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Development of a database and ontology for pathogenic pathways in periodontitis

In Silico Biology 2009年第4期9卷 233-243页

作者： Suzuki, Asami Takai-Igarashi, Takako Numabe, Yukihiro Tanaka, Hiroshi Department of General Dentistry The Nippon Dental University Hospital at Tokyo Tokyo Japan Biomedical Clinical Omics Educational Program Tokyo Medical and Dental University Tokyo Japan Department of Bioinformatics School of Biomedical Science Tokyo Medical and Dental University Bunkyo-Ku Tokyo 113-8510 1-5-45 Yushima Japan Department of Periodontology The Nippon Dental University School of Life Dentistry at Tokyo Tokyo Japan

It is getting familiar that pathway information greatly contributes to elucidate the molecular basis of human disease with large-scale biological data. We developed a pathway database for molecular pathology in periodontitis named 'Pathogenic Pathway Database for Periodontitis'. Periodontitis is an inflammation disease in periodontal tissue and associated with an increased health risk of angina, myocardial infarction, and fetal cardiovascular events. Despite accumulation of biomedical research on periodontitis pathology at the molecular level, there has been no systematization for biological pathways in periodontitis. We checked 185 reference papers and extracted causal relationships among molecules as well as pathways representing molecular etiology. We also built an ontology that systematizes conceptual terms associated with the pathways. Besides pathways, our ontology provides cellular and tissue specific contextual information that is required to represent pathology at the cellular and tissue specific levels. We implemented in our database an integrated viewer for the association between pathways and ontology. Pathogenic Pathway Database for Periodontitis is freely available at http://***/disease/ perio/. © 2009 IOS Press. All rights reserved.

关键词： Alveolar bone resorption Ontology Pathogenic pathway Pathway database Periodontitis

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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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Identification of microRNA in the protist Trichomonas vaginalis

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Genomics 2009年第5期93卷 487-493页

作者： Lin, Wei-Chen Li, Sung-Chou Lin, Wen-Chang Shin, Jyh-Wei Hu, Song-Nian Yu, Xiao-Min Huang, Ting-Yun Chen, Shih-Chieh Chen, Hua-Chien Chen, Shu-Jen Huang, Po-Jung Ruei-Chi Gan, Richie Chiu, Cheng-Hsun Tang, Petrus Molecular Regulation and Bioinformatics Laboratory Chang Gung University Taoyuan 333 Taiwan Division of Microbiology Graduate Institute of Biomedical Sciences Chang Gung University Taoyuan 333 Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei 112 Taiwan Taiwan International Graduate Program in Bioinformatics Academia Sinica Taipei 115 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei 115 Taiwan Department of Parasitology National Cheng Kung University Tainan 701 Taiwan Key Laboratory of Genome Sciences and Information Beijing Institute of Genomics Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Department of Life Sciences Chang Gung University Taoyuan 333 Taiwan Molecular Medicine Research Center Chang Gung University Taoyuan 333 Taiwan Bioinformatics Center Chang Gung University Tao-Yuan 333 Taiwan Chang Gung Molecular Infectious Diseases Research Center Chang Gung Memorial Hospital Taoyuan 333 Taiwan

MicroRNAs (miRNAs) are a class of small noncoding RNAs that have important regulatory roles in multicellular organisms. However, miRNA has never been identified experimentally in protist. Direct cloning of 438 expressed miRNA tags by microRNA serial analysis of gene expression from the parasitic protist Trichomonas vaginalis identified nine candidate miRNAs. bioinformatics analysis of the corresponding genomic region revealed that these miRNA candidates contain a classical stem-loop-stem structure of pre-microRNAs. Analysis of the 20 nt long mature tva-miR-001 showed that it is an intergenic miRNA located at the scaffold DS113596. Tva-miR-001 was differentially expressed in the trophozoite, pseudocyst and amoeboid stages. Based on the experimental results of the present study, we provided solid evidence that protist possesses a miRNA regulating network comparable with multicellular organisms for the first time. © 2009 Elsevier Inc. All rights reserved.

关键词： MicroRNA Protist Trichomonas vaginalis

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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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Complementary quantitative proteomics reveals that transcription factor AP-4 mediates E-box-dependent complex formation for transcriptional repression of HDM2

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Molecular and Cellular Proteomics 2009年第9期8卷 2034-2050页

作者： Ku, Wei-Chi Chiu, Sung-Kay Chen, Yi-Ju Huang, Hsin-Hung Wu, Wen-Guey Chen, Yu-Ju Chemical Biology and Molecular Biophysics Program Institute of Chemistry Academia Sinica Taipei 115 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu 300 Taiwan Department of Biology and Chemistry City University of Hong Kong Hong Kong Hong Kong Institute of Biochemical Sciences National Taiwan University Taipei 106 Taiwan Genomics Research Center Academia Sinica Taipei 115 Taiwan Department of Chemistry National Taiwan University Taipei 106 Taiwan

Transcription factor activating enhancer-binding protein 4 (AP-4) is a basic helix-loop-helix protein that binds to E-box elements. AP-4 has received increasing attention for its regulatory role in cell growth and development, including transcriptional repression of the human homolog of murine double minute 2 (HDM2), an important oncoprotein controlling cell growth and survival, by an unknown mechanism. Here we demonstrate that AP-4 binds to an E-box located in the HDM2-P2 promoter and represses HDM2 transcription in a p53-independent manner. Incremental truncations of AP-4 revealed that the C-terminal Gln/Pro-rich domain was essential for transcriptional repression of HDM2. To further delineate the molecular mechanism(s) of AP-4 transcriptional control and its potential implications, we used DNA-affinity purification followed by complementary quantitative proteomics, cICAT and iTRAQ labeling methods, to identify a previously unknown E-box-bound AP-4 protein complex containing 75 putative components. The two labeling methods complementarity quantified differentially AP-4-enriched proteins, including the most significant recruitment of DNA damage response proteins, followed by transcription factors, transcriptional repressors/corepressors, and histone-modifying proteins. Specific interaction of AP-4 with CCCTC binding factor, stimulatory protein 1, and histone deacetylase 1 (an AP-4 corepressor) was validated using AP-4 truncation mutants. Importantly, inclusion of trichostatin A did not alleviate AP-4-mediated repression of HDM2 transcription, suggesting a previously unidentified histone deacetylase-independent repression mechanism. In contrast, the complementary quantitative proteomics study suggested that transcription repression occurs via coordination of AP-4 with other transcription factors, histone methyltransferases, and/or a nucleosome remodeling SWI-SNF complex. In addition to previously known functions of AP-4, our data suggest that AP-4 participates in a

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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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Article Commentary: Snapshots of Tree Space

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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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Structural insights into TDP-43, a new player in neurodegenerative diseases

Structural insights into TDP-43, a new player in neurodegene...

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亚洲晶体学联合大会

作者： Pan-Hsien Kuo Lyudmila G.Doudeva Che-Kun James Shen Hanna S.Yuan Institute of Bioinformatics and Structural Biology National Tsing Hua University Institute of Molecular Biology Academia Sinica Taiwan International Graduate Program Chemical Biology and Molecular BiophysicsAcademia Sinica Graduate Institute of Biochemistry and Molecular Biology National Taiwan University

TDP-43 is a pathogenic protein: its normal function in binding to UG-rich RNA is related to cystic fibrosis, and inclusion of its C-terminal fragments in brain cells is directly linked to frontotemporal lobar degenera... 详细信息

关键词： TDP RNA domain Structural insights into TDP-43 a new player in neurodegenerative diseases

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Evidence of term-structure differences among folksonomies and controlled indexing languages

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Proceedings of the Association for Information Science and Technology 2009年第1期45卷 1-7页

作者： Benjamin M. Good Joseph T. Tennis The James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research Bioinformatics Graduate Program University of British Columbia Providence Heart + Lung Institute St. Paul's Hospital Vancouver BC Canada The Information School University of Washington Box 352840 Mary Gates Hall Suite 370 Seattle WA 98195-2840

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