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PubMed2006

Adapters, shims, and glue--service interoperability for in silico experiments.

MOTIVATION: Computationally, in silico experiments in biology are workflows describing the collaboration of people, data and methods. The Grid and Web services are proposed to be the next generation infrastructure supporting the deployment of bioinformatics workflows. But the growing number of autonomous and heterogeneous services pose challenges to the used middleware w.r.t. composition, i.e. discovery and interoperability of services required within in silico experiments. In the IRIS project, we handle the problem of service interoperability by a semi-automatic procedure for identifying and placing customizable adapters into workflows built by service composition. RESULTS: We show the effectiveness and robustness of the software-aided composition procedure by a case study in the field of life science. In this study we combine different database services with different analysis services with the objective of discovering required adapters. Our experiments show that we can identify relevant adapters with high precision and recall.

Computational Biology↗
PubMed2003

ELISA: structure-function inferences based on statistically significant and evolutionarily inspired observations.

UNLABELLED: The problem of functional annotation based on homology modeling is primary to current bioinformatics research. Researchers have noted regularities in sequence, structure and even chromosome organization that allow valid functional cross-annotation. However, these methods provide a lot of false negatives due to limited specificity inherent in the system. We want to create an evolutionarily inspired organization of data that would approach the issue of structure-function correlation from a new, probabilistic perspective. Such organization has possible applications in phylogeny, modeling of functional evolution and structural determination. ELISA (Evolutionary Lineage Inferred from Structural Analysis, http://romi.bu.edu/elisa) is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function "neighborhoods". The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). We introduce a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind. AVAILABILITY: The database is available at http://romi.bu.edu/elisa.

Amino Acid Sequence↗
PubMed1999

The Emerging Importance of Genetics in Epidemiologic Research III. Bioinformatics and statistical genetic methods.

PURPOSE: To outline potential benefits of integrating recent developments in bioinformatics and statistical genetics with traditional epidemiologic studies to localize genes influencing complex phenotypes and examine genetic effects on disease susceptibility. METHODS: An overview of bioinformatic and statistical approaches for localizing disease-susceptibility genes as well as challenges associated with identifying functional DNA variants and context-dependent genetic effects concludes this three-part series on the importance of genetics in epidemiologic research. RESULTS: Rapidly evolving bioinformatic and statistical methods are providing invaluable information on newly-discovered genes and molecular variation influencing human diseases that is readily available to epidemiologic researchers. CONCLUSIONS: Integrating bioinformatics and molecular biotechnology with epidemiologic methods of assessing disease risk is rapidly expanding our ability to identify genetic influences on complex human diseases. These technological advances are likely to have a profound impact on current knowledge of complex disease etiology, and may reveal novel approaches to disease treatment and prevention.

Data Interpretation, Statistical↗
PubMed2001

A motif-based profile scanning approach for genome-wide prediction of signaling pathways.

The rapid increase in genomic information requires new techniques to infer protein function and predict protein-protein interactions. Bioinformatics identifies modular signaling domains within protein sequences with a high degree of accuracy. In contrast, little success has been achieved in predicting short linear sequence motifs within proteins targeted by these domains to form complex signaling networks. Here we describe a peptide library-based searching algorithm, accessible over the World Wide Web, that identifies sequence motifs likely to bind to specific protein domains such as 14-3-3, SH2, and SH3 domains, or likely to be phosphorylated by specific protein kinases such as Src and AKT. Predictions from database searches for proteins containing motifs matching two different domains in a common signaling pathway provides a much higher success rate. This technology facilitates prediction of cell signaling networks within proteomes, and could aid in the identification of drug targets for the treatment of human diseases.

Algorithms↗
PubMed2003

Proteomic analysis of the systemic immune response of Drosophila.

Improvements in two-dimensional gel electrophoresis, mass spectrometry, and bioinformatics provide new tools to characterize proteins involved in a physiological process, such as the immune response of the insect model Drosophila melanogaster. Profiling of the proteins present in the hemolymph (insect blood) of noninfected flies versus flies infected with bacteria or fungi was performed by two-dimensional gel electrophoresis, silver or Coomassie staining, and image analysis. Through this differential analysis, more than 70 out of 160 spots were up- or down-regulated by at least 5-fold after microbial infection. Coomassie staining, in-gel digestion, and database searches yielded the identity of a series of proteins that are directly involved in the Drosophila immune system. This included proteases, protease inhibitors, and recognition molecules such as prophenoloxydase-activating enzymes, serpins, and Gram-negative binding protein-like. Proteins with a potential function in the immune response were also identified, such as an odorant binding protein, peptidylglycine alpha-hydroxylating monooxygenase, and transferrin, affording new candidates for further investigation of innate immune mechanisms. Moreover, several molecules resulting from the cleavage of proteins were detected after the fungal infection. Altogether, this first differential proteomic analysis of the immune response of Drosophila paves the way for the study of proteins affected during innate immunity.

Animals↗
PubMed2006

The mouse genome database (MGD): new features facilitating a model system.

The mouse genome database (MGD, http://www.informatics.jax.org/), the international community database for mouse, provides access to extensive integrated data on the genetics, genomics and biology of the laboratory mouse. The mouse is an excellent and unique animal surrogate for studying normal development and disease processes in humans. Thus, MGD's primary goals are to facilitate the use of mouse models for studying human disease and enable the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Core MGD data content includes gene characterization and functions, phenotype and disease model descriptions, DNA and protein sequence data, polymorphisms, gene mapping data and genome coordinates, and comparative gene data focused on mammals. Data are integrated from diverse sources, ranging from major resource centers to individual investigator laboratories and the scientific literature, using a combination of automated processes and expert human curation. MGD collaborates with the bioinformatics community on the development of data and semantic standards, and it incorporates key ontologies into the MGD annotation system, including the Gene Ontology (GO), the Mammalian Phenotype Ontology, and the Anatomical Dictionary for Mouse Development and the Adult Anatomy. MGD is the authoritative source for mouse nomenclature for genes, alleles, and mouse strains, and for GO annotations to mouse genes. MGD provides a unique platform for data mining and hypothesis generation where one can express complex queries simultaneously addressing phenotypic effects, biochemical function and process, sub-cellular location, expression, sequence, polymorphism and mapping data. Both web-based querying and computational access to data are provided. Recent improvements in MGD described here include the incorporation of single nucleotide polymorphism data and search tools, the addition of PIR gene superfamily classifications, phenotype data for NIH-acquired knockout mice, images for mouse phenotypic genotypes, new functional graph displays of GO annotations, and new orthology displays including sequence information and graphic displays.

Animals↗
PubMed2006

DWARF--a data warehouse system for analyzing protein families.

BACKGROUND: The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families. DESCRIPTION: The data warehouse system DWARF integrates data on sequence, structure, and functional annotation for protein fold families. The underlying relational data model consists of three major sections representing entities related to the protein (biochemical function, source organism, classification to homologous families and superfamilies), the protein sequence (position-specific annotation, mutant information), and the protein structure (secondary structure information, superimposed tertiary structure). Tools for extracting, transforming and loading data from public available resources (ExPDB, GenBank, DSSP) are provided to populate the database. The data can be accessed by an interface for searching and browsing, and by analysis tools that operate on annotation, sequence, or structure. We applied DWARF to the family of alpha/beta-hydrolases to host the Lipase Engineering database. Release 2.3 contains 6138 sequences and 167 experimentally determined protein structures, which are assigned to 37 superfamilies 103 homologous families. CONCLUSION: DWARF has been designed for constructing databases of large structurally related protein families and for evaluating their sequence-structure-function relationships by a systematic analysis of sequence, structure and functional annotation. It has been applied to predict biochemical properties from sequence, and serves as a valuable tool for protein engineering.

Amino Acid Sequence↗
PubMed2004

Bioinformatics software resources.

This review looks at internet archives, repositories and lists for obtaining popular and useful biology and bioinformatics software. Resources include collections of free software, services for the collaborative development of new programs, software news media and catalogues of links to bioinformatics software and web tools. Problems with such resources arise from needs for continued curator effort to collect and update these, combined with less than optimal community support, funding and collaboration. Despite some problems, the available software repositories provide needed public access to many tools that are a foundation for analyses in bioscience research efforts.

Algorithms↗
PubMed2004

ProteoMix: an integrated and flexible system for interactively analyzing large numbers of protein sequences.

UNLABELLED: ProteoMix is a suite of JAVA programs for identifying, annotating and predicting regions of interest in large sets of amino acid sequences, according to systematic and consistent criteria. It is based on two concepts (1) the integration of results from different sequence analysis tools increases the prediction reliability; and (2) the integration protocol is critical and needs to be easily adaptable in a case-by-case manner. ProteoMix was designed to analyze simultaneously multiple protein sequences using several bioinformatics tools, merge the results of the analyses using logical functions and display them on an integrated viewer. In addition, new sequences can be added seamlessly to an analysis performed on an initial set of sequences. ProteoMix has a modular design, and bioinformatics tools are run on remote servers accessed using the Internet Simple Object Access Protocol (SOAP), ensuring the swift implementation of additional tools. ProteoMix has a user-friendly interactive graphical user interface environment and runs on PCs with Microsoft OS. AVAILABILITY: ProteoMix is freely available for academic users at http://bio.gsc.riken.jp/ProteoMix/

Database Management Systems↗
PubMed2006

Gene expression profile exploration of a large dataset on chronic fatigue syndrome.

OBJECTIVE: To gain understanding of the molecular basis of chronic fatigue syndrome (CFS) through gene expression analysis using a large microarray data set in conjunction with clinically administrated questionnaires. METHOD: Data from the Wichita (KS, USA) CFS Surveillance Study was used, comprising 167 participants with two self-report questionnaires (multidimensional fatigue inventory [MFI] and Zung depression scale [Zung]), microarray data, empiric classification, and others. Microarray data was analyzed using bioinformatics tools from ArrayTrack. RESULTS: Correspondence analysis was applied to the MFI questionnaire to select the 23 samples having either the most or the least fatigue, and to the Zung questionnaire to select the 26 samples having either the most or least depression; ten samples were common, resulting in a total of 39 samples. The MFI and Zung-based CFS/non-CFS (NF) classifications on the 39 samples were consistent with the empiric classification. Two differentially-expressed gene lists were determined, 188 fatigue-related genes and 164 depression-related genes, which shared 24 common genes and involved 11 common pathways. Principal component analysis based on 24 genes clearly separates 39 samples with respect to their likelihood to be CFS. Most of the 24 genes are not previously reported for CFS, yet their functions are consistent with the prevailing model of CFS, such as immune response, apoptosis, ion channel activity, signal transduction, cell-cell signaling, regulation of cell growth and neuronal activity. Hierarchical cluster analysis was performed based on 24 genes to classify 128 (=167-39) unassigned samples. Several of the 11 identified common pathways are supported by earlier findings for CFS, such as cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction. Importantly, most of the 11 common pathways are interrelated, suggesting complex biological mechanisms associated with CFS. CONCLUSION: Bioinformatics is critical in this study to select definitive sample groups, analyze gene expression data and gain insight into biological mechanisms. The 24 identified common genes and 11 common pathways could be important in future studies of CFS at the molecular level.

Adult↗
PubMed2003

Proteomic identification of a large complement of rat urinary proteins.

The characterization of urinary proteins is an important tool to identify disease-related biomarkers and to better understand renal physiology. Expression of urinary proteins has been previously studied by Western blotting and other immunological methods. The scope of such studies, however, is limited to previously identified proteins for which specific antibodies are existed. We used proteomic analysis to identify proteins and to construct a proteome map for Sprague-Dawley (SD) rat urine isolated by ultracentrifugation. Urinary proteins were separated by two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) and visualized by silver staining. Proteins were identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), followed by peptide mass fingerprinting using the NCBI protein database. A total of 350 protein spots were visualized. From 250 excised spots, 111 protein components were identified including transporters, transport regulators, chaperones, enzymes, signaling proteins, cytoskeletal proteins, pheromone-binding proteins, receptors, and novel gene products. The presence of a number of these identified proteins was unexpected and had not previously been identified in the urine. 2-D Western blot analyses for randomly selected proteins (ezrin, HSP70, beta- and gamma-actin, Rho-GDI, and l-myc) clearly confirmed the proteomic identification. Several potential posttranslational modifications were predicted by bioinformatic analyses. These data indicate that a large complement of expected and unexpected urinary proteins can be simultaneously studied by proteomic analysis. This approach may lead to better understanding of renal physiology and pathophysiology, and to biomarker discovery.

Actins↗
PubMed2003

G-language Genome Analysis Environment: a workbench for nucleotide sequence data mining.

SUMMARY: G-language Genome Analysis Environment (G-language GAE) is an open source generic software package aimed for higher efficiency in bioinformatics analysis. G-language GAE has an interface as a set of Perl libraries for software development, and a graphical user interface for easy manipulation. Both Windows and Linux versions are available. AVAILABILITY: From http://www.g-language.org/ under GNU General Public License. CD-ROMs are distributed freely in major conferences.

Database Management Systems↗
PubMed2002

OILing the way to machine understandable bioinformatics resources.

The complex questions and analyses posed by biologists, as well as the diverse data resources they develop, require the fusion of evidence from different, independently developed, and heterogeneous resources. The web, as an enabler for interoperability, has been an excellent mechanism for data publication and transportation. Successful exchange and integration of information, however, depends on a shared language for communication (a terminology) and a shared understanding of what the data means (an ontology). Without this kind of understanding, semantic heterogeneity remains a problem for both humans and machines. One means of dealing with heterogeneity in bioinformatics resources is through terminology founded upon an ontology. Bioinformatics resources tend to be rich in human readable and understandable annotation, with each resource using its own terminology. These resources are machine readable, but not machine understandable. Ontologies have a role in increasing this machine understanding, reducing the semantic heterogeneity between resources and thus promoting the flexible and reliable interoperation of bioinformatics resources. This paper describes a solution derived from the semantic web [a machine understandable world-wide web (WWW)], the ontology inference layer (OIL), as a solution for semantic bioinformatics resources. The nature of the heterogeneity problems are presented along with a description of how metadata from domain ontologies can be used to alleviate this problem. A companion paper in this issue gives an example of the development of a bio-ontology using OIL.

Algorithms↗
PubMed2005

Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator.

BACKGROUND: Computational methods for problem solving need to interleave information access and algorithm execution in a problem-specific workflow. The structures of these workflows are defined by a scaffold of syntactic, semantic and algebraic objects capable of representing them. Despite the proliferation of GUIs (Graphic User Interfaces) in bioinformatics, only some of them provide workflow capabilities; surprisingly, no meta-analysis of workflow operators and components in bioinformatics has been reported. RESULTS: We present a set of syntactic components and algebraic operators capable of representing analytical workflows in bioinformatics. Iteration, recursion, the use of conditional statements, and management of suspend/resume tasks have traditionally been implemented on an ad hoc basis and hard-coded; by having these operators properly defined it is possible to use and parameterize them as generic re-usable components. To illustrate how these operations can be orchestrated, we present GPIPE, a prototype graphic pipeline generator for PISE that allows the definition of a pipeline, parameterization of its component methods, and storage of metadata in XML formats. This implementation goes beyond the macro capacities currently in PISE. As the entire analysis protocol is defined in XML, a complete bioinformatic experiment (linked sets of methods, parameters and results) can be reproduced or shared among users. AVAILABILITY: http://if-web1.imb.uq.edu.au/Pise/5.a/gpipe.html (interactive), ftp://ftp.pasteur.fr/pub/GenSoft/unix/misc/Pise/ (download). CONCLUSION: From our meta-analysis we have identified syntactic structures and algebraic operators common to many workflows in bioinformatics. The workflow components and algebraic operators can be assimilated into re-usable software components. GPIPE, a prototype implementation of this framework, provides a GUI builder to facilitate the generation of workflows and integration of heterogeneous analytical tools.

Algorithms↗
PubMed2000

Mass spectrometry of the human pituitary proteome: identification of selected proteins.

The field of proteomics involves the combined application of advanced separation techniques, mass spectrometry, and bioinformatics tools to characterize proteins in complex biological mixtures. Here we report the identification of nine proteins from the human pituitary proteome, using the proteomics approach. The pituitary proteins were separated by two-dimensional electrophoresis, and were visualized by silver staining. The proteins of interest were subjected to in-gel digestion with trypsin, and the masses of the resulting peptides were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. This tryptic mass map was used to identify the proteins through a search of a protein-sequence database. The identified proteins include important hormones, and enzymes with various catalytic activities. These proteins will be used to construct a two-dimensional reference database of the human pituitary. This database will be employed to study changes in the pituitary proteome that are associated with the formation of pituitary tumors.

Databases, Factual↗
PubMed2004

A structure-based method for protein sequence alignment.

MOTIVATION: With the continuing rapid growth of protein sequence data, protein sequence comparison methods have become the most widely used tools of bioinformatics. Among these methods are those that use position-specific scoring matrices (PSSMs) to describe protein families. PSSMs can capture information about conserved patterns within families, which can be used to increase the sensitivity of searches for related sequences. Certain types of structural information, however, are not generally captured by PSSM search methods. Here we introduce a program, Structure-based ALignment TOol (SALTO), that aligns protein query sequences to PSSMs using rules for placing and scoring gaps that are consistent with the conserved regions of domain alignments from NCBI's Conserved Domain Database. RESULTS: In most cases, the alignment scores obtained using the local alignment version follow an extreme value distribution. SALTO's performance in finding related sequences and producing accurate alignments is similar to or better than that of IMPALA; one advantage of SALTO is that it imposes an explicit gapping model on each protein family. AVAILABILITY: A stand-alone version of the program that can generate global or local alignments is available by ftp distribution (ftp://ftp.ncbi.nih.gov/pub/SALTO/), and has been incorporated to Cn3D structure/alignment viewer. CONTACT: bryant@ncbi.nlm.nih.gov.

Algorithms↗
PubMed2002

Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer.

The increasing availability and maturity of DNA microarray technology has led to an explosion of cancer profiling studies. To extract maximum value from the accumulating mass of publicly available cancer gene expression data, methods are needed to evaluate, integrate, and intervalidate multiple datasets. Here we demonstrate a statistical model for performing meta-analysis of independent microarray datasets. Implementation of this model revealed that four prostate cancer gene expression datasets shared significantly similar results, independent of the method and technology used (i.e., spotted cDNA versus oligonucleotide). This interstudy cross-validation approach generated a cohort of genes that were consistently and significantly dysregulated in prostate cancer. Bioinformatic investigation of these genes revealed a synchronous network of transcriptional regulation in the polyamine and purine biosynthesis pathways. Beyond the specific implications for prostate cancer, this work establishes a much-needed model for the evaluation, cross-validation, and comparison of multiple cancer profiling studies.

Adenosine Monophosphate↗
PubMed2003

Subunit E of mitochondrial ATP synthase: a bioinformatic analysis reveals a phosphopeptide binding motif supporting a multifunctional regulatory role and identifies a related human brain protein with the same motif.

The mitochondrial adenosine triphosphate (ATP) synthase is located in the inner membrane and consists of at least 16 subunit types in animals, one of which is subunit e, the function of which is not clearly defined. A highly homologous protein is located in the nucleus and named progesterone receptor binding protein (RBF), to designate its role in this organelle. In addition, the expression level of subunit e in mammalian cells fluctuates greatly and is induced by certain carcinogens and elevated in liver cancers. Because these previous observations suggested to us that subunit e may play multifunctional regulatory roles, we employed a bioinformatic approach to test this view. First, from sequence alignment studies, secondary structure analyses, and basic local alignment search tool (BLAST) searches, we concluded that mitochondrial subunit e and the homologous nuclear protein RBF are most likely the same protein. Second, we examined the known sequence and structure of one of the most common multifunctional cell regulatory proteins, the 14-3-3 protein, involved in phosphopeptide binding, and deduced that it has an apparent binding motif (-KX(6)R---RY-). Third, from careful examination of the conserved residues within all subunit e sequences in the database, we discovered that this protein has a comparable binding motif (-RY---KX(6)R-). Finally, in a BLAST search for additional homologs of subunit e, we found a human brain protein, KIAA1578, the C-terminal 30 amino acids of which are identical to those of human subunit e. This protein also contains a potential phosphopeptide binding motif. In summary, these studies provide support for the view that subunit e is a multifunctional cell regulator involved in cell signaling, and implicate the involvement of the KIAA1578 protein in cell signaling as well. These studies suggest also that, while functioning as a subunit of mitochondrial ATP synthases, subunit e may help regulate these complexes by binding to phosphopeptides within one or more of the other subunit types.

14-3-3 Proteins↗