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Article Commentary: Snapshots of Tree Space

Evolutionary bioinformatics 2009年 5卷

作者： Wang, Zheng López-Giréidez, Francesc Townsend, Jeffrey P. Department of Ecology and Evolutionary Biology Yale University New Haven 06520 CT United States Program in Computational Biology and Bioinformatics Yale University New Haven 06520 CT United States

Achievement of the best balance between the accuracy and efficiency is always an important issue when searching a tree space of large data sets. In the 5th issue in 2009, Rodrigo et al used bootstrapped topologies as fixed genealogies to distribute an MCMC analysis across a cluster of computers, resulting in an efficiency yielding results 37 times faster than in the standard MCMC methods. Tree searches can seldom be guided with certainty, so that such snapshots sampling partial but “more-likely” tree space facilitated by parallel programs on computer clusters may provide great promise among a few choices that are computationally affordable when tree space is large and complicated. © 2009 SAGE Publications.

关键词： computer cluster large phylogeny tree sampling

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Francisella tularensis subsp. novicida isolated from a human in Arizona

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BMC Research Notes 2009年第1期2卷 1-6页

作者： Birdsell, Dawn N Stewart, Tasha Vogler, Amy J Lawaczeck, Elisabeth Diggs, Alisa Sylvester, Tammy L Buchhagen, Jordan L Auerbach, Raymond K Keim, Paul Wagner, David M Center for Microbial Genetics and Genomics Northern Arizona University Flagstaff AZ 86011-4073 United States Maricopa County Department of Public Health Phoenix AZ 85012 United States Arizona Department of Health Services Phoenix AZ 85007 United States Translational Genomics Research Institute Phoenix AZ 85004 United States Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States

Background. Francisella tularensis is the etiologic agent of tularemia and is classified as a select agent by the Centers for Disease Control and Prevention. Currently four known subspecies of F. tularensis that differ in virulence and geographical distribution are recognized:tularensis (type A), holarctica (type B), mediasiatica, and novicida. Because of the Select Agent status and differences in virulence and geographical location, the molecular analysis of any clinical case of tularemia is of particular interest. We analyzed an unusual Francisella clinical isolate from a human infection in Arizona using multiple DNA-based approaches. Findings. We report that the isolate is F. tularensis subsp. novicida, a subspecies that is rarely isolated. Conclusion. The rarity of this novicida subspecies in clinical settings makes each case study important for our understanding of its role in disease and its genetic relationship with other F. tularensis subspecies. © 2009 Wagner et al;licensee BioMed Central Ltd.

关键词： Clinical Isolate Coccidioidomycosis Tularemia Francisella Tularensis Tularensis Subsp

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Multiple Routes to Characterize the Folding of a Small DNA Hairpin†

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Angewandte Chemie 2010年第42期122卷

作者： Guillem Portella Modesto Orozco Joint IRB‐BSC Research Program in Computational Biology Institute for Research in Biomedicine (IRB) Josep Samitier 1‐5 Barcelona 08028 (Spain) Barcelona Supercomputing Centre (BSC) Jordi Girona 29 Barcelona 08034 (Spain) Departament de Bioquímica i Biología Molecular Facultat de Biología Universitat de Barcelona Avgda Diagonal 645 Barcelona 08028 (Spain) National Institute of Bioinformatics Parc Científic de Barcelona Josep Samitier 1–5 Barcelona 08028 (Spain)

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Autofix for backward-fit sidechains: Using MolProbity and real-space refinement to put misfits in their place

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Journal of Structural and Functional Genomics 2009年第1期10卷 83-93页

作者： Headd, Jeffrey J. Immormino, Robert M. Keedy, Daniel A. Emsley, Paul Richardson, David C. Richardson, Jane S. Department of Biochemistry Duke University Medical Center 211 Nanaline Duke Building Durham NC 27710 United States Computational Biology and Bioinformatics Program Duke University Durham NC 27708 United States Department of Immunology University of Washington School of Medicine Seattle WA 98195 United States Laboratory of Molecular Biophysics Department of Biochemistry University of Oxford Oxford Oxfordshire 0X1 3QU United Kingdom

Misfit sidechains in protein crystal structures are a stumbling block in using those structures to direct further scientific inference. Problems due to surface disorder and poor electron density are very difficult to address, but a large class of systematic errors are quite common even in well-ordered regions, resulting in sidechains fit backwards into local density in predictable ways. The MolProbity web site is effective at diagnosing such errors, and can perform reliable automated correction of a few special cases such as 180° flips of Asn or Gln sidechain amides, using all-atom contacts and H-bond networks. However, most at-risk residues involve tetrahedral geometry, and their valid correction requires rigorous evaluation of sidechain movement and sometimes backbone shift. The current work extends the benefits of robust automated correction to more sidechain types. The Autofix method identifies candidate systematic, flipped-over errors in Leu, Thr, Val, and Arg using MolProbity quality statistics, proposes a corrected position using real-space refinement with rotamer selection in Coot, and accepts or rejects the correction based on improvement in MolProbity criteria and on χ angle change. Criteria are chosen conservatively, after examining many individual results, to ensure valid correction. To test this method, Autofix was run and analyzed for 945 representative PDB files and on the 50S ribosomal subunit of file 1YHQ. Over 40% of Leu, Val, and Thr outliers and 15% of Arg outliers were successfully corrected, resulting in a total of 3,679 corrected sidechains, or 4 per structure on average. Summary Sentences: A common class of misfit sidechains in protein crystal structures is due to systematic errors that place the sidechain backwards into the local electron density. A fully automated method called "Autofix" identifies such errors for Leu, Val, Thr, and Arg and corrects over one third of them, using MolProbity validation criteria and Coot real-space refinement

关键词： Automation Crystallography Protein/RNA interactions Sidechain rotamers Structure improvement

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Classification of Multiple Time-Series via Boosting

Classification of Multiple Time-Series via Boosting

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IEEE Digital Signal Processing Workshop

作者： Patrick L. Harrington Arvind Rao Alfred O., Hero Bioinformatics Graduate Program University of Michigan Ann Arbor MI USA Lane Center of Computational Biology Carnegie Mellon University Pittsburgh PA USA Department of Statistics University of Michigan Ann Arbor MI USA

Much of modern machine learning and statistics research consists of extracting information from high-dimensional patterns. Often times, the large number of features that comprise this high-dimensional pattern are themselves vector valued, corresponding to sampled values in a time-series. Here, we present a classification methodology to accommodate multiple time-series using boosting. This method constructs an additive model by adaptively selecting basis functions consisting of a discriminating feature's full time-series. We present the necessary modifications to fisher linear discriminant analysis and least-squares, as base learners, to accommodate the weighted data in the proposed boosting procedure. We conclude by presenting the performance of our proposed method against a synthetic stochastic differential equation data set and a real world data set involving prediction of cancer patient susceptibility for a particular chemoradiotherapy.

关键词： Boosting Statistics Vectors Linear discriminant analysis Gene expression bioinformatics computational biology Machine learning Data mining Stochastic processes

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Erratum to: An approach to compare genome tiling microarray and MPSS sequencing data for transcript mapping

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BMC Research Notes 2009年第1期2卷 1-2页

作者： Rajkumar Sasidharan Ashish Agarwal Joel Rozowsky Mark Gerstein Molecular Biophysics and Biochemistry Department Yale University New Haven CT 06520 USA Department of Plant Biology Carnegie Institution for Science Stanford California 94305 USA Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA Department of Computer Science Yale University New Haven CT 06520 USA

We are correcting the abstract of our published article ([1]). The sentence that starts "We observe that 4.5% of MPSS tags...." was not scientifically complete in the original abstract, having only two of the four numbers required to describe a comparison of two technologies in two different organisms. The abstract below more accurately describes our findings, as documented in Figure 1 of the manuscript.

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Meeting Report from the Second "Minimum Information for Biological and Biomedical Investigations" (MIBBI) workshop

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Standards in genomic sciences 2010年第3期3卷 259-66页

作者： Carsten Kettner Dawn Field Susanna-Assunta Sansone Chris Taylor Jan Aerts Nigel Binns Andrew Blake Cedrik M Britten Ario de Marco Jennifer Fostel Pascale Gaudet Alejandra González-Beltrán Nigel Hardy Jan Hellemans Henning Hermjakob Nick Juty Jim Leebens-Mack Eamonn Maguire Steffen Neumann Sandra Orchard Helen Parkinson William Piel Shoba Ranganathan Philippe Rocca-Serra Annapaola Santarsiero David Shotton Peter Sterk Andreas Untergasser Patricia L Whetzel Beilstein-Institut Frankfurt am Main Germany Centre for Ecology & Hydrology UK Oxford e-Research Centre University of Oxford UK The European Bioinformatics Institute (EBI) UK Faculty of Engineering - ESAT/SCD Leuven University Leuven-Heverlee Belgium Division of Pathway Medicine University of Edinburgh Medical School Edinburgh UK MRC Harwell UK Medical Department University Medical Center Johannes Gutenberg University-Mainz Mainz Germany Consortium for Genomic Technology Milano Italy University of Nova Gorica Nova Gorica Slovenia NIEHS NIH US National Toxicology Program USA Swiss Institute of Bioinformatics Geneva Switzerland Computational and Systems Medicine and Department of Computer Science University College London London UK Department of Computer Science Aberystwyth University Aberystwyth UK Center for Medical Genetics Ghent University Ghent Belgium Department of Plant Biology University of Georgia Athens USA Department of Stress- and Developmental Biology Institute for Plant Biochemistry Halle Germany Peabody Museum of Natural History Yale University New Haven USA Macquarie University Sydney Australia National University of Singapore Singapore Cancer Pharmacology The Mario Negri Institute for Pharmacological Research Milan Italy Image Bioinformatics Research Group Department of Zoology University of Oxford Oxford UK Wellcome Trust Sanger Institute UK Zentrum für Molekulare Biologie der Universität Heidelberg Heidelberg Germany The National Center for Biomedical Ontology / Stanford Center for Biomedical Informatics Research Stanford University Stanford USA

This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and Biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-Institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://***/.

关键词： Minimum Information European bioinformatics Institute Biomedical Ontology Stable Link Breakout Group

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Comprehensive analysis of the pseudogenes of glycolytic enzymes in vertebrates: The anomalously high number of GAPDH pseudogenes highlights a recent burst of retrotrans-positional activity

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BMC Genomics 2009年第1期10卷 480-480页

作者： Liu, Yuen-Jong Zheng, Deyou Balasubramanian, Suganthi Carriero, Nicholas Khurana, Ekta Robilotto, Rebecca Gerstein, Mark B. Department of Surgery Beth Israel Deaconess Medical Center Harvard Medical School Boston MA 110 Francis Street United States Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 P.O. Box 208114 United States Albert Einstein College of Medicine of Yeshiva University Department of Neurology Rose F. Kennedy Center Bronx NY 10461 1410 Pelham Parkway South United States Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 Bass 432 266 Whitney Avenue United States

Background: Pseudogenes provide a record of the molecular evolution of genes. As glycolysis is such a highly conserved and fundamental metabolic pathway, the pseudogenes of glycolytic enzymes comprise a standardized genomic measuring stick and an ideal platform for studying molecular evolution. One of the glycolytic enzymes, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), has already been noted to have one of the largest numbers of associated pseudogenes, among all proteins. Results: We assembled the first comprehensive catalog of the processed and duplicated pseudogenes of glycolytic enzymes in many vertebrate model-organism genomes, including human, chimpanzee, mouse, rat, chicken, zebrafish, pufferfish, fruitfly, and worm (available at http://***/glycolysis/). We found that glycolytic pseudogenes are predominantly processed, i.e. retrotransposed from the mRNA of their parent genes. Although each glycolytic enzyme plays a unique role, GAPDH has by far the most pseudogenes, perhaps reflecting its large number of non-glycolytic functions or its possession of a particularly retrotranspositionally active sub-sequence. Furthermore, the number of GAPDH pseudogenes varies significantly among the genomes we studied: none in zebrafish, pufferfish, fruitfly, and worm, 1 in chicken, 50 in chimpanzee, 62 in human, 331 in mouse, and 364 in rat. Next, we developed a simple method of identifying conserved syntenic blocks (consistently applicable to the wide range of organisms in the study) by using orthologous genes as anchors delimiting a conserved block between a pair of genomes. This approach showed that few glycolytic pseudogenes are shared between primate and rodent lineages. Finally, by estimating pseudogene ages using Kimura's two-parameter model of nucleotide substitution, we found evidence for bursts of retrotranspositional activity approximately 42, 36, and 26 million years ago in the human, mouse, and rat lineages, respectively. Conclusion: Overall, we pe

关键词： Glycolytic Enzyme Segmental Duplication Chimpanzee Genome Assembly Version ENSEMBL Release

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Grappling with the Gulf

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Science 2009年第5911期323卷 210页

作者： Dov Greenbaum Mark Gerstein Mcdermott Will and Emory LLP 3150 Porter Drive Palo Alto CA 94304 USA. Department of Molecular Biophysics and Biochemistry Program in Computational Biology and Bioinformatics and Department of Computer Science Yale University New Haven CT 06520 USA.

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It takes glue to tango: MeDICi integration framework's data-intensive computing pipeline

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Scientific Computing 2008年第7期25卷 16-19页

作者： Gorton, Ian Oehmen, Christopher S. McDermott, Hason E. PNNL's Data Intensive Computing Program United States Laboratory's Bioinformatics and Computational Biology Group Fundamental and Computational Sciences Directorate United States

Researchers at the Department of Energy's (DOE) Pacific Northwest National Laboratory (PNNL) in Richland, WA, are creating computing environments for biologists that seamlessly integrate collections of data and computational resources. MeDICi is an evolving middleware platform for building complex, high-performance analytical applications. MIF components are constructed using Java programming interfaces that support inter-component communication using asynchronous messaging. Local components execute inside the MIF container. Remote components create distributed solutions and integrate with non-Java code. Mule provides the MIF container environment. MIF extends the Mule interface to make component and pipeline construction easier and to create an encapsulation device for component creation. The MIF interface is agnostic of the underlying Java messaging platform. This allows deployments to configure MIF applications using technologies that meet individual quality-of-service requirements.

关键词： Containers

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