neighboring genes owing to intergenic splicing. We also integrated manually collected literature and OMIM data for chromosome translocation as an aid to assess the validity of each fusion event. The database is available at http://genome.ewha.ac.kr/ChimerDB/ for human, mouse and rat genomes.

Animals↗

PubMed2006

Nuclear Receptor Signaling Atlas (www.nursa.org): hyperlinking the nuclear receptor signaling community.

The nuclear receptor signaling (NRS) field has generated a substantial body of information on nuclear receptors, their ligands and coregulators, with the ultimate goal of constructing coherent models of the biological and clinical significance of these molecules. As a component of the Nuclear Receptor Signaling Atlas (NURSA)--the development of a functional atlas of nuclear receptor biology--the NURSA Bioinformatics Resource is developing a strategy to organize and integrate legacy and future information on these molecules in a single web-based resource (www.nursa.org). This entails parallel efforts of (i) developing an appropriate software framework for handling datasets from NURSA laboratories and (ii) designing strategies for the curation and presentation of public data relevant to NRS. To illustrate our approach, we have described here in detail the development of a web-based interface for the NURSA quantitative PCR nuclear receptor expression dataset, incorporating bioinformatics analysis which provides novel perspectives on functional relationships between these molecules. We anticipate that the free and open access of the community to a platform for data mining and hypothesis generation strategies will be a significant contribution to the progress of research in this field.

Animals↗

PubMed2006

PUMA2--grid-based high-throughput analysis of genomes and metabolic pathways.

The PUMA2 system (available at http://compbio.mcs.anl.gov/puma2) is an interactive, integrated bioinformatics environment for high-throughput genetic sequence analysis and metabolic reconstructions from sequence data. PUMA2 provides a framework for comparative and evolutionary analysis of genomic data and metabolic networks in the context of taxonomic and phenotypic information. Grid infrastructure is used to perform computationally intensive tasks. PUMA2 currently contains precomputed analysis of 213 prokaryotic, 22 eukaryotic, 650 mitochondrial and 1493 viral genomes and automated metabolic reconstructions for >200 organisms. Genomic data is annotated with information integrated from >20 sequence, structural and metabolic databases and ontologies. PUMA2 supports both automated and interactive expert-driven annotation of genomes, using a variety of publicly available bioinformatics tools. It also contains a suite of unique PUMA2 tools for automated assignment of gene function, evolutionary analysis of protein families and comparative analysis of metabolic pathways. PUMA2 allows users to submit batch sequence data for automated functional analysis and construction of metabolic models. The results of these analyses are made available to the users in the PUMA2 environment for further interactive sequence analysis and annotation.

Computational Biology↗

PubMed2006

The International Gene Trap Consortium Website: a portal to all publicly available gene trap cell lines in mouse.

Gene trapping is a method of generating murine embryonic stem (ES) cell lines containing insertional mutations in known and novel genes. A number of international groups have used this approach to create sizeable public cell line repositories available to the scientific community for the generation of mutant mouse strains. The major gene trapping groups worldwide have recently joined together to centralize access to all publicly available gene trap lines by developing a user-oriented Website for the International Gene Trap Consortium (IGTC). This collaboration provides an impressive public informatics resource comprising approximately 45 000 well-characterized ES cell lines which currently represent approximately 40% of known mouse genes, all freely available for the creation of knockout mice on a non-collaborative basis. To standardize annotation and provide high confidence data for gene trap lines, a rigorous identification and annotation pipeline has been developed combining genomic localization and transcript alignment of gene trap sequence tags to identify trapped loci. This information is stored in a new bioinformatics database accessible through the IGTC Website interface. The IGTC Website (www.genetrap.org) allows users to browse and search the database for trapped genes, BLAST sequences against gene trap sequence tags, and view trapped genes within biological pathways. In addition, IGTC data have been integrated into major genome browsers and bioinformatics sites to provide users with outside portals for viewing this data. The development of the IGTC Website marks a major advance by providing the research community with the data and tools necessary to effectively use public gene trap resources for the large-scale characterization of mammalian gene function.

Animals↗

PubMed2006

PseudoViewer: web application and web service for visualizing RNA pseudoknots and secondary structures.

Visualizing RNA secondary structures and pseudoknot structures is essential to bioinformatics systems that deal with RNA structures. However, many bioinformatics systems use heterogeneous data structures and incompatible software components, so integration of software components (including a visualization component) into a system can be hindered by incompatibilities between the components of the system. This paper presents an XML web service and web application program for visualizing RNA secondary structures with pseudoknots. Experimental results show that the PseudoViewer web service and web application are useful for resolving many problems with incompatible software components as well as for visualizing large-scale RNA secondary structures with pseudoknots of any type. The web service and web application are available at http://pseudoviewer.inha.ac.kr/.

Computational Biology↗

PubMed2006

PATRIC: the VBI PathoSystems Resource Integration Center.

The PathoSystems Resource Integration Center (PATRIC) is one of eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infection Diseases (NIAID) to create a data and analysis resource for selected NIAID priority pathogens, specifically proteobacteria of the genera Brucella, Rickettsia and Coxiella, and corona-, calici- and lyssaviruses and viruses associated with hepatitis A and E. The goal of the project is to provide a comprehensive bioinformatics resource for these pathogens, including consistently annotated genome, proteome and metabolic pathway data to facilitate research into counter-measures, including drugs, vaccines and diagnostics. The project's curation strategy has three prongs: 'breadth first' beginning with whole-genome and proteome curation using standardized protocols, a 'targeted' approach addressing the specific needs of researchers and an integrative strategy to leverage high-throughput experimental data (e.g. microarrays, proteomics) and literature. The PATRIC infrastructure consists of a relational database, analytical pipelines and a website which supports browsing, querying, data visualization and the ability to download raw and curated data in standard formats. At present, the site warehouses complete sequences for 17 bacterial and 332 viral genomes. The PATRIC website (https://patric.vbi.vt.edu) will continually grow with the addition of data, analysis and functionality over the course of the project.

Bioterrorism↗

PubMed2006

TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels.

TransportDB (http://www.membranetransport.org/) is a comprehensive database resource of information on cytoplasmic membrane transporters and outer membrane channels in organisms whose complete genome sequences are available. The complete set of membrane transport systems and outer membrane channels of each organism are annotated based on a series of experimental and bioinformatic evidence and classified into different types and families according to their mode of transport, bioenergetics, molecular phylogeny and substrate specificities. User-friendly web interfaces are designed for easy access, query and download of the data. Features of the TransportDB website include text-based and BLAST search tools against known transporter and outer membrane channel proteins; comparison of transporter and outer membrane channel contents from different organisms; known 3D structures of transporters, and phylogenetic trees of transporter families. On individual protein pages, users can find detailed functional annotation, supporting bioinformatic evidence, protein/DNA sequences, publications and cross-referenced external online resource links. TransportDB has now been in existence for over 10 years and continues to be regularly updated with new evidence and data from newly sequenced genomes, as well as having new features added periodically.

Bacterial Outer Membrane Proteins↗

PubMed2006

The Universal Protein Resource (UniProt).

The ability to store and interconnect all available information on proteins is crucial to modern biological research. Accordingly, the Universal Protein Resource (UniProt) plays an increasingly important role by providing a stable, comprehensive, freely accessible central resource on protein sequences and functional annotation. UniProt is produced by the UniProt Consortium, formed in 2002 by the European Bioinformatics Institute (EBI), the Protein Information Resource (PIR) and the Swiss Institute of Bioinformatics (SIB). The core activities include manual curation of protein sequences assisted by computational analysis, sequence archiving, development of a user-friendly UniProt web site and the provision of additional value-added information through cross-references to other databases. UniProt is comprised of three major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase and the UniProt Reference Clusters. An additional component consisting of metagenomic and environmental sequences has recently been added to UniProt to ensure availability of such sequences in a timely fashion. UniProt is updated and distributed on a bi-weekly basis and can be accessed online for searches or download at http://www.uniprot.org.

Amino Acid Sequence↗

PubMed2006

The National Microbial Pathogen Database Resource (NMPDR): a genomics platform based on subsystem annotation.

The National Microbial Pathogen Data Resource (NMPDR) (http://www.nmpdr.org) is a National Institute of Allergy and Infections Disease (NIAID)-funded Bioinformatics Resource Center that supports research in selected Category B pathogens. NMPDR contains the complete genomes of approximately 50 strains of pathogenic bacteria that are the focus of our curators, as well as >400 other genomes that provide a broad context for comparative analysis across the three phylogenetic Domains. NMPDR integrates complete, public genomes with expertly curated biological subsystems to provide the most consistent genome annotations. Subsystems are sets of functional roles related by a biologically meaningful organizing principle, which are built over large collections of genomes; they provide researchers with consistent functional assignments in a biologically structured context. Investigators can browse subsystems and reactions to develop accurate reconstructions of the metabolic networks of any sequenced organism. NMPDR provides a comprehensive bioinformatics platform, with tools and viewers for genome analysis. Results of precomputed gene clustering analyses can be retrieved in tabular or graphic format with one-click tools. NMPDR tools include Signature Genes, which finds the set of genes in common or that differentiates two groups of organisms. Essentiality data collated from genome-wide studies have been curated. Drug target identification and high-throughput, in silico, compound screening are in development.

Bacteria↗

PubMed2005

Identification and characterization of the peroxiredoxin gene family in chickens.

Peroxiredoxin (PRX) is a crucial antioxidant protein that protects against endogenously produced peroxides in prokaryotes to eukaryotes. To date, 6 different isoforms have been identified in mammals. In this study, we describe the first members of the PRX protein family to be characterized in Chicken. Through bioinformatics analysis, we observed that at least 4 different classes of PRX protein have been evolutionarily conserved in chickens. Furthermore, in vitro functional assays of the candidate chicken PRX proteins demonstrated that they had levels of antioxidant activity similar to those of the mammalian enzymes. The expression patterns of the PRX transcript in several chicken tissues were not tissue specific, suggesting that they might play an essential role as a housekeeping gene in all tissues to protect against oxidative damage. In conclusion, the sequences of the putative members of this functional gene family in chickens could be effectively retrieved in silico through bioinformatics analysis, and the functionality of their gene products evaluated by in vitro comparative assay.

Animals↗

PubMed2006

Multiple approaches to a complete inventory of Pseudomonas syringae pv. tomato DC3000 type III secretion system effector proteins.

Pseudomonas syringae pv. tomato DC3000 is a pathogen of tomato and Arabidopsis that translocates virulence effector proteins into host cells via a type III secretion system (T3SS). Many effector-encoding hypersensitive response and pathogenicity (Hrp) outer protein (hop) genes have been identified previously in DC3000 using bioinformatic methods based on Hrp promoter sequences and characteristic N-terminal amino acid patterns that are associated with T3SS substrates. To approach completion of the Hop/effector inventory in DC3000, 44 additional candidates were tested by the Bordetella pertussis calmodulin-dependent adenylate cyclase (Cya) translocation reporter assay; 10 of the high-probability candidates were confirmed as T3SS substrates. Several previously predicted hop genes were tested for their ability to be expressed in an HrpL-dependent manner in culture or to be expressed in planta. The data indicate that DC3000 harbors 53 hop/avr genes and pseudogenes (encoding both injected effectors and T3SS substrates that probably are released to the apoplast); 33 of these genes are likely functional in DC3000, 12 are nonfunctional members of valid Hop families, and 8 are less certain regarding their production at functional levels. Growth of DC3000 in tomato and Arabidopsis Col-0 was not impaired by constitutive expression of repaired versions of two hops that were disrupted naturally by transposable elements or of hop genes that are naturally cryptic. In summary, DC3000 carries a complex mixture of active and inactive hop genes, and the hop genes in P. syringae can be identified efficiently by bioinformatic methods; however, a precise inventory of the subset of Hops that are important in pathogenesis awaits more knowledge based on mutant phenotypes and functions within plants.

Adenylate Cyclase Toxin↗

PubMed2003

A global analysis of protein expression profiles in Sinorhizobium meliloti: discovery of new genes for nodule occupancy and stress adaptation.

A proteomic examination of Sinorhizobium meliloti strain 1021 was undertaken using a combination of 2-D gel electrophoresis, peptide mass fingerprinting, and bioinformatics. Our goal was to identify (i) putative symbiosis- or nutrient-stress-specific proteins, (ii) the biochemical pathways active under different conditions, (iii) potential new genes, and (iv) the extent of posttranslational modifications of S. meliloti proteins. In total, we identified the protein products of 810 genes (13.1% of the genome's coding capacity). The 810 genes generated 1,180 gene products, with chromosomal genes accounting for 78% of the gene products identified (18.8% of the chromosome's coding capacity). The activity of 53 metabolic pathways was inferred from bioinformatic analysis of proteins with assigned Enzyme Commission numbers. Of the remaining proteins that did not encode enzymes, ABC-type transporters composed 12.7% and regulatory proteins 3.4% of the total. Proteins with up to seven transmembrane domains were identified in membrane preparations. A total of 27 putative nodule-specific proteins and 35 nutrient-stress-specific proteins were identified and used as a basis to define genes and describe processes occurring in S. meliloti cells in nodules and under stress. Several nodule proteins from the plant host were present in the nodule bacteria preparations. We also identified seven potentially novel proteins not predicted from the DNA sequence. Post-translational modifications such as N-terminal processing could be inferred from the data. The posttranslational addition of UMP to the key regulator of nitrogen metabolism, PII, was demonstrated. This work demonstrates the utility of combining mass spectrometry with protein arraying or separation techniques to identify candidate genes involved in important biological processes and niche occupations that may be intransigent to other methods of gene expression profiling.

Adaptation, Physiological↗

PubMed2003

Expression profiling in pancreatic cancer research: the initial steps and the road ahead.

A part of living in a new and exciting era for the biologic sciences and medicine, novel high throughput tools allow exploration of the human genome in an unprecedented manner. This "information revolution" is fueled by the study of genome-wide expression profiles for complex biologic and pathophysiologic conditions using DNA arrays, as well as the development and use of robust bioinformatic algorithms. Meticulous translational experiments are becoming possible because of the development of efficient DNA printing technology for producing high-density microarrays. Therefore, there is no doubt that microarray experiments, combined with bioinformatics, will advance the understanding of the pathobiology of pancreatic cancer, assist in the diagnoses and prognoses of this disease, and develop novel therapies. In this article, the most recent advances in the application of microarray technology to pancreatic cancer research are reviewed, and areas for further development are identified.

Journal Article↗

PubMedDate not supplied

Immune reactivity against a novel HLA-A3-restricted influenza virus peptide identified by predictive algorithms and interferon-gamma quantitative PCR.

The use of appropriate antigenic peptides for the most common human major histocompatibility complex (MHC) alleles is required for the amplification of the autologous cytotoxic compartment and the development of cytotoxic T cell-mediated immunity. The human A2 allele of the MHC plays an important role for the identification of peptide-specific cytotoxic T cells (CTL) against tumor and viral epitopes. Computer-based prediction algorithms, which are available on the Internet, have already proved to be applicable for the identification of novel CTL epitopes. Using the bioinformatics approach, the authors have identified the novel influenza matrix protein-derived and HLA-A3-restricted 9-mer peptide RLEDVFAGK capable of inducing peptide specific CTL reactivity. Peripheral blood mononuclear cells (PBMC) from healthy individuals and patients with lung cancer were pulsed with this peptide and with the well-characterized HLA-A2-restricted influenza A virus matrix peptide GILGFVFTL. Using quantitative PCR (TaqMan; Applied Biosystems, Foster City, CA, U.S.A), reactivity for both peptides was determined by measuring the change in type 1 cytokine (IFN-gamma) expression upon in vitro stimulation. Peptide-specific reactivity matched well with the subsequently determined MHC-class I alleles of the tested individuals. Results from this study indicate that the use of bioinformatics and the PCR-based screening system for the monitoring of T cell reactivity may allow for the identification of novel CTL epitopes.

Algorithms↗

PubMed2004

Current applications of microarrays in head and neck cancer research.

OBJECTIVES/HYPOTHESIS: The objective was to introduce microarray technology and its applications in cancer research to the head and neck clinician. STUDY DESIGN: Literature review combined with methodology and examples from the authors' experiences with microarray analysis of tumors of the head and neck. METHODS: Search of literature and the authors' experience was made for technical details, alternative methods of data analysis, available bioinformatics tools, and applications of microarrays in cancer research. RESULTS: Microarrays allow the simultaneous analysis of the expression of thousands of genes. The use of a well-developed microarray study design leads to informative results. There are various bioinformatics resources widely available to aid in the analysis of microarray data. However, there is not yet a gold standard for analysis because this methodology is still evolving. CONCLUSION: Microarray studies may allow researchers to identify genetic changes relevant to diagnosis and prognosis in patients with head and neck cancer. Although still relatively new, this powerful methodology has immense potential to aid in understanding of the genetic changes that are important in head and neck cancer.

DNA Mutational Analysis↗