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PubMed2004

Development of an integrated genome informatics, data management and workflow infrastructure: a toolbox for the study of complex disease genetics.

The genetic dissection of complex disease remains a significant challenge. Sample-tracking and the recording, processing and storage of high-throughput laboratory data with public domain data, require integration of databases, genome informatics and genetic analyses in an easily updated and scaleable format. To find genes involved in multifactorial diseases such as type 1 diabetes (T1D), chromosome regions are defined based on functional candidate gene content, linkage information from humans and animal model mapping information. For each region, genomic information is extracted from Ensembl, converted and loaded into ACeDB for manual gene annotation. Homology information is examined using ACeDB tools and the gene structure verified. Manually curated genes are extracted from ACeDB and read into the feature database, which holds relevant local genomic feature data and an audit trail of laboratory investigations. Public domain information, manually curated genes, polymorphisms, primers, linkage and association analyses, with links to our genotyping database, are shown in Gbrowse. This system scales to include genetic, statistical, quality control (QC) and biological data such as expression analyses of RNA or protein, all linked from a genomics integrative display. Our system is applicable to any genetic study of complex disease, of either large or small scale.

Animals↗
PubMed2004

Inferring pathways from gene lists using a literature-derived network of biological relationships.

MOTIVATION: A number of omic technologies such as transcriptional profiling, proteomics, literature searches, genetic association, etc. help in the identification of sets of important genes. A subset of these genes may act in a coordinated manner, possibly because they are part of the same biological pathway. Interpreting such gene lists and relating them to pathways is a challenging task. Databases of biological relationships between thousands of mammalian genes can help in deciphering omics data. The relationships between genes can be assembled into a biological network with each protein as a node and each relationship as an edge between two proteins (or nodes). This network may then be searched for subnetworks consisting largely of interesting genes from the omics experiment. The subset of genes in the subnetwork along with the web of relationships between them helps to decipher the underlying pathways. Finding such subnetworks that maximally include all proteins from the query set but few others is the focus for this paper. RESULTS: We present a heuristic algorithm and a scoring function that work well both on simulated data and on data from known pathways. The scoring function is an extension of a previous study for a single biological experiment. We use a simple set of heuristics that provide a more efficient solution than the simulated annealing method. We find that our method works on reasonably complex curated networks containing approximately 9000 biological entities (genes and metabolites), and approximately 30,000 biological relationships. We also show that our method can pick up a pathway signal from a query list including a moderate number of genes unrelated to the pathway. In addition, we quantify the sensitivity and specificity of the technique.

Algorithms↗
PubMed2006

A reference database for circular dichroism spectroscopy covering fold and secondary structure space.

MOTIVATION: Circular Dichroism (CD) spectroscopy is a long-established technique for studying protein secondary structures in solution. Empirical analyses of CD data rely on the availability of reference datasets comprised of far-UV CD spectra of proteins whose crystal structures have been determined. This article reports on the creation of a new reference dataset which effectively covers both secondary structure and fold space, and uses the higher information content available in synchrotron radiation circular dichroism (SRCD) spectra to more accurately predict secondary structure than has been possible with existing reference datasets. It also examines the effects of wavelength range, structural redundancy and different means of categorizing secondary structures on the accuracy of the analyses. In addition, it describes a novel use of hierarchical cluster analyses to identify protein relatedness based on spectral properties alone. The databases are shown to be applicable in both conventional CD and SRCD spectroscopic analyses of proteins. Hence, by combining new bioinformatics and biophysical methods, a database has been produced that should have wide applicability as a tool for structural molecular biology.

Algorithms↗
PubMed2004

MitoProteome: mitochondrial protein sequence database and annotation system.

MitoProteome is an object-relational mitochondrial protein sequence database and annotation system. The initial release contains 847 human mitochondrial protein sequences, derived from public sequence databases and mass spectrometric analysis of highly purified human heart mitochondria. Each sequence is manually annotated with primary function, subfunction and subcellular location, and extensively annotated in an automated process with data extracted from external databases, including gene information from LocusLink and Ensembl; disease information from OMIM; protein-protein interaction data from MINT and DIP; functional domain information from Pfam; protein fingerprints from PRINTS; protein family and family-specific signatures from InterPro; structure data from PDB; mutation data from PMD; BLAST homology data from NCBI NR; and proteins found to be related based on LocusLink and SWISS-PROT references and sequence and taxonomy data. By highly automating the processes of maintaining the MitoProteome Protein List and extracting relevant data from external databases, we are able to present a dynamic database, updated frequently to reflect changes in public resources. The MitoProteome database is publicly available at http://www. mitoproteome.org/. Users may browse and search MitoProteome, and access a complete compilation of data relevant to each protein of interest, cross-linked to external databases.

Computational Biology↗
PubMed2005

openSputnik--a database to ESTablish comparative plant genomics using unsaturated sequence collections.

The public expressed sequence tag collections are continually being enriched with high-quality sequences that represent an ever-expanding range of taxonomically diverse plant species. While these sequence collections provide biased insight into the populations of expressed genes available within individual species and their associated tissues, the information is conceivably of wider relevance in a comparative context. When we consider the available expressed sequence tag (EST) collections of summer 2004, most of the major plant taxonomic clades are at least superficially represented. Investigation of the five million available plant ESTs provides a wealth of information that has applications in modelling the routes of plant genome evolution and the identification of lineage-specific genes and gene families. Over four million ESTs from over 50 distinct plant species have been collated within an EST analysis pipeline called openSputnik. The ESTs were resolved down into approximately one million unigene sequences. These have been annotated using orthology-based annotation transfer from reference plant genomes and using a variety of contemporary bioinformatics methods to assign peptide, structural and functional attributes. The openSputnik database is available at http://sputnik.btk.fi.

Cluster Analysis↗
PubMedDate not supplied

Diagnosing breast cancer based on support vector machines.

The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.

Algorithms↗
PubMed2006

Collecting and organizing systematic sets of protein data.

Systems biology, particularly of mammalian cells, is data starved. However, technologies are now in place to obtain rich data, in a form suitable for model construction and validation, that describes the activities, states and locations of cell-signalling molecules. The key is to use several measurement technologies simultaneously and, recognizing each of their limits, to assemble a self-consistent compendium of systematic data.

Animals↗
PubMed2000

Systematic and fully automated identification of protein sequence patterns.

We present an efficient algorithm to systematically and automatically identify patterns in protein sequence families. The procedure is based on the Splash deterministic pattern discovery algorithm and on a framework to assess the statistical significance of patterns. We demonstrate its application to the fully automated discovery of patterns in 974 PROSITE families (the complete subset of PROSITE families which are defined by patterns and contain DR records). Splash generates patterns with better specificity and undiminished sensitivity, or vice versa, in 28% of the families; identical statistics were obtained in 48% of the families, worse statistics in 15%, and mixed behavior in the remaining 9%. In about 75% of the cases, Splash patterns identify sequence sites that overlap more than 50% with the corresponding PROSITE pattern. The procedure is sufficiently rapid to enable its use for daily curation of existing motif and profile databases. Third, our results show that the statistical significance of discovered patterns correlates well with their biological significance. The trypsin subfamily of serine proteases is used to illustrate this method's ability to exhaustively discover all motifs in a family that are statistically and biologically significant. Finally, we discuss applications of sequence patterns to multiple sequence alignment and the training of more sensitive score-based motif models, akin to the procedure used by PSI-BLAST. All results are available at httpl//www.research.ibm.com/spat/.

Algorithms↗
PubMed2004

An architecture for biological information extraction and representation.

MOTIVATIONS: Technological advances in biomedical research are generating a plethora of heterogeneous data at a high rate. There is a critical need for extraction, integration and management tools for information discovery and synthesis from these heterogeneous data. RESULTS: In this paper, we present a general architecture, called ALFA, for information extraction and representation from diverse biological data. The ALFA architecture consists of: (i) a networked, hierarchical, hyper-graph object model for representing information from heterogeneous data sources in a standardized, structured format; and (ii) a suite of integrated, interactive software tools for information extraction and representation from diverse biological data sources. As part of our research efforts to explore this space, we have currently prototyped the ALFA object model and a set of interactive software tools for searching, filtering, and extracting information from scientific text. In particular, we describe BioFerret, a meta-search tool for searching and filtering relevant information from the web, and ALFA Text Viewer, an interactive tool for user-guided extraction, disambiguation, and representation of information from scientific text. We further demonstrate the potential of our tools in integrating the extracted information with experimental data and diagrammatic biological models via the common underlying ALFA representation. CONTACT: aditya_vailaya@agilent.com.

Abstracting and Indexing↗
PubMed2005

Utilizing logical relationships in genomic data to decipher cellular processes.

The wealth of available genomic data has spawned a corresponding interest in computational methods that can impart biological meaning and context to these experiments. Traditional computational methods have drawn relationships between pairs of proteins or genes based on notions of equality or similarity between their patterns of occurrence or behavior. For example, two genes displaying similar variation in expression, over a number of experiments, may be predicted to be functionally related. We have introduced a natural extension of these approaches, instead identifying logical relationships involving triplets of proteins. Triplets provide for various discrete kinds of logic relationships, leading to detailed inferences about biological associations. For instance, a protein C might be encoded within an organism if, and only if, two other proteins A and B are also both encoded within the organism, thus suggesting that gene C is functionally related to genes A and B. The method has been applied fruitfully to both phylogenetic and microarray expression data, and has been used to associate logical combinations of protein activity with disease state phenotypes, revealing previously unknown ternary relationships among proteins, and illustrating the inherent complexities that arise in biological data.

Algorithms↗
PubMed2003

[Regulatory issues of biologically active supplements in Armenia].

Since 1996 by Drug and Medical Technology Agency of the Ministry of Health of the Republic Armenia there has been a record of biologically active supplements, and up to now it numbers 61 products. There has been created a database on biologically active supplements containing relevant information on producers, composition of the product, indications for applying etc. There have been cited some data on examining biologically active supplements and on criteria of such products appraisal. The parameters of not approved Dietary supplements Applications are included and the requirements for Dietary supplements Labels are described in it. There has been cited the division of registration biologically active supplements as per active ingredients and recommendations on use. According to the results of special questionnaire for the Yerevan policlinic doctors, there have been analyzed the spreading area and dynamics biologically active supplements prescription Also there has been estimated the adequacy of their prescription and doctors opinion this product.

Armenia↗
PubMed2002

The TRIPLES database: a community resource for yeast molecular biology.

TRIPLES is a web-accessible database of TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces cerevisiae-a relational database housing nearly half a million data points generated from an ongoing study using large-scale transposon mutagenesis to characterize gene function in yeast. At present, TRIPLES contains three principal data sets (i.e. phenotypic data, protein localization data and expression data) for over 3500 annotated yeast genes as well as several hundred non-annotated open reading frames. In addition, the TRIPLES web site provides online order forms linked to each data set so that users may request any strain or reagent generated from this project free of charge. In response to user requests, the TRIPLES web site has undergone several recent modifications. Our localization data have been supplemented with approximately 500 fluorescent micrographs depicting actual staining patterns observed upon indirect immunofluorescence analysis of indicated epitope-tagged proteins. These localization data, as well as all other data sets within TRIPLES, are now available in full as tab-delimited text. To accommodate increased reagent requests, all orders are now cataloged in a separate database, and users are notified immediately of order receipt and shipment. Also, TRIPLES is one of five sites incorporated into the new functional analysis tool Function Junction provided by the Saccharomyces Genome Database. TRIPLES may be accessed from the Yale Genome Analysis Center (YGAC) homepage at http://ygac.med.yale.edu.

Computer Graphics↗
PubMed2004

The Swiss-Prot protein knowledgebase and ExPASy: providing the plant community with high quality proteomic data and tools.

The Swiss-Prot protein knowledgebase provides manually annotated entries for all species, but concentrates on the annotation of entries from model organisms to ensure the presence of high quality annotation of representative members of all protein families. A specific Plant Protein Annotation Program (PPAP) was started to cope with the increasing amount of data produced by the complete sequencing of plant genomes. Its main goal is the annotation of proteins from the model plant organism Arabidopsis thaliana. In addition to bibliographic references, experimental results, computed features and sometimes even contradictory conclusions, direct links to specialized databases connect amino acid sequences with the current knowledge in plant sciences. As protein families and groups of plant-specific proteins are regularly reviewed to keep up with current scientific findings, we hope that the wealth of information of Arabidopsis origin accumulated in our knowledgebase, and the numerous software tools provided on the Expert Protein Analysis System (ExPASy) web site might help to identify and reveal the function of proteins originating from other plants. Recently, a single, centralized, authoritative resource for protein sequences and functional information, UniProt, was created by joining the information contained in Swiss-Prot, Translation of the EMBL nucleotide sequence (TrEMBL), and the Protein Information Resource-Protein Sequence Database (PIR-PSD). A rising problem is that an increasing number of nucleotide sequences are not being submitted to the public databases, and thus the proteins inferred from such sequences will have difficulties finding their way to the Swiss-Prot or TrEMBL databases.

Arabidopsis↗