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PubMed2005

Chemosensitivity profile of cancer cell lines and identification of genes determining chemosensitivity by an integrated bioinformatical approach using cDNA arrays.

We have established a panel of 45 human cancer cell lines (JFCR-45) to explore genes that determine the chemosensitivity of these cell lines to anticancer drugs. JFCR-45 comprises cancer cell lines derived from tumors of three different organs: breast, liver, and stomach. The inclusion of cell lines derived from gastric and hepatic cancers is a major point of novelty of this study. We determined the concentration of 53 anticancer drugs that could induce 50% growth inhibition (GI50) in each cell line. Cluster analysis using the GI50s indicated that JFCR-45 could allow classification of the drugs based on their modes of action, which coincides with previous findings in NCI-60 and JFCR-39. We next investigated gene expression in JFCR-45 and developed an integrated database of chemosensitivity and gene expression in this panel of cell lines. We applied a correlation analysis between gene expression profiles and chemosensitivity profiles, which revealed many candidate genes related to the sensitivity of cancer cells to anticancer drugs. To identify genes that directly determine chemosensitivity, we further tested the ability of these candidate genes to alter sensitivity to anticancer drugs after individually overexpressing each gene in human fibrosarcoma HT1080. We observed that transfection of HT1080 cells with the HSPA1A and JUN genes actually enhanced the sensitivity to mitomycin C, suggesting the direct participation of these genes in mitomycin C sensitivity. These results suggest that an integrated bioinformatical approach using chemosensitivity and gene expression profiling is useful for the identification of genes determining chemosensitivity of cancer cells.

Antineoplastic Agents↗
PubMed2005

Annotated proteome of a human T-cell lymphoma.

As the reliable identification of proteins by tandem mass spectrometry becomes increasingly common, the full characterization of large data sets of proteins remains a difficult challenge. Our goal was to survey the proteome of a human T-cell lymphoma-derived cell line in a single set of experiments and present an automated method for the annotation of lists of proteins. A downstream application of these data includes the identification of novel pathogenetic and candidate diagnostic markers of T-cell lymphoma. Total protein isolated from cytoplasmic, membrane, and nuclear fractions of the SUDHL-1 T-cell lymphoma cell line was resolved by SDS-PAGE, and the entire gel lanes digested and analyzed by tandem mass spectrometry. Acquired data files were searched against the UniProt protein database using the SEQUEST algorithm. Search results for each subcellular fraction were analyzed using INTERACT and ProteinProphet. All protein identifications with an error rate of less than 10% were directly exported into excel and analyzed using GOMiner (NIH/NCI). The Gene ontology molecular function and cell location data were summarized for the identified proteins and results exported as user-interactive directed acyclic graphs. A total of 1105 unique proteins were identified and fully annotated, including numerous proteins that had not been previously characterized in lymphoma, in functional categories such as cell adhesion, migration, signaling, and stress response. This study demonstrates the utility of currently available bioinformatics tools for the robust identification and annotation of large numbers of proteins in a batchwise fashion.

Algorithms↗
PubMed2005

Automated derivation and refinement of sequence length patterns for protein sequences using evolutionary computation.

Several stratagems are used in protein bioinformatics for the classification of proteins based on sequence, structure or function. We explore the concept of a minimal signature embedded in a sequence that defines the likely position of a protein in a classification. Specifically, we address the derivation of sparse profiles for the G-protein coupled receptor (GPCR) clan of integral membrane proteins. We present an evolutionary algorithm (EA) for the derivation of sparse profiles (signatures) without the need to supply a multiple alignment. We also apply an evolution strategy (ES) to the problem of pattern and profile refinement. Patterns were derived for the GPCR 'superfamily' and GPCR families 1-3 individually from starting populations of randomly generated signatures, using a database of integral membrane protein sequences and an objective function using a modified receiver operator characteristic (ROC) statistic. The signature derived for the family 1 GPCR sequences was shown to perform very well in a stringent cross-validation test, detecting 76% of unseen GPCR sequences at 5% error. Application of the ES refinement method to a signature developed by a previously described method [Sadowski, M.I., Parish, J.H., 2003. Automated generation and refinement of protein signatures: case study with G-protein coupled receptors. Bioinformatics 19, 727-734] resulted in a 6% increase of coverage for 5% error as measured in the validation test. We note that there might be a limit to this or any classification of proteins based on patterns or schemata.

Algorithms↗
PubMed2006

The Mouse Genome Database (MGD): updates and enhancements.

The Mouse Genome Database (MGD) integrates genetic and genomic data for the mouse in order to facilitate the use of the mouse as a model system for understanding human biology and disease processes. A core component of the MGD effort is the acquisition and integration of genomic, genetic, functional and phenotypic information about mouse genes and gene products. MGD works within the broader bioinformatics community to define referential and semantic standards to facilitate data exchange between resources including the incorporation of information from the biomedical literature. MGD is also a platform for computational assessment of integrated biological data with the goal of identifying candidate genes associated with complex phenotypes. MGD is web accessible at http://www.informatics.jax.org. Recent improvements in MGD described here include the incorporation of an interactive genome browser, the enhancement of phenotype resources and the further development of functional annotation resources.

Animals↗
PubMed2006

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 http://biochip.nchu.edu.tw/crsd1/.

3' Untranslated Regions↗
PubMed2007

Bioinformatic analysis of the TonB protein family.

TonB is a protein prevalent in a large number of Gram-negative bacteria that is believed to be responsible for the energy transduction component in the import of ferric iron complexes and vitamin B(12) across the outer membrane. We have analyzed all the TonB proteins that are currently contained in the Entrez database and have identified nine different clusters based on its conserved 90-residue C-terminal domain amino acid sequence. The vast majority of the proteins contained a single predicted cytoplasmic transmembrane domain; however, nine of the TonB proteins encompass a approximately 290 amino acid N-terminal extension homologous to the MecR1 protein, which is composed of three additional predicted transmembrane helices. The periplasmic linker region, which is located between the N-terminal domain and the C-terminal domain, is extremely variable both in length (22-283 amino acids) and in proline content, indicating that a Pro-rich domain is not a required feature for all TonB proteins. The secondary structure of the C-terminal domain is found to be well preserved across all families, with the most variable region being between the second alpha-helix and the third beta-strand of the antiparallel beta-sheet. The fourth beta-strand found in the solution structure of the Escherichia coli TonB C-terminal domain is not a well conserved feature in TonB proteins in most of the clusters. Interestingly, several of the TonB proteins contained two C-terminal domains in series. This analysis provides a framework for future structure-function studies of TonB, and it draws attention to the unusual features of several TonB proteins.

Amino Acid Sequence↗
PubMedDate not supplied

An ontology for pharmaceutical ligands and its application for in silico screening and library design.

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.

Combinatorial Chemistry Techniques↗
PubMed2005

MEPD: a resource for medaka gene expression patterns.

The Medaka Expression Pattern Database (MEPD) is a database for gene expression patterns determined by in situ hybridization in the small freshwater fish medaka (Oryzias latipes). Data have been collected from various research groups and MEPD is developing into a central expression pattern depository within the medaka community. Gene expression patterns are described by images and terms of a detailed medaka anatomy ontology of over 4000 terms, which we have developed for this purpose and submitted to Open Biological Ontologies. Sequences have been annotated via BLAST match results and using Gene Ontology terms. These new features will facilitate data analyses using bioinformatics approaches and allow cross-species comparisons of gene expression patterns. Presently, MEPD has 19,757 entries, for 1024 of them the expression pattern has been determined.

Animals↗
PubMed2005

High accuracy prediction of beta-turns and their types using propensities and multiple alignments.

We have developed a method that predicts both the presence and the type of beta-turns, using a straightforward approach based on propensities and multiple alignments. The propensities were calculated classically, but the way to use them for prediction was completely new: starting from a tetrapeptide sequence on which one wants to evaluate the presence of a beta-turn, the propensity for a given residue is modified by taking into account all the residues present in the multiple alignment at this position. The evaluation of a score is then done by weighting these propensities by the use of Position-specific score matrices generated by PSI-BLAST. The introduction of secondary structure information predicted by PSIPRED or SSPRO2 as well as taking into account the flanking residues around the tetrapeptide improved the accuracy greatly. This latter evaluated on a database of 426 reference proteins (previously used on other studies) by a sevenfold crossvalidation gave very good results with a Matthews Correlation Coefficient (MCC) of 0.42 and an overall prediction accuracy of 74.8%; this places our method among the best ones. A jackknife test was also done, which gave results within the same range. This shows that it is possible to reach neural networks accuracy with considerably less computional cost and complexity. Furthermore, propensities remain excellent descriptors of amino acid tendencies to belong to beta-turns, which can be useful for peptide or protein engineering and design. For beta-turn type prediction, we reached the best accuracy ever published in terms of MCC (except for the irregular type IV) in the range of 0.25-0.30 for types I, II, and I' and 0.13-0.15 for types VIII, II', and IV. To our knowledge, our method is the only one available on the Web that predicts types I' and II'. The accuracy evaluated on two larger databases of 547 and 823 proteins was not improved significantly. All of this was implemented into a Web server called COUDES (French acronym for: Chercher Ou Une Deviation Existe Surement), which is available at the following URL: http://bioserv.rpbs.jussieu.fr/Coudes/index.html within the new bioinformatics platform RPBS.

Amino Acid Sequence↗
PubMed2004

HyBrow: a prototype system for computer-aided hypothesis evaluation.

MOTIVATION: Experimental design, hypothesis-testing and model-building in the current data-rich environment require the biologists' to collect, evaluate and integrate large amounts of information of many disparate kinds. Developing a unified framework for the representation and conceptual integration of biological data and processes is a major challenge in bioinformatics because of the variety of available data and the different levels of detail at which biological processes can be considered. RESULTS: We have developed the HyBrow (Hypothesis Browser) system as a prototype bioinformatics tool for designing hypotheses and evaluating them for consistency with existing knowledge. HyBrow consists of a modeling framework with the ability to accommodate diverse biological information sources, an event-based ontology for representing biological processes at different levels of detail, a database to query information in the ontology and programs to perform hypothesis design and evaluation. We demonstrate the HyBrow prototype using the galactose gene network in Saccharomyces cerevisiae as our test system, and evaluate alternative hypotheses for consistency with stored information. AVAILABILITY: www.hybrow.org

Computational Biology↗
PubMed2004

MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource for plant genomics.

Arabidopsis thaliana is the most widely studied model plant. Functional genomics is intensively underway in many laboratories worldwide. Beyond the basic annotation of the primary sequence data, the annotated genetic elements of Arabidopsis must be linked to diverse biological data and higher order information such as metabolic or regulatory pathways. The MIPS Arabidopsis thaliana database MAtDB aims to provide a comprehensive resource for Arabidopsis as a genome model that serves as a primary reference for research in plants and is suitable for transfer of knowledge to other plants, especially crops. The genome sequence as a common backbone serves as a scaffold for the integration of data, while, in a complementary effort, these data are enhanced through the application of state-of-the-art bioinformatics tools. This information is visualized on a genome-wide and a gene-by-gene basis with access both for web users and applications. This report updates the information given in a previous report and provides an outlook on further developments. The MAtDB web interface can be accessed at http://mips.gsf.de/proj/thal/db.

Arabidopsis↗
PubMed2005

Molecular cloning and bioinformatic analysis of SPATA4 gene.

Full-length cDNA sequences of four novel SPATA4 genes in chimpanzee, cow, chicken and ascidian were identified by bioinformatic analysis using mouse or human SPATA4 cDNA fragment as electronic probe. All these genes have 6 exons and have similar protein molecular weight and do not localize in sex chromosome. The mouse SPATA4 sequence is identified as significantly changed in cryptorchidism, which shares no significant homology with any known protein in swissprot databases except for the homologous genes in various vertebrates. Our searching results showed that all SPATA4 proteins have a putative conserved domain DUF1042. The percentages of putative SPATA4 protein sequence identity ranging from 30 % to 99 %. The high similarity was also found in 1 kb promoter regions of human, mouse and rat SPATA4 gene. The similarities of the sequences upstream of SPATA4 promoter also have a high proportion. The results of searching SymAtlas (http://symatlas.gnf.org/SymAtlas/) showed that human SPATA4 has a high expression in testis, especially in testis interstitial, leydig cell, seminiferous tubule and germ cell. Mouse SPATA4 was observed exclusively in adult mouse testis and almost no signal was detected in other tissues. The pI values of the protein are negative, ranging from 9.44 to 10.15. The subcellular location of the protein is usually in the nucleus. And the signal peptide possibilities for SPATA4 are always zero. Using the SNPs data in NCBI, we found 33 SNPs in human SPATA4 gene genomic DNA region, with the distribution of 29 SNPs in the introns. CpG island searching gives the data about CpG island, which shows that the regions of the CpG island have a high similarity with each other, though the length of the CpG island is different from each other. This research is a fundamental work in the fields of the bioinformational analysis, and also put forward a new way for the bioinformatic analysis of other genes.

Amino Acid Sequence↗
PubMed2003

Plant metabolomics: large-scale phytochemistry in the functional genomics era.

Metabolomics or the large-scale phytochemical analysis of plants is reviewed in relation to functional genomics and systems biology. A historical account of the introduction and evolution of metabolite profiling into today's modern comprehensive metabolomics approach is provided. Many of the technologies used in metabolomics, including optical spectroscopy, nuclear magnetic resonance, and mass spectrometry are surveyed. The critical role of bioinformatics and various methods of data visualization are summarized and the future role of metabolomics in plant science assessed.

Computational Biology↗
PubMed2005

Poxvirus Bioinformatics Resource Center: a comprehensive Poxviridae informational and analytical resource.

The Poxvirus Bioinformatics Resource Center (PBRC) has been established to provide informational and analytical resources to the scientific community to aid research directed at providing a better understanding of the Poxviridae family of viruses. The PBRC was specifically established as the result of the concern that variola virus, the causative agent of smallpox, as well as related viruses, might be utilized as biological weapons. In addition, the PBRC supports research on poxviruses that might be considered new and emerging infectious agents such as monkeypox virus. The PBRC consists of a relational database and web application that supports the data storage, annotation, analysis and information exchange goals of the project. The current release consists of over 35 complete genomic sequences of various genera, species and strains of viruses from the Poxviridae family. Sequence and annotation information for these viruses has been obtained from sequences publicly available from GenBank as well as sequences not yet deposited in GenBank that have been obtained from ongoing sequencing projects. In addition to sequence data, the PBRC provides comprehensive annotation and curation of virus genes; analytical tools to aid in the understanding of the available sequence data, including tools for the comparative analysis of different virus isolates; and visualization tools to help better display the results of various analyses. The PBRC represents the initial development of what will become a more comprehensive Viral Bioinformatics Resource Center for Biodefense that will be one of the National Institute of Allergy and Infectious Diseases' 'Bioinformatics Resource Centers for Biodefense and Emerging or Re-Emerging Infectious Diseases'. The PBRC website is available at http://www.poxvirus.org.

Computational Biology↗
PubMed2001

Bringing the human genome and the revolution in bioinformatics to the medical school classroom: a case report from Washington University School of Medicine.

The human genome project is revolutionizing medical research and the practice of clinical medicine. To understand and participate in this revolution, physicians must be fluent in human genomics and bioinformatics. At Washington University School of Medicine (WUSM), the authors designed a module for teaching these skills to first-year students. The module uses clinical cases as a platform for accessing information stored in GenBank, Online Mendelian Inheritance in Man (OMIM), and PubMed databases at the National Center for Biotechnology Information (NCBI). This module, which is also designed to reinforce problem-solving skills, has been integrated into WUSM's first-year medical genetics course.

Computer-Assisted Instruction↗
PubMed2003

Poxvirus orthologous clusters: toward defining the minimum essential poxvirus genome.

Increasingly complex bioinformatic analysis is necessitated by the plethora of sequence information currently available. A total of 21 poxvirus genomes have now been completely sequenced and annotated, and many more genomes will be available in the next few years. First, we describe the creation of a database of continuously corrected and updated genome sequences and an easy-to-use and extremely powerful suite of software tools for the analysis of genomes, genes, and proteins. These tools are available free to all researchers and, in most cases, alleviate the need for using multiple Internet sites for analysis. Further, we describe the use of these programs to identify conserved families of genes (poxvirus orthologous clusters) and have named the software suite POCs, which is available at www.poxvirus.org. Using POCs, we have identified a set of 49 absolutely conserved gene families-those which are conserved between the highly diverged families of insect-infecting entomopoxviruses and vertebrate-infecting chordopoxviruses. An additional set of 41 gene families conserved in chordopoxviruses was also identified. Thus, 90 genes are completely conserved in chordopoxviruses and comprise the minimum essential genome, and these will make excellent drug, antibody, vaccine, and detection targets. Finally, we describe the use of these tools to identify necessary annotation and sequencing updates in poxvirus genomes. For example, using POCs, we identified 19 genes that were widely conserved in poxviruses but missing from the vaccinia virus strain Tian Tan 1998 GenBank file. We have reannotated and resequenced fragments of this genome and verified that these genes are conserved in Tian Tan. The results for poxvirus genes and genomes are discussed in light of evolutionary processes.

Amino Acid Sequence↗
PubMed2004

Expressed sequence tags: analysis and annotation.

Expressed sequence tags (ESTs) present a special set of problems for bioinformatic analysis. They are partial and error-prone, and large datasets can have significant internal redundancy. To facilitate analysis of small EST datasets from in-house projects, we present an integrated "pipeline" of tools that take EST data from sequence trace to database submission. These tools also can be used to provide clustering of ESTs into putative genes and to annotate these genes with preliminary sequence similarity searches. The systems are written to use the public-domain LINUX environment and other openly available analytical tools.

Computational Biology↗
PubMed2005

Trends in the quality of data from 5168 oligonucleotide microarrays from a single facility.

Improvements in the quality of gene expression data were investigated based on a database consisting of 5168 oligonucleotide microarrays collected over 3 years. The database includes diverse treatments of human and mouse samples collected from multiple laboratories. The array designs and algorithms used to capture the data have also changed over the 3 years of data collection. All hybridizations and labeling were conducted in the Hartwell Center for Bioinformatics and Biotechnology at St. Jude's Children's Research Hospital. Quality metrics for each human and mouse array were collected and analyzed. Statistical tests, such as ANOVA and linear regression, were applied to test for the effects of array design, algorithm, and time. The quality metrics tested were average background, actin 3'/5' ratio, Bio B signal, percent present, and scale factor. ANOVA results indicate that both recent algorithms and chip designs significantly correlate with improvements in Bio B, scale factor, percent present, and average background. Significant quality improvements correlated with new chip designs, algorithms, and their interaction. In addition, within one chip type analyzed by the same algorithm significant improving trends were still observed. Scale factor, percent present, and average background significantly improved over time for U133A arrays analyzed by the Affymetrix MicroArray Suite 5.0 algorithm according to linear regression. Proportionally fewer outlier arrays (those with less than 25% present calls) were seen over time. Also, high throughput periods did not increase the proportion of outliers, indicating that laboratory monitoring of quality is successfully preventing failures.

Actins↗