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NIDDK data repository: A central collection of clinical trial data

BMC Medical Informatics and Decision Making 2006年第1期6卷 1-6页

作者： Cuticchia, A. Jamie Cooley, Philip C. Hall, R. David Qin, Ying Duke Institute for Genome Sciences and Policy Duke University Durham NC United States Bioinformatics Program RTI International Research Triangle Park NC United States

Background: The National Institute of Diabetes and Digestive and Kidney Diseases have established central repositories for the collection of DNA, biological samples, and clinical data to be catalogued at a single site. Here we present an overview of the site which stores the clinical data and links to biospecimens. Description: The NIDDK Data repository is a web-enabled resource cataloguing clinical trial data and supporting information from NIDDK supported studies. The Data Repository allows for the co-location of multiple electronic datasets that were created as part of clinical investigations. The Data Repository does not serve the role of a Data Coordinating Center, but rather as a warehouse for the clinical findings once the trials have been completed. Because both biological and genetic samples are collected from many of the studies, a data management system for the cataloguing and retrieval of samples was developed. Conclusion: The Data Repository provides a unique resource for researchers in the clinical areas supported by NIDDK. In addition to providing a warehouse of data, Data Repository staff work with the users to educate them on the datasets as well as assist them in the acquisition of multiple data sets for cross-study analysis. Unlike the majority of biological databases, the Data Repository acts both as a catalogue for data, biosamples, and genetic materials and as a central processing point for the requests for all biospecimens. Due to regulations on the use of clinical data, the ultimate release of that data is governed under NIDDK data release policies. The Data Repository serves as the conduit for such requests. © 2006 Cuticchia et al;licensee BioMed Central Ltd.

关键词： Interstitial Cystitis Data Repository Liver Transplant Recipient Central Repository Sample Request

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Combinatorial model for sequence and spatial motif discovery in short sequence fragments: Examples from β-barrel membrane proteins

Combinatorial model for sequence and spatial motif discovery...

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28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06

作者： Jackups Jr., Ronald Liang, Jie Program in Bioinformatics Department of Bioengineering University of Illinois at Chicago 851 S. Morgan Street Chicago IL 60607-7052 United States

ISBN: (纸本)1424400325

Motifs are over-represented sequence or spatial patterns appearing in proteins. They often play important roles in maintaining protein stability and in facilitating protein functions. When motifs are located in short sequence fragments, as in transmembrane domains that are only 10-20 residues in length, and when there is only very limited data, it is difficult to identify motifs. In this study, we develop combinatorial models for assessing statistically significant sequence and spatial patterns. We show our method can uncover previously unknown sequence and spatial motifs in β-barrel membrane proteins. © 2006 IEEE.

关键词： Proteins

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The generation challenge programme platform: Semantic standards and workbench for crop science

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International Journal of Plant Genomics 2008年第1期2008卷 369601页

作者： Bruskiewich, Richard Senger, Martin Davenport, Guy Ruiz, Manuel Rouard, Mathieu Hazekamp, Tom Takeya, Masaru Doi, Koji Satoh, Kouji Costa, Marcos Simon, Reinhard Balaji, Jayashree Akintunde, Akinnola Mauleon, Ramil Wanchana, Samart Shah, Trushar Anacleto, Mylah Portugal, Arllet Ulat, Victor Jun Thongjuea, Supat Braak, Kyle Ritter, Sebastian Dereeper, Alexis Skofic, Milko Rojas, Edwin Martins, Natalia Pappas, Georgios Alamban, Ryan Almodiel, Roque Barboza, Lord Hendrix Detras, Jeffrey Manansala, Kevin Mendoza, Michael Jonathan Morales, Jeffrey Peralta, Barry Valerio, Rowena Zhang, Yi Gregorio, Sergio Hermocilla, Joseph Echavez, Michael Yap, Jan Michael Farmer, Andrew Schiltz, Gary Lee, Jennifer Casstevens, Terry Jaiswal, Pankaj Meintjes, Ayton Wilkinson, Mark Good, Benjamin Wagner, James Morris, Jane Marshall, David Collins, Anthony Kikuchi, Shoshi Metz, Thomas McLaren, Graham Van Hintum, Theo Crop Research Informatics Laboratory International Rice Research Institute (IRRI) DAPO Box 7777 Metro Manila Philippines Crop Research Informatics Laboratory International Maize and Wheat Improvement Center (CIMMYT) DF 06600 Mexico Apdo. Postal 6-641 Mexico Centre International de Recherche Agronomique Pour le Développement (CIRAD) 34398 Montpellier Cedex 5 Avenue Agropolis France Bioversity International 00057 Maccarese (Fiumicino) Rome Via dei Tre Denari 472/a Italy National Institute for Agrobiological Sciences (NIAS) Tsukuba Ibaraki 305-8602 Kannondai 2-1-2 Japan Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) Brasilia DF 70770-900 Parque Estaçao Biologia Final W5 Norte Brazil Centro Internacional de la Papa (CIP) Lima 12 Avenida La Molina 1895 La Molina Peru International Crops Research Institute for the Semi-Arid Tropics Patancheru Andhra Pradesh 502324 India International Center for Agricultural Research in the Dry Areas Aleppo P.O. Box 5466 Syrian Arab Republic National Center for Genetic Engineering and Biotechnology 113 Thailand Science Park Klong Luang Pathumthani 12120 Phahonyothin Road Klong 1 Thailand Institute of Computer Science College of Arts and Sciences University of the Philippines Los Banos Laguna 4031 Philippines Department of Computer Science University of the Philippines Melchor Hall Diliman Quezon City 1101 Philippines National Center for Genome Resources Santa Fe NM 87505 2935 Rodeo Park Drive East United States Scottish Crop Research Institute Invergowrie Dundee DD2 5DA United Kingdom Department of Plant Breeding Cornell University Ithaca NY 14853 United States African Centre for Gene Technologies Lynnwood Ridge 0040 P.O. Box 75011 South Africa Department of Medical Genetics Faculty of Medicine University of British Columbia Vancouver BC V6T 1Z3 Canada School of Computing Science Simon Fraser Universtiy Burnaby BC V5A 1S6 8888 University Drive Canada Bioinformatics Graduate Program Genome Sciences Centre BC

The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform;(ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive, high-throughput analyses of project data;(iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making.

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Nonpolypoid neoplastic lesions of the colorectal mucosa

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GASTROINTESTINAL ENDOSCOPY 2008年第4期68卷 S3-S47页

作者： Kudo, Shin ei Lambert, Rene Allen, John I. Fujii, Hiroaki Fujii, Takahiro Kashida, Hiroshi Matsuda, Takahisa Mori, Masaki Saito, Hiroshi Shimoda, Tadakazu Tanaka, Shinji Watanabe, Hidenobu Sung, Joseph J. Feld, Andrew D. Inadomi, John M. O'Brien, Michael J. Lieberman, David A. Ransohoff, David F. Soetikno, Roy M. Triadafilopoulos, George Zauber, Ann Teixeira, Claudio Rolim Rey, Jean Franigois Jaramillo, Edgar Rubio, Carlos A. Van Gossum, Andre Jung, Michael Vieth, Michael Jass, Jeremy R. Hurlstone, Paul D. [a]Current affiliations: Hiroaki Fujii MD Department of Pathology (II) Juntendo University School of Medicine Tokyo Japan. Takahiro Fujii MD TF Clinic Ginza Tokyo Japan. Hiroshi Kashida MD Shin ei Kudo MD Digestive Disease Center Northern Yokohama Hospital Showa University Yokohama Japan. Takahisa Matsuda MD Division of Endoscopy Tadakazu Shimoda MD Clinical Laboratory Division National Cancer Centre Hospital Tokyo Japan. Masaki Mori MD PhD FACS Department of Surgery Medical Institute of Bioregulation Kyushu University Hospital Beppu Japan. Hiroshi Saito MD Cancer Screening Technology Division Research Center for Cancer Prevention and Screening National Cancer Center Tokyo Japan. Shinji Tanaka MD Department of Endoscopy Hiroshima University Hospital Hiroshima Japan. Hidenobu Watanabe MD Division of Molecular and Diagnostic Pathology Graduate School of Medical and Dental Sciences Niigata University Niigata Japan. Joseph J. Sung MD Institute of Digestive Diseases Chinese University of Hong Kong Prince of Wales Hospital Shatin Hong Kong. John I. Allen MD Medical Director Minnesota Gastroenterology University of Minnesota School of Medicine Minneapolis Minnesota USA. Andrew D. Feld MD Clinical Associate Professor of Medicine Central Division of Gastroenterology Group Health Cooperative University of Washington Seattle Washington USA. John M. Inadomi MD Director GI Health Outcomes Research Program University of California San Francisco Division of Gastroenterology San Francisco General Hospital San Francisco California USA. Michael J. O'Brien MD Department of Pathology Boston University Medical Center Boston Massachusetts USA. David A. Lieberman MD Division of Gastroenterology Portland VA Hospital Portland Oregon USA. David F. Ransohoff MD Bioinformatics Department of Medicine University of North Carolina Chapel Hill North Carolina USA. Roy M. Soetikno MD Gastroenterology Section Veterans Affairs Palo Alto Health

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Finding Phylogenetically Informative Genes by Estimating Multispecies Gene Entropy

Finding Phylogenetically Informative Genes by Estimating Mul...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaoxu Han Department of Mathematics and Bioinformatics Program Eastern Michigan University Ypsilanti MI USA

Although entropy and relative entropy (K-L distance) are widely applied in many bioinformatics areas, there is no method given to compute the multispecies gene entropy yet. This paper presents the first multispecies gene entropy estimation method from the data mining point of view. In this study, a self-organizing map (SOM) is employed to mine a multispecies gene set to obtain the probability distribution of each gene in the feature space, which is the approximation of its corresponding probability distribution in the original sequence space. The multispecies gene entropy is computed by the probability distribution of a gene in the feature space. The phylogenetic applications of the multispecies gene entropy are investigated in an example of resolving incongruence between gene trees and species trees. It is found that genes with nearest K-L distances to the minimum entropy gene are more likely to be phylogenetically informative. A K-L distance based gene concatenation approach under gene clustering is proposed to resolve the gene tree and species tree problem. Under the same testing dataset, the K-L distance based approach not only avoids the ad-hoc mechanism of the original gene concatenation method but also is easy to extend to other dataset and free from prohibitive phylogenetic computing from large number of taxa.

关键词： Entropy Sequences DNA Probability distribution bioinformatics Phylogeny Data mining Distributed computing Testing Information analysis

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Organization of brain complexity - Synapse proteome form and function

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Briefings in Functional Genomics and Proteomics 2006年第1期5卷 66-73页

作者： Pocklington, Andrew J. Armstrong, J. Douglas Grant, Seth G.N. Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Edinburgh United Kingdom Edinburgh Centre for Bioinformatics University of Edinburgh Edinburgh United Kingdom Cognition Program Wellcome Trust Sanger Institute Hinxton CB10 1SA United Kingdom

Proteomic study of the synapse has generated an extensive list of molecular components, revealing one of the most complex functional systems currently known to cell biology. While fundamental to neural information processing, behaviour and disease, the molecular organisation of the synapse and its relation to higher-level function has yet to be clearly understood. Neurotransmitter receptor complexes, such as the N-methyl-D-aspartate receptor complex (NRC/MASC), are major components of the synaptic proteome. We have recently completed a detailed study of MASC, its functional organisation and involvement in behaviour and disease. This pointed to simple design principles underlying synaptic organisation. Drawing together the results of proteomic and analytical study, we sketch out a model for synaptic functional organisation. © 2006 Oxford University Press.

关键词： LTP Networks NMDA receptor Protein complex Synaptic plasticity

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Breaking the high-throughput bottleneck

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Scientific Computing 2006年第4期23卷 22-26页

作者： Waters, Katrina M. Singhal, Mudita Webb-Robertson, Bobbie-Jo M. Stephan, Eric G. Gephart, Julie M. Computational Biology and Bioinformatics Groups United States Lab's Systems Biology Program Biological Sciences Division United States PNNL United States

The development of high-throughput (HTP) technologies such as transcriptomics (microarrays) and proteomics has made a revolution in the medical industry. PNNL is developing a systems biology computational framework using a problem-solving environment called bioinformatics Resource Manager (BRM) that automates routine data-merging and data-mining methods, and provides seamless linkage to data visualization tools. The BRM is designed to be operated by biologists looking for greater insights into their experimental results. BRM uses Remote Method Invocation (RMI) to launch several external data analysis tools, including NCBI's PubMed, the Conserved Domain Architectural Retrieval Tool (CDART) and the Basic Local Alignment Search Tool (BLAST). PQuad is a proteomics visualization tool that combines DNA or RNA sequence and proteomics data at multiple resolutions. It enables visualization of the data at the chromosome, gene and sequence levels.

关键词： Data visualization

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Comparing Kernels for Predicting Protein Binding Sites from Amino Acid Sequence

Comparing Kernels for Predicting Protein Binding Sites from ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Feihong Wu B. Olson D. Dobbs V. Honavar Bioinformatics and Computational Biology Graduate Program Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA USA

The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. Support vector machines (SVM) and related kernel methods offer an attractive approach to predicting protein binding sites. An appropriate choice of the kernel function is critical to the performance of SVM. Kernel functions offer a way to incorporate domain-specific knowledge into the classifier. We compare the performance of three types of kernels functions: identity kernel, sequence-alignment kernel, and amino acid substitution matrix kernel in the case of SVM classifiers for predicting protein-protein, protein-DNA and protein-RNA binding sites. The results show that the identity kernel is quite effective in on all three tasks. The substitution kernel based on amino acid substitution matrices that take into account structural or evolutionary conservation or physicochemical properties of amino acids yields modest improvement.

关键词： Kernel Proteins Amino acids Support vector machines Support vector machine classification Matrices Drugs Signal design Signal analysis Signal processing

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High torsional energy disulfides: Relationship between cross-strand disulfides and right-handed staples

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Journal of bioinformatics and Computational Biology 2006年第1期4卷 155-168页

作者： Haworth, Naomi L. Feng, Lina L. Wouters, Merridee A. Computational Biology and Bioinformatics Program Victor Chang Cardiac Research Institute Darlinghurst NSW 2010 384 Victoria Rd. Australia School of Chemistry University of Sydney Sydney NSW 2006 Australia School of Biotechnology and Biomolecular Sciences University of New South Wales Sydney Sydney NSW 2052 Australia

Redox-active disulfides are capable of being oxidized and reduced under physiological conditions. The enzymatic role of redox-active disulfides in thiol-disulfide reductases is well-known, but redox-active disulfides are also present in non-enzymatic protein structures where they may act as switches of protein function. Here, we examine disulfides linking adjacent β-strands (cross-strand disulfides), which have been reported to be redox-active. Our previous work has established that these cross-strand disulfides have high torsional energies, a quantity likely to be related to the ease with which the disulfide is reduced. We examine the relationship between conformations of disulfides and their location in protein secondary structures. By identifying the overlap between cross-strand disulfides and various conformations, we wish to address whether the high torsional energy of a cross-strand disulfide is sufficient to confer redox activity or whether other factors, such as the presence of the cross-strand disulfide in a strained β-sheet, are required. © Imperial College Press.

关键词： Cross-strand disulfide Disulfide Redox signaling Right-handed staple

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Biologically Supervised Hierarchical Clustering Algorithms for Gene Expression Data

Biologically Supervised Hierarchical Clustering Algorithms f...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Grzegorz M Boratyn Susmita Datta Somnath Datta Kidney Disease Program and Clinical Proteomics Center University of Louisville Louisville KY Faculty of Depertment of Bioinformatics and Biostatistics University of Louisville Louisville KY

Cluster analysis has become a standard part of gene expression analysis. In this paper, we propose a novel semi-supervised approach that offers the same flexibility as that of a hierarchical clustering. Yet it utilizes, along with the experimental gene expression data, common biological information about different genes that is being complied at various public, Web accessible databases. We argue that such an approach is inherently superior than the standard unsupervised approach of grouping genes based on expression data alone. It is shown that our biologically supervised methods produce better clustering results than the corresponding unsupervised methods as judged by the distance from the model temporal profiles. R-codes of the clustering algorithm are available from the authors upon request

关键词： Clustering algorithms Gene expression Databases Algorithm design and analysis Biological system modeling Biology computing Cities and towns Data analysis Measurement standards Diseases

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