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At least 883 records · Page 49Linked to original sources
PubMed2004

VisANT: an online visualization and analysis tool for biological interaction data.

BACKGROUND: New techniques for determining relationships between biomolecules of all types--genes, proteins, noncoding DNA, metabolites and small molecules--are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems. RESULTS: We present VisANT, an application for integrating biomolecular interaction data into a cohesive, graphical interface. This software features a multi-tiered architecture for data flexibility, separating back-end modules for data retrieval from a front-end visualization and analysis package. VisANT is a freely available, open-source tool for researchers, and offers an online interface for a large range of published data sets on biomolecular interactions, including those entered by users. This system is integrated with standard databases for organized annotation, including GenBank, KEGG and SwissProt. VisANT is a Java-based, platform-independent tool suitable for a wide range of biological applications, including studies of pathways, gene regulation and systems biology. CONCLUSION: VisANT has been developed to provide interactive visual mining of biological interaction data sets. The new software provides a general tool for mining and visualizing such data in the context of sequence, pathway, structure, and associated annotations. Interaction and predicted association data can be combined, overlaid, manipulated and analyzed using a variety of built-in functions. VisANT is available at http://visant.bu.edu.

Animals↗
PubMed2005

Pseudomonas aeruginosa Genome Database and PseudoCAP: facilitating community-based, continually updated, genome annotation.

Using the Pseudomonas aeruginosa Genome Project as a test case, we have developed a database and submission system to facilitate a community-based approach to continually updated genome annotation (http://www.pseudomonas.com). Researchers submit proposed annotation updates through one of three web-based form options which are then subjected to review, and if accepted, entered into both the database and log file of updates with author acknowledgement. In addition, a coordinator continually reviews literature for suitable updates, as we have found such reviews to be the most efficient. Both the annotations database and updates-log database have Boolean search capability with the ability to sort results and download all data or search results as tab-delimited files. To complement this peer-reviewed genome annotation, we also provide a linked GBrowse view which displays alternate annotations. Additional tools and analyses are also integrated, including PseudoCyc, and knockout mutant information. We propose that this database system, with its focus on facilitating flexible queries of the data and providing access to both peer-reviewed annotations as well as alternate annotation information, may be a suitable model for other genome projects wishing to use a continually updated, community-based annotation approach. The source code is freely available under GNU General Public Licence.

DNA, Bacterial↗
PubMed2006

Automated SNP detection from a large collection of white spruce expressed sequences: contributing factors and approaches for the categorization of SNPs.

BACKGROUND: High-throughput genotyping technologies represent a highly efficient way to accelerate genetic mapping and enable association studies. As a first step toward this goal, we aimed to develop a resource of candidate Single Nucleotide Polymorphisms (SNP) in white spruce (Picea glauca [Moench] Voss), a softwood tree of major economic importance. RESULTS: A white spruce SNP resource encompassing 12,264 SNPs was constructed from a set of 6,459 contigs derived from Expressed Sequence Tags (EST) and by using the bayesian-based statistical software PolyBayes. Several parameters influencing the SNP prediction were analysed including the a priori expected polymorphism, the probability score (PSNP), and the contig depth and length. SNP detection in 3' and 5' reads from the same clones revealed a level of inconsistency between overlapping sequences as low as 1%. A subset of 245 predicted SNPs were verified through the independent resequencing of genomic DNA of a genotype also used to prepare cDNA libraries. The validation rate reached a maximum of 85% for SNPs predicted with either PSNP > or = 0.95 or > or = 0.99. A total of 9,310 SNPs were detected by using PSNP > or = 0.95 as a criterion. The SNPs were distributed among 3,590 contigs encompassing an array of broad functional categories, with an overall frequency of 1 SNP per 700 nucleotide sites. Experimental and statistical approaches were used to evaluate the proportion of paralogous SNPs, with estimates in the range of 8 to 12%. The 3,789 coding SNPs identified through coding region annotation and ORF prediction, were distributed into 39% nonsynonymous and 61% synonymous substitutions. Overall, there were 0.9 SNP per 1,000 nonsynonymous sites and 5.2 SNPs per 1,000 synonymous sites, for a genome-wide nonsynonymous to synonymous substitution rate ratio (Ka/Ks) of 0.17. CONCLUSION: We integrated the SNP data in the ForestTreeDB database along with functional annotations to provide a tool facilitating the choice of candidate genes for mapping purposes or association studies.

Algorithms↗
PubMedDate not supplied

Correlation between gene expression and GO semantic similarity.

This research analyzes some aspects of the relationship between gene expression, gene function, and gene annotation. Many recent studies are implicitly based on the assumption that gene products that are biologically and functionally related would maintain this similarity both in their expression profiles as well as in their Gene Ontology (GO) annotation. We analyze how accurate this assumption proves to be using real publicly available data. We also aim to validate a measure of semantic similarity for GO annotation. We use the Pearson correlation coefficient and its absolute value as a measure of similarity between expression profiles of gene products. We explore a number of semantic similarity measures (Resnik, Jiang, and Lin) and compute the similarity between gene products annotated using the GO. Finally, we compute correlation coefficients to compare gene expression similarity against GO semantic similarity. Our results suggest that the Resnik similarity measure outperforms the others and seems better suited for use in Gene Ontology. We also deduce that there seems to be correlation between semantic similarity in the GO annotation and gene expression for the three GO ontologies. We show that this correlation is negligible up to a certain semantic similarity value; then, for higher similarity values, the relationship trend becomes almost linear. These results can be used to augment the knowledge provided by clustering algorithms and in the development of bioinformatic tools for finding and characterizing gene products.

Algorithms↗
PubMed2003

Three-dimensional database of subcortical electrophysiology for image-guided stereotactic functional neurosurgery.

We present a method of constructing a database of intraoperatively observed human subcortical electrophysiology. In this approach, patient electrophysiological data are standardized using a multiparameter coding system, annotated to their respective magnetic resonance images (MRIs), and nonlinearly registered to a high-resolution MRI reference brain. Once registered, we are able to demonstrate clustering of like interpatient physiologic responses within the thalamus, globus pallidus, subthalamic nucleus, and adjacent structures. These data may in turn be registered to a three-dimensional patient MRI within our image-guided visualization program enabling prior to surgery the delineation of surgical targets, anatomy with high probability of containing specific cell types, and functional borders. The functional data were obtained from 88 patients (106 procedures) via microelectrode recording and electrical stimulation performed during stereotactic neurosurgery at the London Health Sciences Centre. Advantages of this method include the use of nonlinear registration to accommodate for interpatient anatomical variability and the avoidance of digitized versions of printed atlases of anatomy as a common database coordinate system. The resulting database is expandable, easily searched using a graphical user interface, and provides a visual representation of functional organization within the deep brain.

Brain↗
PubMed2005

Overcoming obstacles to collecting narrative data from eye care professionals at the point-of-care.

Capturing the nuances of clinical observations in an electronic format has been a major challenge in implementing electronic health records. In a formative evaluation study using three different methodologies, we identified that the greatest obstacle to point-of-care data entry for eye care was supporting free text annotation of clinical observations. To overcome this obstacle, we developed an approach that captures an image of a free text entry and associates this image with related data elements in an encounter note. Through simulated patient studies, we observed that this approach successfully supported complex documentation at the point of care by clinicians.

Attitude to Computers↗
PubMed2003

CLICK and EXPANDER: a system for clustering and visualizing gene expression data.

MOTIVATION: Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering genes based on their expression patterns. RESULTS: We present a novel clustering algorithm, called CLICK, and its applications to gene expression analysis. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clusters. We report on the application of CLICK to a variety of gene expression data sets. In all those applications it outperformed extant algorithms according to several common figures of merit. We also point out that CLICK can be successfully used for the identification of common regulatory motifs in the upstream regions of co-regulated genes. Furthermore, we demonstrate how CLICK can be used to accurately classify tissue samples into disease types, based on their expression profiles. Finally, we present a new java-based graphical tool, called EXPANDER, for gene expression analysis and visualization, which incorporates CLICK and several other popular clustering algorithms. AVAILABILITY: http://www.cs.tau.ac.il/~rshamir/expander/expander.html

Algorithms↗
PubMed2006

The plant structure ontology, a unified vocabulary of anatomy and morphology of a flowering plant.

Formal description of plant phenotypes and standardized annotation of gene expression and protein localization data require uniform terminology that accurately describes plant anatomy and morphology. This facilitates cross species comparative studies and quantitative comparison of phenotypes and expression patterns. A major drawback is variable terminology that is used to describe plant anatomy and morphology in publications and genomic databases for different species. The same terms are sometimes applied to different plant structures in different taxonomic groups. Conversely, similar structures are named by their species-specific terms. To address this problem, we created the Plant Structure Ontology (PSO), the first generic ontological representation of anatomy and morphology of a flowering plant. The PSO is intended for a broad plant research community, including bench scientists, curators in genomic databases, and bioinformaticians. The initial releases of the PSO integrated existing ontologies for Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa); more recent versions of the ontology encompass terms relevant to Fabaceae, Solanaceae, additional cereal crops, and poplar (Populus spp.). Databases such as The Arabidopsis Information Resource, Nottingham Arabidopsis Stock Centre, Gramene, MaizeGDB, and SOL Genomics Network are using the PSO to describe expression patterns of genes and phenotypes of mutants and natural variants and are regularly contributing new annotations to the Plant Ontology database. The PSO is also used in specialized public databases, such as BRENDA, GENEVESTIGATOR, NASCArrays, and others. Over 10,000 gene annotations and phenotype descriptions from participating databases can be queried and retrieved using the Plant Ontology browser. The PSO, as well as contributed gene associations, can be obtained at www.plantontology.org.

Gene Expression Regulation, Plant↗
PubMed2006

Semiparametric approach to characterize unique gene expression trajectories across time.

BACKGROUND: A semiparametric approach was used to identify groups of cDNAs and genes with distinct expression profiles across time and overcome the limitations of clustering to identify groups. The semiparametric approach allows the generalization of mixtures of distributions while making no specific parametric assumptions about the distribution of the hidden heterogeneity of the cDNAs. The semiparametric approach was applied to study gene expression in the brains of Apis mellifera ligustica honey bees raised in two colonies (A. m. mellifera and ligustica) with consistent patterns across five maturation ages. RESULTS: The semiparametric approach provided unambiguous criteria to detect groups of genes, trajectories and probability of gene membership to groups. The semiparametric results were cross-validated in both colony data sets. Gene Ontology analysis enhanced by genome annotation helped to confirm the semiparametric results and revealed that most genes with similar or related neurobiological function were assigned to the same group or groups with similar trajectories. Ten groups of genes were identified and nine groups had highly similar trajectories in both data sets. Differences in the trajectory of the reminder group were consistent with reports of accelerated maturation in ligustica colonies compared to mellifera colonies. CONCLUSION: The combination of microarray technology, genomic information and semiparametric analysis provided insights into the genomic plasticity and gene networks linked to behavioral maturation in the honey bee.

Age Factors↗
PubMed2005

Identification of estrogen-responsive genes using a genome-wide analysis of promoter elements for transcription factor binding sites.

We developed a pipeline to identify novel genes regulated by the steroid hormone-dependent transcription factor, estrogen receptor, through a systematic analysis of upstream regions of all human and mouse genes. We built a data base of putative promoter regions for 23,077 human and 19,984 mouse transcripts from National Center for Biotechnology Information annotation and 8793 human and 6785 mouse promoters from the Data Base of Transcriptional Start Sites. We used this data base of putative promoters to identify potential targets of estrogen receptor by identifying estrogen response elements (EREs) in their promoters. Our program correctly identified EREs in genes known to be regulated by estrogen in addition to several new genes whose putative promoters contained EREs. We validated six genes (KIAA1243, NRIP1, MADH9, NME3, TPD52L, and ABCG2) to be estrogen-responsive in MCF7 cells using reverse transcription PCR. To allow for extensibility of our program in identifying targets of other transcription factors, we have built a Web interface to access our data base and programs. Our Web-based program for Promoter Analysis of Genome, PAGen@UIC, allows a user to identify putative target genes for vertebrate transcription factors through the analysis of their upstream sequences. The interface allows the user to search the human and mouse promoter data bases for potential target genes containing one or more listed transcription factor binding sites (TFBSs) in their upstream elements, using either regular expression-based consensus or position weight matrices. The data base can also be searched for promoters harboring user-defined TFBSs given as a consensus or a position weight matrix. Furthermore, the user can retrieve putative promoter sequences for any given gene together with identified TFBSs located on its promoter. Orthologous promoters are also analyzed to determine conserved elements.

Algorithms↗
PubMed2005

Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation.

BACKGROUND: Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties. RESULTS: We reconstructed a genome-scale metabolic network of S. aureus strain N315. This reconstruction, termed iSB619, consists of 619 genes that catalyze 640 metabolic reactions. For 91% of the reactions, open reading frames are explicitly linked to proteins and to the reaction. All but three of the metabolic reactions are both charge and elementally balanced. The reaction list is the most complete to date for this pathogen. When the capabilities of the reconstructed network were analyzed in the context of maximal growth, we formed hypotheses regarding growth requirements, the efficiency of growth on different carbon sources, and potential drug targets. These hypotheses can be tested experimentally and the data gathered can be used to improve subsequent versions of the reconstruction. CONCLUSION: iSB619 represents comprehensive biochemically and genetically structured information about the metabolism of S. aureus to date. The reconstructed metabolic network can be used to predict cellular phenotypes and thus advance our understanding of a troublesome pathogen.

Bacterial Proteins↗
PubMed2005

Concept-based annotation of enzyme classes.

MOTIVATION: Given the explosive growth of biomedical data as well as the literature describing results and findings, it is getting increasingly difficult to keep up to date with new information. Keeping databases synchronized with current knowledge is a time-consuming and expensive task-one which can be alleviated by automatically gathering findings from the literature using linguistic approaches. We describe a method to automatically annotate enzyme classes with disease-related information extracted from the biomedical literature for inclusion in such a database. RESULTS: Enzyme names for the 3901 enzyme classes in the BRENDA database, a repository for quantitative and qualitative enzyme information, were identified in more than 100,000 abstracts retrieved from the PubMed literature database. Phrases in the abstracts were assigned to concepts from the Unified Medical Language System (UMLS) utilizing the MetaMap program, allowing for the identification of disease-related concepts by their semantic fields in the UMLS ontology. Assignments between enzyme classes and diseases were created based on their co-occurrence within a single sentence. False positives could be removed by a variety of filters including minimum number of co-occurrences, removal of sentences containing a negation and the classification of sentences based on their semantic fields by a Support Vector Machine. Verification of the assignments with a manually annotated set of 1500 sentences yielded favorable results of 92% precision at 50% recall, sufficient for inclusion in a high-quality database. AVAILABILITY: Source code is available from the author upon request. SUPPLEMENTARY INFORMATION: ftp.uni-koeln.de/institute/biochemie/pub/brenda/info/diseaseSupp.pdf.

Algorithms↗
PubMed2005

Columba: an integrated database of proteins, structures, and annotations.

BACKGROUND: Structural and functional research often requires the computation of sets of protein structures based on certain properties of the proteins, such as sequence features, fold classification, or functional annotation. Compiling such sets using current web resources is tedious because the necessary data are spread over many different databases. To facilitate this task, we have created COLUMBA, an integrated database of annotations of protein structures. DESCRIPTION: COLUMBA currently integrates twelve different databases, including PDB, KEGG, Swiss-Prot, CATH, SCOP, the Gene Ontology, and ENZYME. The database can be searched using either keyword search or data source-specific web forms. Users can thus quickly select and download PDB entries that, for instance, participate in a particular pathway, are classified as containing a certain CATH architecture, are annotated as having a certain molecular function in the Gene Ontology, and whose structures have a resolution under a defined threshold. The results of queries are provided in both machine-readable extensible markup language and human-readable format. The structures themselves can be viewed interactively on the web. CONCLUSION: The COLUMBA database facilitates the creation of protein structure data sets for many structure-based studies. It allows to combine queries on a number of structure-related databases not covered by other projects at present. Thus, information on both many and few protein structures can be used efficiently. The web interface for COLUMBA is available at http://www.columba-db.de.

Base Sequence↗
PubMed1999

DNA Data Bank of Japan dealing with large-scale data submission.

The DNA Data Bank of Japan (DDBJ) (http//:www.ddbj.nig.ac.jp) has developed a software system for mass submissions to cope with a recent expansion of EST and genome data submissions. The system is composed of four parts, the WWW data submission, large-scale submission, submission management and storing. Using this system one can submit data on a large number of sequences or a very long sequence while checking the consistency between the annotation and sequence without much effort. DDBJ has received large scale data of Homo sapiens, Arabidopsis and Pyrococcus from Japanese researchers who made full use of the new submission system.

Animals↗
PubMed2005

CLU: a new algorithm for EST clustering.

BACKGROUND: The continuous flow of EST data remains one of the richest sources for discoveries in modern biology. The first step in EST data mining is usually associated with EST clustering, the process of grouping of original fragments according to their annotation, similarity to known genomic DNA or each other. Clustered EST data, accumulated in databases such as UniGene, STACK and TIGR Gene Indices have proven to be crucial in research areas from gene discovery to regulation of gene expression. RESULTS: We have developed a new nucleotide sequence matching algorithm and its implementation for clustering EST sequences. The program is based on the original CLU match detection algorithm, which has improved performance over the widely used d2_cluster. The CLU algorithm automatically ignores low-complexity regions like poly-tracts and short tandem repeats. CONCLUSION: CLU represents a new generation of EST clustering algorithm with improved performance over current approaches. An early implementation can be applied in small and medium-size projects. The CLU program is available on an open source basis free of charge. It can be downloaded from http://compbio.pbrc.edu/pti.

Algorithms↗
PubMed2005

The GATO gene annotation tool for research laboratories.

Large-scale genome projects have generated a rapidly increasing number of DNA sequences. Therefore, development of computational methods to rapidly analyze these sequences is essential for progress in genomic research. Here we present an automatic annotation system for preliminary analysis of DNA sequences. The gene annotation tool (GATO) is a Bioinformatics pipeline designed to facilitate routine functional annotation and easy access to annotated genes. It was designed in view of the frequent need of genomic researchers to access data pertaining to a common set of genes. In the GATO system, annotation is generated by querying some of the Web-accessible resources and the information is stored in a local database, which keeps a record of all previous annotation results. GATO may be accessed from everywhere through the internet or may be run locally if a large number of sequences are going to be annotated. It is implemented in PHP and Perl and may be run on any suitable Web server. Usually, installation and application of annotation systems require experience and are time consuming, but GATO is simple and practical, allowing anyone with basic skills in informatics to access it without any special training. GATO can be downloaded at [http://mariwork.iq.usp.br/gato/]. Minimum computer free space required is 2 MB.

Biomedical Research↗
PubMed2003

OrthoMCL: identification of ortholog groups for eukaryotic genomes.

The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of "recent" paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum, and Escherichia coli). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns (http://www.cbil.upenn.edu/gene-family). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.

Animals↗
PubMed2002

Grouping and identification of sequence tags (GRIST): bioinformatics tools for the NEIBank database.

NEIBank is a project to develop and organize genomics and bioinformatics resources for the eye. As part of this effort, tools have been developed for bioinformatics analysis and web based display of data from expressed sequence tag (EST) analyses. EST sequences are identified and formed into groups or clusters representing related transcripts from the same gene. This is carried out by a rules-based procedure called GRIST (GRouping and Identification of Sequence Tags) that uses sequence match parameters derived from BLAST programs. Linked procedures are used to eliminate non-mRNA contaminants. All data are assembled in a relational database and assembled for display as web pages with annotations and links to other informatics resources. Genome projects generate huge amounts of data that need to be classified and organized to become easily accessible to the research community. GRIST provides a useful tool for assembling and displaying the results of EST analyses. The NEIBank web site contains a growing set of pages cataloging the known transcriptional repertoire of eye tissues, derived from new NEIBank cDNA libraries and from eye-related data deposited in the dbEST section of GenBank.

Animals↗