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At least 865 records · Page 48Linked to original sources
PubMed2000

The murine DSCR1-like (Down syndrome candidate region 1) gene family: conserved synteny with the human orthologous genes.

A recently recognized gene family, conserved from yeast to humans, includes Down syndrome candidate region 1 gene (DSCR1), Adapt78 (recognized as the hamster ortholog of the DSCR1 isoform 4), ZAKI-4 (renamed DSCR1-like 1, DSCR1L1) and DSCR1L2 (a novel gene on human chromosome 1), along with yeast and C. elegans single members (Strippoli P., Lenzi L., Petrini M., Carinci P., Zannotti M., 2000. A new gene family including DSCR1 (Down Syndrome Candidate Region 1) and ZAKI-4: characterization from yeast to human and identification of DSCR1-like 2, a novel human member. Genomics 64, 252-263). The proposed family labels were a putative single-strand nucleic acid binding domain similar to the RNA recognition motif, and a unique, highly-conserved serine-proline motif. We have used a bioinformatics-driven molecular biology approach to characterize the murine members of DSCR1-like gene family. Systematic expressed-sequence-tags (EST) database search and reverse-transcription polymerase chain rection (RT-PCR) product sequencing allowed identification of the murine DSCR1, DSCR1L1 and DSCR1L2. The sequences of the respective protein products are of 198, 197 and 241 amino acids, respectively, and are very similar to the corresponding human proteins. The very broad expression pattern of the murine DSCR1 genes is similar to that of the human genes. Using a radiation hybrid panel, we mapped the murine DSCR1-like family members. The murine DSCR1 ortholog is located on the chromosome 16, in a region corresponding to that on human chromosome 21 just upstream of the Down syndrome candidate region. DSCR1L1 and DSCR1L2 murine genes are also located in chromosomal segments of chromosome 17 and 4, respectively, exactly corresponding to those containing the respective human homologs on chromosomes 6 and 1. Description of the mouse orthologs for DSCR1-like genes will allow knockout mice to be obtained for specific family members.

Adaptor Proteins, Signal Transducing↗
PubMed2004

Utility of homology models in the drug discovery process.

Advances in bioinformatics and protein modeling algorithms, in addition to the enormous increase in experimental protein structure information, have aided in the generation of databases that comprise homology models of a significant portion of known genomic protein sequences. Currently, 3D structure information can be generated for up to 56% of all known proteins. However, there is considerable controversy concerning the real value of homology models for drug design. This review provides an overview of the latest developments in this area and includes selected examples of successful applications of the homology modeling technique to pharmaceutically relevant questions. In addition, the strengths and limitations of the application of homology models during all phases of the drug discovery process are discussed.

Animals↗
PubMed2006

Analyzing proteomes and protein function using graphical comparative analysis of tandem mass spectrometry results.

Although generating large amounts of proteomic data using tandem mass spectrometry has become routine, there is currently no single set of comprehensive tools for the rigorous analysis of tandem mass spectrometry results given the large variety of possible experimental aims. Currently available applications are typically designed for displaying proteins and posttranslational modifications from the point of view of the mass spectrometrist and are not versatile enough to allow investigators to develop biological models of protein function, protein structure, or cell state. In addition, storage and dissemination of mass spectrometry-based proteomic data are problems facing the scientific community. To address these issues, we have developed a relational database model that efficiently stores and manages large amounts of tandem mass spectrometry results. We have developed an integrated suite of multifunctional analysis software for interpreting, comparing, and displaying these results. Our system, Bioinformatic Graphical Comparative Analysis Tools (BIGCAT), allows sophisticated analysis of tandem mass spectrometry results in a biologically intuitive format and provides a solution to many data storage and dissemination issues.

Amino Acid Sequence↗
PubMed2003

tmRDB (tmRNA database).

Maintained at the University of Texas Health Science Center at Tyler, Texas, the tmRNA database (tmRDB) is accessible at the URL http://psyche.uthct.edu/dbs/tmRDB/tmRDB.html with mirror sites located at Auburn University, Auburn, Alabama (http://www.ag.auburn.edu/mirror/tmRDB/) and the Bioinformatics Research Center, Aarhus, Denmark (http://www.bioinf.au.dk/tmRDB/). The tmRDB collects and distributes information relevant to the study of tmRNA. In trans-translation, this molecule combines properties of tRNA and mRNA and binds several proteins to form the tmRNP. Related RNPs are likely to be functional in all bacteria. In this release of tmRDB, 186 new entries from 10 bacterial groups for a total of 274 tmRNA sequences have been added. Lists of the tmRNAs and the corresponding tmRNA-encoded tag-peptides are presented in alphabetical and phylogenetic order. The tmRNA sequences are aligned manually, assisted by computational tools, to determine base pairs supported by comparative sequence analysis. The tmRNA alignment, available in a variety of formats, provides the basis for the secondary and tertiary structure of each tmRNA molecule. Three-dimensional models of the tmRNAs and their associated proteins in PDB format give evidence for the recent progress that has been made in the understanding of tmRNP structure and function.

Bacteria↗
PubMed2005

Structure and functional analysis of unclassified genes strongly expressed in human visceral adipose tissue.

Our previous work has described the gene expression patterns of human visceral adipose tissue (VAT) at the transcriptome level and reported that the strongly expressed genes in VAT showed an uneven distribution throughout the genome. The aim of the present work was to focus on the unclassified genes and known expressed sequence tags (ESTs) strongly expressed in VAT and analyze their structure and function with bioinformatics. Among the 400 ESTs strongly expressed in the VAT, 340 clones were classified into known genes through searching the latest Genbank database. Functional classification showed that 85 clones were unclassified known genes, and approx 90% of them were found to be expressed in adipose tissue for the first time. Among the 85 unclassified genes, only two share similarities in the coding sequences with all species examined, and six genes had so far no obvious similarity to any genes across different species. The protein products of 7 genes had putative signal peptide and 11 had transmembrane domains. The protein products of 39 genes had relative specific motifs or prosites on primary structure. In silico Northern blot showed that 21 known ESTs were abundantly specifically expressed in adipose tissue, which may provide clues to identify novel genes closely related to adipocyte function with potential pathophysiological implications.

Abdomen↗
PubMed2005

Combinatorial natural products: from cloning to analysis.

Medicinal compounds from plants represent one of the largest and most diverse groups of plant secondary metabolites. The advent of advanced bioinformatics tools and modern genetic technology allowed for manipulation of biosynthetic pathways with the potential of generating novel chemical entities. First, public databases of secondary metabolite related enzymes were interrogated to identify relevant plant genes from vinca rosea (Catharanthus roseus) and other species. Genes of interest were tested after cloning by transfection into tobacco cell cultures using DNA viral vectors. The biosynthetic enzymes coded by these genes were over-expressed in the host. Automated solvent extraction procedure was employed to extract secondary metabolites from plant leaf tissues and transfected tobacco cell culture samples. The composition of the extracts was analyzed by state of the art bioanalytical methods such as high performance liquid chromatography and capillary electrophoresis to monitor changes in secondary metabolite patterns.

Chromatography, High Pressure Liquid↗
PubMed2005

SPD--a web-based secreted protein database.

With the improved secreted protein prediction approach and comprehensive data sources, including Swiss-Prot, TrEMBL, RefSeq, Ensembl and CBI-Gene, we have constructed secretomes of human, mouse and rat, with a total of 18 152 secreted proteins. All the entries are ranked according to the prediction confidence. They were further annotated via a proteome annotation pipeline that we developed. We also set up a secreted protein classification pipeline and classified our predicted secreted proteins into different functional categories. To make the dataset more convincing and comprehensive, nine reference datasets are also integrated, such as the secreted proteins from the Gene Ontology Annotation (GOA) system at the European Bioinformatics Institute, and the vertebrate secreted proteins from Swiss-Prot. All these entries were grouped via a TribeMCL based clustering pipeline. We have constructed a web-based secreted protein database, which has been publicly available at http://spd.cbi.pku.edu.cn. Users can browse the database via a GO assignment or chromosomal-location-based interface. Moreover, text query and sequence similarity search are also provided, and the sequence and annotation data can be downloaded freely from the SPD website.

Animals↗
PubMed2006

DBMODELING: a database applied to the study of protein targets from genome projects.

Genome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms. We are now faced with assigning, understanding, and modifying the functions of proteins encoded by these genomes. DBMODELING is a relational database of annotated comparative protein structure models and their metabolic pathway characterization, when identified. This procedure was applied to complete genomes such as Mycobacterium tuberculosis and Xylella fastidiosa. The main interest in the study of metabolic pathways is that some of these pathways are not present in humans, which makes them selective targets for drug design, decreasing the impact of drugs in humans. In the database, there are currently 1,116 proteins from two genomes. It can be accessed by any researcher at http://www.biocristalografia.df.ibilce.unesp.br/tools/. This project confirms that homology modeling is a useful tool in structural bioinformatics and that it can be very valuable in annotating genome sequence information, contributing to structural and functional genomics, and analyzing protein-ligand docking.

Animals↗
PubMed2004

New approaches towards integrated proteomic databases and depositories.

Since the publication of the human genome, two key points have emerged. First, it is still not certain which regions of the genome code for proteins. Second, the number of discrete protein-coding genes is far fewer than the number of different proteins. Proteomics has the potential to address some of these postgenomic issues if the obstacles that we face can be overcome in our efforts to combine proteomic and genomic data. There are many challenges associated with high-throughput and high-output proteomic technologies. Consequently, for proteomics to continue at its current growth rate, new approaches must be developed to ease data management and data mining. Initiatives have been launched to develop standard data formats for exchanging mass spectrometry proteomic data, including the Proteomics Standards Initiative formed by the Human Proteome Organization. Databases such as SwissProt and Uniprot are publicly available repositories for protein sequences annotated for function, subcellular location and known potential post-translational modifications. The availability of bioinformatics solutions is crucial for proteomics technologies to fulfil their promise of adding further definition to the functional output of the human genome. The aim of the Oxford Genome Anatomy Project is to provide a framework for integrating molecular, cellular, phenotypic and clinical information with experimental genetic and proteomics data. This perspective also discusses models to make the Oxford Genome Anatomy Project accessible and beneficial for academic and commercial research and development.

Databases, Protein↗
PubMed2002

Using bioinformatics to identify kidney genes.

Online knowledge of genes involved in mouse kidney development is available from the literature (PubMed) and text-based (Kidney Development and Mouse Gene Expression-GXD) databases. Further information is in gene databases of other organisms having tissues homologous to the metanephros (e.g. Drosophila Malpighian tubules; tools are available for identifying mouse homologues of non-mouse genes) and from tissues homologous to those in the developing kidney (e.g. mesothelium that, like nephrons, arises from a mesenchyme to epithelial transition). Future databases will also include graphical data, and this knowledge will provide a further level of insight. These databases and tools are discussed here.

Animals↗
PubMed2006

Introduction to bioinformatics.

This article introduces the field of bioinformatics and describes bioinformatic approaches and their application to the study of protein allergens. The predominant bioinformatics tools and resources are listed and discussed.

Allergens↗
PubMed2006

Predicting protease types by hybridizing gene ontology and pseudo amino acid composition.

Proteases play a vitally important role in regulating most physiological processes. Different types of proteases perform different functions with different biological processes. Therefore, it is highly desired to develop a fast and reliable means to identify the types of proteases according to their sequences, or even just identify whether they are proteases or nonproteases. The avalanche of protein sequences generated in the postgenomic era has made such a challenge become even more critical and urgent. By hybridizing the gene ontology approach and pseudo amino acid composition approach, a powerful predictor called GO-PseAA predictor was introduced to address the problems. To avoid redundancy and bias, demonstrations were performed on a dataset where none of proteins has >/= 25% sequence identity to any other. The overall success rates thus obtained by the jackknife cross-validation test in identifying protease and nonprotease was 91.82%, and that in identifying the protease type was 85.49% among the following five types: (1) aspartic, (2) cysteine, (3) metallo, (4) serine, and (5) threonine. The high jackknife success rates yielded for such a stringent dataset indicate the GO-PseAA predictor is very powerful and might become a useful tool in bioinformatics and proteomics.

Amino Acids↗
PubMed2002

Viruses and interferon: a fight for supremacy.

The action of interferons (IFNs) on virus-infected cells and surrounding tissues elicits an antiviral state that is characterized by the expression and antiviral activity of IFN-stimulated genes. In turn, viruses encode mechanisms to counteract the host response and support efficient viral replication, thereby minimizing the therapeutic antiviral power of IFNs. In this review, we discuss the interplay between the IFN system and four medically important and challenging viruses -- influenza, hepatitis C, herpes simplex and vaccinia -- to highlight the diversity of viral strategies. Understanding the complex network of cellular antiviral processes and virus-host interactions should aid in identifying new and common targets for the therapeutic intervention of virus infection. This effort must take advantage of the recent developments in functional genomics, bioinformatics and other emerging technologies.

Animals↗
PubMed2004

FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes.

We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute.

Algorithms↗
PubMed2006

springScape: visualisation of microarray and contextual bioinformatic data using spring embedding and an 'information landscape'.

The interpretation of microarray and other high-throughput data is highly dependent on the biological context of experiments. However, standard analysis packages are poor at simultaneously presenting both the array and related bioinformatic data. We have addressed this challenge by developing a system springScape based on 'spring embedding' and an 'information landscape' allowing several related data sources to be dynamically combined while highlighting one particular feature. Each data source is represented as a network of nodes connected by weighted edges. The networks are combined and embedded in the 2-D plane by spring embedding such that nodes with a high similarity are drawn close together. Complex relationships can be discovered by varying the weight of each data source and observing the dynamic response of the spring network. By modifying Procrustes analysis, we find that the visualizations have an acceptable degree of reproducibility. The 'information landscape' highlights one particular data source, displaying it as a smooth surface whose height is proportional to both the information being viewed and the density of nodes. The algorithm is demonstrated using several microarray data sets in combination with protein-protein interaction data and GO annotations. Among the features revealed are the spatio-temporal profile of gene expression and the identification of GO terms correlated with gene expression and protein interactions. The power of this combined display lies in its interactive feedback and exploitation of human visual pattern recognition. Overall, springScape shows promise as a tool for the interpretation of microarray data in the context of relevant bioinformatic information.

Algorithms↗
PubMed2006

Enrichment or depletion of a GO category within a class of genes: which test?

MOTIVATION: A number of available program packages determine the significant enrichments and/or depletions of GO categories among a class of genes of interest. Whereas a correct formulation of the problem leads to a single exact null distribution, these GO tools use a large variety of statistical tests whose denominations often do not clarify the underlying P-value computations. SUMMARY: We review the different formulations of the problem and the tests they lead to: the binomial, chi2, equality of two probabilities, Fisher's exact and hypergeometric tests. We clarify the relationships existing between these tests, in particular the equivalence between the hypergeometric test and Fisher's exact test. We recall that the other tests are valid only for large samples, the test of equality of two probabilities and the chi2-test being equivalent. We discuss the appropriateness of one- and two-sided P-values, as well as some discreteness and conservatism issues. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data Interpretation, Statistical↗
PubMed2003

From microarrays to genome duplications.

A report on the fifth annual conference of the Society for Bioinformatics in the Nordic Countries (SOCBIN), 'Bioinformatics 2003', Helsinki, Finland, 22-24 May 2003.

Animals↗
PubMed2002

Addressing protein localization within the nucleus.

Bridging the gap between the number of gene sequences in databases and the number of gene products that have been functionally characterized in any way is a major challenge for biology. A key characteristic of proteins, which can begin to elucidate their possible functions, is their subcellular location. A number of experimental approaches can reveal the subcellular localization of proteins in mammalian cells. However, genome databases now contain predicted sequences for a large number of potentially novel proteins that have yet to be studied in any way, let alone have their subcellular localization determined. Here we ask whether using bioinformatics tools to analyse the sequence of proteins whose subnuclear localizations have been determined can reveal characteristics or signatures that might allow us to predict localization for novel protein sequences.

Amino Acid Motifs↗