NMDA receptors, neural pathways, and protein interaction databases.
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Animals↗
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Results for “Biological databases”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
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Dry work in a wet world: computation in systems biology.
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Computational Biology↗
PCSB--a program collection for structural biology and biophysical chemistry.
We present the first package of Java classes specifically aimed at the handling of structural and biophysical problems. To enable object-oriented programming a basis of fundamental Java classes is required which deals with basic operations of vectors, matrices, amino acid sequences, crystal symmetries and PDB files. Five classes, which carry out these basic operations, were constructed and bundled together with several utility functions in the PCSB package. Furthermore, to demonstrate their applicability and to obtain programs handling common tasks in structural laboratories, we present the first six applications of PCSB. All applications are portable to different platforms and require only the Java Runtime Environment to be installed on the system. available as PDF file.
Algorithms↗
Pathbase: a new reference resource and database for laboratory mouse pathology.
Pathbase (http://www.pathbase.net) is a web accessible database of histopathological images of laboratory mice, developed as a resource for the coding and archiving of data derived from the analysis of mutant or genetically engineered mice and their background strains. The metadata for the images, which allows retrieval and interoperability with other databases, is derived from a series of orthogonal ontologies and controlled vocabularies. One of these controlled vocabularies, MPATH, was developed by the Pathbase Consortium as a formal description of the content of mouse histopathological images. The database currently has over 1000 images on-line with 2000 more under curation and presents a paradigm for the development of future databases dedicated to aspects of experimental biology.
Animals↗
A correlated motif approach for finding short linear motifs from protein interaction networks.
BACKGROUND: An important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches. RESULTS: We propose a novel approach of finding correlated short sequence motifs from protein-protein interaction data to effectively circumvent the above-mentioned limitations. Correlated motifs are those motifs that consistently co-occur only in pairs of interacting protein sequences, and could possibly interact with each other directly or indirectly to mediate interactions. We adopted the (l, d)-motif model and formulate finding the correlated motifs as an (l, d)-motif pair finding problem. We present both an exact algorithm, D-MOTIF, as well as its approximation algorithm, D-STAR to solve this problem. Evaluation on extensive simulated data showed that our approach not only eliminated the need for any prior protein grouping, but is also more robust in extracting motifs from noisy interaction data. Application on two biological datasets (SH3 interaction network and TGFbeta signaling network) demonstrates that the approach can extract correlated motifs that correspond to actual interacting subsequences. CONCLUSION: The correlated motif approach outlined in this paper is able to find correlated linear motifs from sparse and noisy interaction data. This, in turn, will expedite the discovery of novel linear binding motifs, and facilitate the studies of biological pathways mediated by them.
Algorithms↗
A new journal - "Theoretical Biology and Medical Modelling".
Biology has a conceptual basis that allows one to build models and theorize across many life sciences, including medicine and medically-related disciplines. A dearth of good venues for publication has been perceived during a period when bioinformatics, systems analysis and biomathematics are burgeoning. Steps have been taken to provide the sort of journal with a quick turnaround time for manuscripts which is online and freely accessible to all readers, whatever their persuasion or discipline. We have now been running for some time a journal which has had many good papers presented pre-launch, and a steady stream of papers thereafter. The value of this journal as a new venue has already been vindicated. Within a short space of time, we have founded a state-of-the-art electronic journal freely accessible to all in a much sort-after interdisciplinary field that will be of benefit to the thinking life scientist, which must include medically qualified doctors as well as scientists who prefer to build their new hypotheses on basic principles and sound concepts underpinning biology. At the same time, these principles are not sacrosanct and require critical analysis. The journal http://www.tbiomed.com promises to deliver many exciting ideas in the future.
Computational Biology↗
Putting the 'bio' into bioinformatics.
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Computational Biology↗
Bioinformatics service, education and research: the EMBnet and CBI. European Molecular Biology Network Centre of Bioinformatics.
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Computational Biology↗
Complex systems biology: exploring universal statistical and dynamical features in cellular processes.
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Cell Physiological Phenomena↗
Database searching for compounds with similar biological activity using short binary bit string representations of molecules.
In an effort to identify biologically active molecules in compound databases, we have investigated similarity searching using short binary bit strings with a maximum of 54 bit positions. These "minifingerprints" (MFPs) were designed to account for the presence or absence of structural fragments and/or aromatic character, flexibility, and hydrogen-bonding capacity of molecules. MFP design was based on an analysis of distributions of molecular descriptors and structural fragments in two large compound collections. The performance of different MFPs and a reference fingerprint was tested by systematic "one-against-all" similarity searches of molecules in a database containing 364 compounds with different biological activities. For each fingerprint, the most effective similarity cutoff value was determined. An MFP accounting for only 32 structural fragments showed less than 2% false positive similarity matches and correctly assigned on average approximately 40% of the compounds with the same biological activity to a query molecule. Inclusion of three numerical two-dimensional (2D) molecular descriptors increased the performance by 15%. This MFP performed better than a complex 2D fingerprint. At a similarity cutoff value of 0.85, the 2D fingerprint totally eliminated false positives but recognized less than 10% of the compounds within the same activity class.
Cyclooxygenase Inhibitors↗
Meta-analysis of the p53 mutation database for mutant p53 biological activity reveals a methodologic bias in mutation detection.
PURPOSE: Analyses of the pattern of p53 mutations have been essential for epidemiologic studies linking carcinogen exposure and cancer. We were concerned by the inclusion of dubious reports in the p53 databases that could lead to controversial analysis prejudicial to the scientific community. EXPERIMENTAL DESIGN: We used the universal mutation database p53 database (21,717 mutations) combined with a new p53 mutant activity database (2,300 mutants) to perform functional analysis of 1,992 publications reporting p53 alterations. This analysis was done using a statistical approach similar to that of clinical meta-analyses. RESULTS: This analysis reveals that some reports of infrequent mutations are associated with almost normal activities of p53 proteins. These particular mutations are frequently found in studies reporting multiple mutations in one tumor, silent mutations, or lacking mutation hotspots. These reports are often associated with particular methodologies, such as nested PCR, for which key controls are not satisfactory. CONCLUSIONS: We show the importance of accurate functional analysis before inferring any genetic variation. The quality of the p53 databases is essential in order to prevent erroneous analysis and/or conclusions. The availability of functional data from our new p53 web site (http://p53.free.fr and http://www.umd.be:2072/) will allow functional prescreening to identify potential artifactual data.
Databases, Factual↗
Improved database searches for orthologous sequences by conditioning on outgroup sequences.
MOTIVATION: Searches of biological sequence databases are usually focussed on distinguishing significant from random matches. However, the increasing abundance of related sequences on databases present a second challenge: to distinguish the evolutionarily most closely related sequences (often orthologues) from more distantly related homologues. This is particularly important when searching a database of partial sequences, where short orthologous sequences from a non-conserved region will score much more poorly than non-orthologous (outgroup) sequences from a conserved region. RESULTS: Such inferences are shown to be improved by conditioning the search results on the scores of an outgroup sequence. The log-odds score for each target sequence identified on the database has the log-odds score of the outgroup sequence subtracted from it. A test group of Caenorhabditis elegans kinase sequences and their identified C.elegans outgroups were searched against a test database of human Expressed Sequence Tag (EST) sequences, where the sets of true target sequences were known in advance. The outgroup conditioned method was shown to identify 58% more true positives ahead of the first false positive, compared to the straightforward search without an outgroup. A test dataset of 151 proteins drawn from the C.elegans genome, where the putative 'outgroup' was assigned automatically, similarly found 50% more true positives using outgroup conditioning. Thus, outgroup conditioning provides a means to improve the results of database searching with little increase in the search computation time.
Algorithms↗
Using genomic databases for sequence-based biological discovery.
The inherent potential underlying the sequence data produced by the International Human Genome Sequencing Consortium and other systematic sequencing projects is, obviously, tremendous. As such, it becomes increasingly important that all biologists have the ability to navigate through and cull important information from key publicly available databases. The continued rapid rise in available sequence information, particularly as model organism data is generated at breakneck speed, also underscores the necessity for all biologists to learn how to effectively make their way through the expanding "sequence information space." This review discusses some of the more commonly used tools for sequence discovery; tools have been developed for the effective and efficient mining of sequence information. These include LocusLink, which provides a gene-centric view of sequence-based information, as well as the 3 major genome browsers: the National Center for Biotechnology Information Map Viewer, the University of California Santa Cruz Genome Browser, and the European Bioinformatics Institute's Ensembl system. An overview of the types of information available through each of these front-ends is given, as well as information on tutorials and other documentation intended to increase the reader's familiarity with these tools.
Animals↗
MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource based on the first complete plant genome.
Arabidopsis thaliana is the first plant for which the complete genome has been sequenced and published. Annotation of complex eukaryotic genomes requires more than the assignment of genetic elements to the sequence. Besides completing the list of genes, we need to discover their cellular roles, their regulation and their interactions in order to understand the workings of the whole plant. The MIPS Arabidopsis thaliana Database (MAtDB; http://mips.gsf.de/proj/thal/db) started out as a repository for genome sequence data in the European Scientists Sequencing Arabidopsis (ESSA) project and the Arabidopsis Genome Initiative. Our aim is to transform MAtDB into an integrated biological knowledge resource by integrating diverse data, tools, query and visualization capabilities and by creating a comprehensive resource for Arabidopsis as a reference model for other species, including crop plants.
Arabidopsis↗
Calculating similarities between biological activities in the MDL Drug Data Report database.
There are a number of licensed databases that assign biological activities to druglike compounds. The MDL Drug Data Report (MDDR), compiled from the patent literature, is a popular example. It contains several hundred distinct activities, some of which are therapeutic areas (e.g., Antihypertensive) and some of which are related to specific enzymes or receptors (e.g., ACE inhibitor). There are several data mining applications where it would be useful to calculate a similarity between any two activities. Two distinct activity labels can have a significant similarity for a number of reasons: two activities can be nearly synonymous (e.g., CCK B antagonist vs Gastrin antagonist), one activity may be a subset of another (e.g., Dopamine (D2) agonist vs Dopamine agonist), or an activity can be the mechanism by which another activity works (e.g., ACE inhibitor vs Antihypertensive), etc. In an ideal world, similarities for two activities could be calculated simply by comparing the compounds they have in common, but in hand-curated databases such as the MDDR the assignment of activities to compounds are inevitably inconsistent and incomplete. We propose a number of methods of calculating activity-activity similarities that hopefully compensate for errors in hand-curation. Two of these, TIMI and trend vector, show promise. Soft clustering of the activities using a union of similarity methods shows a reasonable association of therapeutic areas with their mechanisms.
Algorithms↗
TRANSFAC database as a bridge between sequence data libraries and biological function.
The TRANSFAC database contains information about regulatory DNA sequences and the proteins (transcription factors) binding to and acting through them. It may thus serve as a dictionary for the biological meaning of these sequence elements. Moreover, the TRANSFAC data can be used to describe these elements, to define consensi and matrices for elements of certain function, and thus to provide means of identifying regulatory signals in newly unravelled genomic sequences.
Base Sequence↗
A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models.
BACKGROUND: G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligands interact and activate GPCRs, leading to signal transduction within cells. Most of these responses are mediated through the interaction of GPCRs with heterotrimeric GTP-binding proteins (G-proteins). Due to the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. Experimental data reported in the literature suggest that heterotrimeric G-proteins interact with parts of the activated receptor at the transmembrane helix-intracellular loop interface. Utilizing this information and membrane topology information, we have developed an intensive exploratory approach to generate a refined library of statistical models (Hidden Markov Models) that predict the coupling preference of GPCRs to heterotrimeric G-proteins. The method predicts the coupling preferences of GPCRs to Gs, Gi/o and Gq/11, but not G12/13 subfamilies. RESULTS: Using a dataset of 282 GPCR sequences of known coupling preference to G-proteins and adopting a five-fold cross-validation procedure, the method yielded an 89.7% correct classification rate. In a validation set comprised of all receptor sequences that are species homologues to GPCRs with known coupling preferences, excluding the sequences used to train the models, our method yields a correct classification rate of 91.0%. Furthermore, promiscuous coupling properties were correctly predicted for 6 of the 24 GPCRs that are known to interact with more than one subfamily of G-proteins. CONCLUSION: Our method demonstrates high correct classification rate. Unlike previously published methods performing the same task, it does not require any transmembrane topology prediction in a preceding step. A web-server for the prediction of GPCRs coupling specificity to G-proteins available for non-commercial users is located at http://bioinformatics.biol.uoa.gr/PRED-COUPLE.
Algorithms↗
Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer.
With the proliferation of related microarray studies by independent groups, a natural step in the analysis of these gene expression data is to combine the results across these studies. However, this raises a variety of issues in the analysis of such data. In this article, we discuss the statistical issues of combining data from multiple gene expression studies. This leads to more complications than those in standard meta-analyses, including different experimental platforms, duplicate spots and complex data structures. We illustrate these ideas using data from four prostate cancer profiling studies. In addition, we develop a simple approach for assessing differential expression using the LASSO method. A combination of the results and the pathway databases are then used to generate candidate biological pathways for cancer.
Algorithms↗