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At least 1,621 records · Page 90Linked to original sources
PubMed2004

Capturing biological information with class-responsibility-collaboration cards.

UNLABELLED: Class-responsibility-collaboration (CRC) cards have been used extensively in the software industry for defining complex object-oriented software requirements. We have adapted this tool to capture information about biological components, collaborators and responsibilities within these collaborations, which is not captured by current annotation tools. CRC cards should provide a common ground that will facilitate communication between biologist and computer scientists. AVAILABILITY: A CRC card template, XML representation and XML schema are freely available at http://people.musc.edu/~zhengw/CRCCard/CRC_Card_Index.html SUPPLEMENTARY INFORMATION: Supplemental Figures 1-4.

Computational Biology↗
PubMed2004

easyLINKAGE: a PERL script for easy and automated two-/multi-point linkage analyses.

UNLABELLED: We have generated the program easyLINKAGE that combines automated setup and performance of linkage analyses and simulation under an easy to handle graphical user interface for Microsoft Windows 2000/XP and standard UNIX systems. The program package supports two-point linkage analyses (FastLink v4.1 and SPLink v1.09), multi-point linkage analyses [GENEHUNTER v2.1, GENEHUNTER-PLUS with the emendation by Kong and Cox v1.2 (allele sharing modelling)] and the simulation package SLINK v2.65, and provides genome-wide as well as chromosomal postscript plots of LOD scores, NPL scores, P-values and other parameters. AVAILABILITY: http://www.uni-wuerzburg.de/nephrologie/molecular_genetics/molecular_genetics.htm SUPPLEMENTARY INFORMATION: Supplementary information is available on the website.

Chromosome Mapping↗
PubMed2004

GLAD: a system for developing and deploying large-scale bioinformatics grid.

MOTIVATION: Grid computing is used to solve large-scale bioinformatics problems with gigabytes database by distributing the computation across multiple platforms. Until now in developing bioinformatics grid applications, it is extremely tedious to design and implement the component algorithms and parallelization techniques for different classes of problems, and to access remotely located sequence database files of varying formats across the grid. In this study, we propose a grid programming toolkit, GLAD (Grid Life sciences Applications Developer), which facilitates the development and deployment of bioinformatics applications on a grid. RESULTS: GLAD has been developed using ALiCE (Adaptive scaLable Internet-based Computing Engine), a Java-based grid middleware, which exploits the task-based parallelism. Two bioinformatics benchmark applications, such as distributed sequence comparison and distributed progressive multiple sequence alignment, have been developed using GLAD.

Computational Biology↗
PubMed2004

Modelling cellular systems with PySCeS.

UNLABELLED: The Python Simulator for Cellular Systems (PySCeS) is an extendable research tool for the numerical analysis and investigation of cellular systems. AVAILABILITY: PySCeS is distributed as Open Source Software under the GNU General Public Licence and is available for download from http://pysces.sourceforge.net CONTACT: bgoli@sun.ac.za.

Cell Physiological Phenomena↗
PubMed2004

Circular genome visualization and exploration using CGView.

UNLABELLED: CGView (Circular Genome Viewer) is a Java application and library for generating high-quality, zoomable maps of circular genomes. It converts XML or tab-delimited input into a graphical map (PNG, JPG or Scalable Vector Graphics format), complete with sequence features, labels, legends and footnotes. In addition to the default full view map, the program can generate a series of hyperlinked maps showing expanded views. The linked maps can be explored using any Web browser, allowing rapid genome browsing and facilitating data sharing. AVAILABILITY: CGView (the standalone application, library or applet), sample input, sample maps and documentation can be obtained from http://wishart.biology.ualberta.ca/cgview/ CONTACT: david.wishart@ualberta.ca.

Chromosome Mapping↗
PubMed2004

PDBML: the representation of archival macromolecular structure data in XML.

SUMMARY: The Protein Data Bank (PDB) has recently released versions of the PDB Exchange dictionary and the PDB archival data files in XML format collectively named PDBML. The automated generation of these XML files is driven by the data dictionary infrastructure in use at the PDB. The correspondences between the PDB dictionary and the XML schema metadata are described as well as the XML representations of PDB dictionaries and data files.

Amino Acid Sequence↗
PubMed2005

High-recall protein entity recognition using a dictionary.

SUMMARY: Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields (CRFs) that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches with that of Maximum Entropy (MaxEnt) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary-the measure of most interest in our intended application. AVAILABILITY: Dictionary HMMs were implemented in Java. Algorithms are available through an information extraction package MINORTHIRD on http://minorthird.sourceforge.net

Algorithms↗
PubMed2005

Data integration and visualization system for enabling conceptual biology.

MOTIVATION: Integration of heterogeneous data in life sciences is a growing and recognized challenge. The problem is not only to enable the study of such data within the context of a biological question but also more fundamentally, how to represent the available knowledge and make it accessible for mining. RESULTS: Our integration approach is based on the premise that relationships between biological entities can be represented as a complex network. The context dependency is achieved by a judicious use of distance measures on these networks. The biological entities and the distances between them are mapped for the purpose of visualization into the lower dimensional space using the Sammon's mapping. The system implementation is based on a multi-tier architecture using a native XML database and a software tool for querying and visualizing complex biological networks. The functionality of our system is demonstrated with two examples: (1) A multiple pathway retrieval, in which, given a pathway name, the system finds all the relationships related to the query by checking available metabolic pathway, transcriptional, signaling, protein-protein interaction and ontology annotation resources and (2) A protein neighborhood search, in which given a protein name, the system finds all its connected entities within a specified depth. These two examples show that our system is able to conceptually traverse different databases to produce testable hypotheses and lead towards answers to complex biological questions.

Computational Biology↗
PubMed2005

A path planning approach for computing large-amplitude motions of flexible molecules.

MOTIVATION: Motion is inherent in molecular interactions. Molecular flexibility must be taken into account in order to develop accurate computational techniques for predicting interactions. Energy-based methods currently used in molecular modeling (i.e. molecular dynamics, Monte Carlo algorithms) are, in practice, only able to compute local motions while accounting for molecular flexibility. However, large-amplitude motions often occur in biological processes. We investigate the application of geometric path planning algorithms to compute such large motions in flexible molecular models. Our purpose is to exploit the efficacy of a geometric conformational search as a filtering stage before subsequent energy refinements. RESULTS: In this paper two kinds of large-amplitude motion are treated: protein loop conformational changes (involving protein backbone flexibility) and ligand trajectories to deep active sites in proteins (involving ligand and protein side-chain flexibility). First studies performed using our two-stage approach (geometric search followed by energy refinements) show that, compared to classical molecular modeling methods, quite similar results can be obtained with a performance gain of several orders of magnitude. Furthermore, our results also indicate that the geometric stage can provide highly valuable information to biologists. AVAILABILITY: The algorithms have been implemented in the general-purpose motion planning software Move3D, developed at LAAS-CNRS. We are currently working on an optimized stand-alone library that will be available to the scientific community.

Algorithms↗
PubMed2005

Tag SNP selection in genotype data for maximizing SNP prediction accuracy.

MOTIVATION: The search for genetic regions associated with complex diseases, such as cancer or Alzheimer's disease, is an important challenge that may lead to better diagnosis and treatment. The existence of millions of DNA variations, primarily single nucleotide polymorphisms (SNPs), may allow the fine dissection of such associations. However, studies seeking disease association are limited by the cost of genotyping SNPs. Therefore, it is essential to find a small subset of informative SNPs (tag SNPs) that may be used as good representatives of the rest of the SNPs. RESULTS: We define a new natural measure for evaluating the prediction accuracy of a set of tag SNPs, and use it to develop a new method for tag SNPs selection. Our method is based on a novel algorithm that predicts the values of the rest of the SNPs given the tag SNPs. In contrast to most previous methods, our prediction algorithm uses the genotype information and not the haplotype information of the tag SNPs. Our method is very efficient, and it does not rely on having a block partition of the genomic region. We compared our method with two state-of-the-art tag SNP selection algorithms on 58 different genotype datasets from four different sources. Our method consistently found tag SNPs with considerably better prediction ability than the other methods. AVAILABILITY: The software is available from the authors on request.

Algorithms↗
PubMed2005

Clustering short time series gene expression data.

MOTIVATION: Time series expression experiments are used to study a wide range of biological systems. More than 80% of all time series expression datasets are short (8 time points or fewer). These datasets present unique challenges. On account of the large number of genes profiled (often tens of thousands) and the small number of time points many patterns are expected to arise at random. Most clustering algorithms are unable to distinguish between real and random patterns. RESULTS: We present an algorithm specifically designed for clustering short time series expression data. Our algorithm works by assigning genes to a predefined set of model profiles that capture the potential distinct patterns that can be expected from the experiment. We discuss how to obtain such a set of profiles and how to determine the significance of each of these profiles. Significant profiles are retained for further analysis and can be combined to form clusters. We tested our method on both simulated and real biological data. Using immune response data we show that our algorithm can correctly detect the temporal profile of relevant functional categories. Using Gene Ontology analysis we show that our algorithm outperforms both general clustering algorithms and algorithms designed specifically for clustering time series gene expression data. AVAILABILITY: Information on obtaining a Java implementation with a graphical user interface (GUI) is available from http://www.cs.cmu.edu/~jernst/st/ SUPPLEMENTARY INFORMATION: Available at http://www.cs.cmu.edu/~jernst/st/

Algorithms↗
PubMed2005

In silico identification of functional regions in proteins.

MOTIVATION: In silico prediction of functional regions on protein surfaces, i.e. sites of interaction with DNA, ligands, substrates and other proteins, is of utmost importance in various applications in the emerging fields of proteomics and structural genomics. When a sufficient number of homologs is found, powerful prediction schemes can be based on the observation that evolutionarily conserved regions are often functionally important, typically, only the principal functionally important region of the protein is detected, while secondary functional regions with weaker conservation signals are overlooked. Moreover, it is challenging to unambiguously identify the boundaries of the functional regions. METHODS: We present a new methodology, called PatchFinder, that automatically identifies patches of conserved residues that are located in close proximity to each other on the protein surface. PatchFinder is based on the following steps: (1) Assignment of conservation scores to each amino acid position on the protein surface. (2) Assignment of a score to each putative patch, based on its likelihood to be functionally important. The patch of maximum likelihood is considered to be the main functionally important region, and the search is continued for non-overlapping patches of secondary importance. RESULTS: We examined the accuracy of the method using the IGPS enzyme, the SH2 domain and a benchmark set of 112 proteins. These examples demonstrated that PatchFinder is capable of identifying both the main and secondary functional patches. AVAILABILITY: The PatchFinder program is available at: http://ashtoret.tau.ac.il/~nimrodg/

Algorithms↗
PubMed2005

YeastHub: a semantic web use case for integrating data in the life sciences domain.

MOTIVATION: As the semantic web technology is maturing and the need for life sciences data integration over the web is growing, it is important to explore how data integration needs can be addressed by the semantic web. The main problem that we face in data integration is a lack of widely-accepted standards for expressing the syntax and semantics of the data. We address this problem by exploring the use of semantic web technologies-including resource description framework (RDF), RDF site summary (RSS), relational-database-to-RDF mapping (D2RQ) and native RDF data repository-to represent, store and query both metadata and data across life sciences datasets. RESULTS: As many biological datasets are presently available in tabular format, we introduce an RDF structure into which they can be converted. Also, we develop a prototype web-based application called YeastHub that demonstrates how a life sciences data warehouse can be built using a native RDF data store (Sesame). This data warehouse allows integration of different types of yeast genome data provided by different resources in different formats including the tabular and RDF formats. Once the data are loaded into the data warehouse, RDF-based queries can be formulated to retrieve and query the data in an integrated fashion. AVAILABILITY: The YeastHub website is accessible via the following URL: http://yeasthub.gersteinlab.org.

Biology↗
PubMed2005

Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data.

MOTIVATION: The classification of high-dimensional data is always a challenge to statistical machine learning. We propose a novel method named shallow feature selection that assigns each feature a probability of being selected based on the structure of training data itself. Independent of particular classifiers, the high dimension of biodata can be fleetly reduced to an applicable case for consequential processing. Moreover, to improve both efficiency and performance of classification, these prior probabilities are further used to specify the distributions of top-level hyperparameters in hierarchical models of Bayesian neural network (BNN), as well as the parameters in Gaussian process models. RESULTS: Three BNN approaches were derived and then applied to identify ovarian cancer from NCI's high-resolution mass spectrometry data, which yielded an excellent performance in 1000 independent k-fold cross validations (k = 2,...,10). For instance, indices of average sensitivity and specificity of 98.56 and 98.42%, respectively, were achieved in the 2-fold cross validations. Furthermore, only one control and one cancer were misclassified in the leave-one-out cross validation. Some other popular classifiers were also tested for comparison. AVAILABILITY: The programs implemented in MatLab, R and Neal's fbm.2004-11-10.

Bayes Theorem↗
PubMed2005

Conservative extraction of over-represented extensible motifs.

MOTIVATION: The discovery of motifs in biosequences is frequently torn between the rigidity of the model on the one hand and the abundance of candidates on the other. In particular, the variety of motifs described by strings that include 'don't care' (dot) patterns escalates exponentially with the length of the motif, and this gets only worse if a dot is allowed to stretch up to some prescribed maximum length. This circumstance tends to generate daunting computational burdens, and often gives rise to tables that are impossible to visualize and digest. This is unfortunate, as it seems to preclude precisely those massive analyses that have become conceivable with the increasing availability of massive genomic and protein data. Although a part of the problem is endemic, another part of it seems rooted in the various characterizations offered for the notion of a motif, that are typically based either on syntax or on statistics alone. It seems worthwhile to consider alternatives that result from a prudent combination of these two aspects in the model. RESULTS: We introduce and study a notion of extensible motif in a sequence which tightly combines the structure of the motif pattern, as described by its syntactic specification, with the statistical measure of its occurrence count. We show that a combination of appropriate saturation conditions (expressed in terms of minimum number of dots compatible with a given list of occurrences) and the monotonicity of probabilistic scores over regions of constant frequency afford us significant parsimony in the generation and testing of candidate over-represented motifs. The merits of the method are documented by the results obtained in implementation, which specifically targeted protein sequence families. In all cases tested, the motif reported in PROSITE as the most important in terms of functional/structural relevance emerges among the top 30 extensible motifs returned by our algorithm, often right at the top. Of equal importance seems the fact that the sets of all surprising motifs returned in each experiment are extracted faster and come in much more manageable sizes than would be obtained in the absence of saturation constrains. AVAILABILITY: This software will be available for use with the suite of tools at www.research.ibm.com/bioinformatics.

Algorithms↗
PubMed2005

Automatic detection of subsystem/pathway variants in genome analysis.

MOTIVATION: Proteins work together in pathways and networks, collectively comprising the cellular machinery. A subsystem (a generalization of pathway concept) is a group of related functional roles (such as enzymes) jointly involved in a specific aspect of the cellular machinery. Subsystems provide a natural framework for comparative genome analysis and functional annotation. A subsystem may be implemented in a number of different functional variants in individual species. In order to reliably project functional assignments across multiple genomes, we have to be able to identify the variants implemented in each genome. The analysis of such variants across diverse species is an interesting problem by itself and may provide new evolutionary insights. However, no computational techniques are presently available for an automated detection and analysis of subsystem variants. RESULTS: Here we formulate the subsystem variant detection problem as finding the minimum number of subgraphs of a subsystem, which is represented as a graph, and solve the optimization problem by integer programming approach. The performance of our method was tested on subsystems encoded in the SEED, a genomic integration platform developed by the Fellowship for Interpretation of Genomes as a component of a large-scale effort on comparative analysis and annotation of multiple diverse genomes. Here we illustrate the results obtained for two expert-encoded subsystems of the biosynthesis of Coenzyme A and FMN/FAD cofactors. Applications of variant detection, to support genomic annotations and to assess divergence of species, are briefly discussed in the context of these universally conserved and essential metabolic subsystems. SUPPLEMENTARY INFORMATION: The details of the variant detection results are available at http://ffas.burnham.org/svar/supp.html.

Animals↗
PubMed2004

The SBW-MATLAB interface.

SUMMARY: The SBW-MATLAB Interface allows MATLAB users to take advantage of the wide variety of tools available through SBW, the Systems Biology Workbench (Sauro et al. (2003) OMICS, 7, 355-372). It also enables MATLAB users to themselves create SBW-enabled tools which can be freely distributed.

Computer Communication Networks↗