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SPARSE: Quadratic time simultaneous alignment and folding of rnas without sequence-based heuristics

17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013

作者： Will, Sebastian Schmiedl, Christina Miladi, Milad Möhl, Mathias Backofen, Rolf Bioinformatics Department of Computer Science University of Freiburg Germany Bioinformatics Department of Computer Science University of Leipzig Germany University of Freiburg Germany University of Freiburg Germany Centre for Non-coding RNA in Technology and Health Bagsvaerd Denmark

ISBN: (纸本)9783642371943

Motivation: There is increasing evidence of pervasive transcription, resulting in hundreds of thousands of ncrnas of unknown function. Standard computational analysis tasks for inferring functional annotations like clustering require fast and accurate rna comparisons based on sequence and structure similarity. The gold standard for the latter is Sankoff's algorithm [3], which simultaneously aligns and folds rnas. Because of its extreme time complexity of O(n6), numerous faster "Sankoff-style" approaches have been suggested. Several such approaches introduce heuristics based on sequence alignment, which compromises the alignment quality for rnas with sequence identities below 60% [1]. Avoiding such heuristics, as e.g. in LocArna [4], has been assumed to prohibit time complexities better than O(n 4), which strongly limits large-scale applications. © 2013 Springer-Verlag.

关键词： Alignment

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Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

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BMC bioinformatics 2016年第Sup3期17 Suppl 3卷 95页

作者： Katie Wilkins Mehedi Hassan Margherita Francescatto Jakob Jespersen R. Gonzalo Parra Bart Cuypers Dan DeBlasio Alexander Junge Anupama Jigisha Farzana Rahman Griet Laenen Sander Willems Lieven Thorrez Yves Moreau Nagarajan Raju Sonia Pankaj Chothani C. Ramakrishnan Masakazu Sekijima M. Michael Gromiha Paddy J Slator Nigel J Burroughs Przemysław Szałaj Zhonghui Tang Paul Michalski Oskar Luo Xingwang Li Yijun Ruan Dariusz Plewczynski Giulia Fiscon Emanuel Weitschek Massimo Ciccozzi Paola Bertolazzi Giovanni Felici Bart Cuypers Pieter Meysman Manu Vanaerschot Maya Berg Hideo Imamura Jean-Claude Dujardin Kris Laukens Westa Domanova James R. Krycer Rima Chaudhuri Pengyi Yang Fatemeh Vafaee Daniel J. Fazakerley Sean J. Humphrey David E. James Zdenka Kuncic Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University Ithaca NY USA Graduate Field of Computational Biology Cornell University Ithaca NY USA School of Computing & Mathematics University of South Wales Cardiff UK Department of Genome Biology for Neurodegenerative Diseases German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association ᅟ Germany Department of Surgery Massachusetts General Hospital The Broad Institute of MIT and Harvard Boston USA Department of Systems Biology Technical University of Denmark Kemitorvet Denmark Protein Physiology Lab Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina Molecular Parasitology Unit (MPU) Institute of Tropical Medicine Nationalestraat 155 2000 Antwerp Belgium Advanced Database Research and Modeling (ADReM) research group University of Antwerp Middelheimlaan 1 2020 Antwerpen Belgium Department of Computer Science University of Arizona Tucson AZ 84721 USA Department of Veterinary Clinical and Animal Sciences Center for non-coding RNA in Technology and Health University of Copenhagen Copenhagen Denmark University College Dublin Dublin Ireland Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium iMinds Medical IT Department Leuven Belgium Scientific Institute of Public Health (WIV-ISP) Platform of Biotechnology and Molecular Biology (PBB) Brussels Belgium Department of Development and Regeneration @ Kulak KU Leuven Kortrijk Belgium Department of Biotechnology Bhupat and Jyoti Metha School of Biosciences Indian Institute of Technology Madras Chennai 600036 Tamilnadu India Philips Research North America 345 Scarborough Road Briarcliff Manor NY 10510 USA Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan Systems Biology C

A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-rna recognition: A computational analysis of protein-rna complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A Systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

关键词： Visceral Leishmaniasis Feature Selection Method Frequent Itemset Mining Leishmania Donovani Student Council

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Inferring chemical reaction patterns using rule composition in graph grammars

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Journal of Systems Chemistry 2013年第1期4卷 1页

作者： Andersen, Jakob L Flamm, Christoph Merkle, Daniel Stadler, Peter F Department of Mathematics and Computer Science University of Southern Denmark Campusvej 55 Odense M DK-5230 Denmark Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig D-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 Wien A-1090 Austria Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 Leipzig D-04107 Germany Fraunhofer Institute for Cell Therapy and Immunology Perlickstraße 1 Leipzig D-04103 Germany Center for non-coding RNA in Technology and Health University of Copenhagen Grønnegårdsvej 3 Frederiksberg C DK-1870 Denmark Santa Fe Institute 1399 Hyde Park Rd Santa Fe NM 87501 USA

Modeling molecules as undirected graphs and chemical reactions as graph rewriting operations is a natural and convenient approach to modeling chemistry. Graph grammar rules are most naturally employed to model elementary reactions like merging, splitting, and isomerisation of molecules. It is often convenient, in particular in the analysis of larger systems, to summarize several subsequent reactions into a single composite chemical reaction. We introduce a generic approach for composing graph grammar rules to define a chemically useful rule compositions. We iteratively apply these rule compositions to elementary transformations in order to automatically infer complex transformation patterns. As an application we automatically derive the overall reaction pattern of the Formose cycle, namely two carbonyl groups that can react with a bound glycolaldehyde to a second glycolaldehyde. Rule composition also can be used to study polymerization reactions as well as more complicated iterative reaction schemes. Terpenes and the polyketides, for instance, form two naturally occurring classes of compounds of utmost pharmaceutical interest that can be understood as "generalized polymers" consisting of five-carbon (isoprene) and two-carbon units, respectively. The framework of graph transformations provides a valuable set of tools to generate and investigate large networks of chemical networks. Within this formalism, rule composition is a canonical technique to obtain coarse-grained representations that reflect, in a natural way, "effective" reactions that are obtained by lumping together specific combinations of elementary reactions.

关键词： HYPERGRAPHS CHEMICAL reactions POLYKETIDES POLYMERIZATION research SPLITTING (Psychology) GLYCOLALDEHYDE STUDY & teaching

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Comparative ncrna gene and structure prediction using foldalign and foldalignM

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Current Protocols in Bioinformatics 2012年第SUPPL.39期39卷

作者： Havgaard, Jakob Kaur, Simranjeet Gorodkin, Jan Center for Non-Coding RNA in Technology and Health Department of Veterinary Clinical and Animal Sciences University of Copenhagen Copenhagen Denmark

This unit describes how to use Foldalign and FoldalignM to make structural alignments of non-protein-coding-rna (ncrna). These tools can be used to find new ncrnas, to find the structure of novel ncrnas, and to improv... 详细信息

关键词： ncrna ncrna gene finding non-coding rna rna multiple sequence alignment rna structural alignment rna structure prediction

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Integrated Analyses Identify a Master Microrna Regulatory Network for the Mesenchymal Subtype in Serous Ovarian Cancer (vol 23, pg 186, 2013)

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CANCER CELL 2013年第5期23卷 705-705页

作者： Yang, Da Sun, Yan Hu, Limei Zheng, Hong Ji, Ping Pecot, Chad V. Zhao, Yanrui Reynolds, Sheila Cheng, Hanyin Rupaimoole, Rajesha Cogdell, David Nykter, Matti Broaddus, Russell Rodriguez-Aguayo, Cristian Lopez-Berestein, Gabriel Liu, Jinsong Shmulevich, Ilya Sood, Anil K. Chen, Kexin Zhang, Wei Department of Pathology The University of Texas MD Anderson Cancer Center Houston TX 77030 USA Department of Gynecologic Oncology and Reproductive Medicine The University of Texas MD Anderson Cancer Center Houston TX 77030 USA Department of Cancer Biology The University of Texas MD Anderson Cancer Center Houston TX 77030 USA Department of Experimental Therapeutics The University of Texas MD Anderson Cancer Center Houston TX 77030 USA Center for RNAi and Non-Coding RNA The University of Texas MD Anderson Cancer Center Houston TX 77030 USA Division of Cancer Medicine The University of Texas MD Anderson Cancer Center Houston TX 77030 USA Department of Pathology Tianjin Medical University Cancer Institute and Hospital Tianjin 300060 China Department of Epidemiology and Biostatistics Tianjin Medical University Cancer Institute and Hospital Tianjin 300060 China Institute for Systems Biology Seattle WA 98103 USA Tampere University of Technology Tampere 33101 Finland

Integrated genomic analyses revealed a mirna-regulatory network that further defined a robust integrated mesenchymal subtype associated with poor overall survival in 459 cases of serous ovarian cancer (OvCa) from The Cancer Genome Atlas and 560 cases from independent cohorts. Eight key mirnas, including miR-506, miR-141, and miR-200a, were predicted to regulate 89% of the targets in this network. Follow-up functional experiments illustrate that miR-506 augmented E-cadherin expression, inhibited cell migration and invasion, and prevented TGFβ-induced epithelial-mesenchymal transition by targeting SNAI2, a transcriptional repressor of E-cadherin. In human OvCa, miR-506 expression was correlated with decreased SNAI2 and VIM, elevated E-cadherin, and beneficial prognosis. Nanoparticle delivery of miR-506 in orthotopic OvCa mouse models led to E-cadherin induction and reduced tumor growth.

关键词： Animals Apoptosis/drug effects Blotting, Western Blotting, Western Cadherins/genetics Cadherins/metabolism Cell Movement Cell Proliferation/drug effects Cystadenocarcinoma, Serous/genetics Cystadenocarcinoma, Serous/mortality Cystadenocarcinoma, Serous/pathology Epithelial-Mesenchymal Transition/genetics Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Immunoenzyme Techniques Immunoenzyme Techniques Mesoderm/metabolism Mesoderm/pathology Mice Mice, Nude Micrornas/genetics Micrornas/genetics Middle Aged Neoplasm Grading Ovarian Neoplasms/genetics Ovarian Neoplasms/mortality Ovarian Neoplasms/pathology Prognosis Real-Time Polymerase Chain Reaction Reverse Transcriptase Polymerase Chain Reaction rna, Messenger/genetics Survival Rate Transcription Factors/genetics Transcription Factors/metabolism Transforming Growth Factor beta/genetics Transforming Growth Factor beta/metabolism Tumor Cells, Cultured Tumor Markers, Biological/genetics Vimentin/genetics Vimentin/metabolism

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Maximizing output and recognizing autocatalysis in chemical reaction networks is NP-complete

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Journal of Systems Chemistry 2012年第1期3卷 1-9页

作者： Andersen, Jakob L. Flamm, Christoph Merkle, Daniel Stadler, Peter F. Department for Mathematics and Computer Science University of Southern Denmark DK-5230 Odense M Campusvej 55 Denmark Institute for Theoretical Chemistry University of Vienna A-1090 Wien Währingerstraße 17 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics D-04107 Leipzig Härtelstraße 16-18 Germany Max Planck Institute for Mathematics in the Sciences D-04103 Leipzig Inselstraße 22 Germany Fraunhofer Institute for Cell Therapy and Immunology D-04103 Leipzig Perlickstraße 1 Germany Center for non-coding RNA in Technology and Health University of Copenhagen DK-1870 Frederiksberg C Grønnegårdsvej 3 Denmark Santa Fe Institute Santa Fe NM 87501 1399 Hyde Park Rd. United States

Background: A classical problem in metabolic design is to maximize the production of a desired compound in a given chemical reaction network by appropriately directing the mass flow through the network. Computationally, this problem is addressed as a linear optimization problem over the flux cone. The prior construction of the flux cone is computationally expensive and no polynomial-time algorithms are known. Results: Here we show that the output maximization problem in chemical reaction networks is NP-complete. This statement remains true even if all reactions are monomolecular or bi-molecular and if only a single molecular species is used as influx. As a corollary we show, furthermore, that the detection of autocatalytic species, i.e., types that can only be produced from the influx material when they are present in the initial reaction mixture, is an NPcomplete computational problem. Conclusions: Hardness results on combinatorial problems and optimization problems are important to guide the development of computational tools for the analysis of metabolic networks in particular and chemical reaction networks in general. Our results indicate that efficient heuristics and approximate algorithms need to be employed for the analysis of large chemical networks since even conceptually simple flow problems are provably intractable. © 2012 Andersen et al.;licensee Chemistry Central Ltd.

关键词： CHEMICAL processes AUTOCATALYSIS MATHEMATICAL optimization CHEMICAL reactions COMPUTATIONAL chemistry

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Erratum to: Targeting the PELP1-KDM1 axis as a potential therapeutic strategy for breast cancer

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Breast Cancer Research 2012年第6期14卷 1-2页

作者： Valerie Cortez Monica Mann Seshidhar Tekmal Ratna K Vadlamudi Takayoshi Suzuki Naoki Miyata Cristian Rodriguez-Aguayo Gabriel Lopez-Berestein Anil K Sood Department of Obstetrics and Gynecology University of Texas Health Science Center San Antonio TX 78229 USA Department of Cell and Structural Biology University of Texas Health Science Center San Antonio TX 78229 USA Cancer Therapy and Research Center University of Texas Health Science Center San Antonio TX 78229 USA Graduate School of Medical Science Kyoto Prefectural University of Medicine 13 Taishogun Nishitakatsukasa-Cho Kita-ku Kyoto 403-8334 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Graduate School of Pharmaceutical Sciences Nagoya City University 3-1 Tanabe-dori Mizuho-ku Nagoya Aichi 467-8673 Japan Department of Gynecologic Oncology University of Texas MD Anderson Cancer Center Houston TX 78030 USA Center for RNA Interference and Non-coding RNA University of Texas MD Anderson Cancer Center Houston TX 78030 USA Department of Cancer Biology University of Texas MD Anderson Cancer Center Houston TX 78030 USA

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Computational strategies to search structured rnas in genomic sequence(and roads towards further analysis)

Computational strategies to search structured RNAs in genomi...

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2011国际基因组学大会

作者： Jan Gorodkin Center for non-coding RNA in Technology and Health IBHVUniversity of CopenhagenDenmark

A key application of early methods for computational rna folding was to aid interpretation of structure probing *** the years computational rna biology has developed in which improved energy models and the availability of multiple genomes have contributed substantially to the development of the *** methods that search for common rna structures in a set of co-regulated rnas have

关键词： rna and roads towards further analysis Computational strategies to search structured rnas in genomic sequence

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Erratum to “Circulating Levels of Microrna from Children with Newly Diagnosed Type 1 Diabetes and healthy Controls: Evidence That miR-25 Associates to Residual Beta-Cell Function and Glycaemic Control during Disease Progression”

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Journal of Diabetes Research 2012年第1期2012卷 672865-672865页

作者： Lotte B. Nielsen Cheng Wang Kaspar Sørensen Claus H. Bang-Berthelsen Lars Hansen Marie-Louise M. Andersen Philip Hougaard Anders Juul Chen-Yu Zhang Flemming Pociot Henrik B. Mortensen Department of Pediatrics Herlev Hospital 2730 Herlev Denmarkherlevhospital.dk Faculty of Health Sciences University of Copenhagen 2200 Copenhagen Denmarkku.dk Jiangsu Engineering Research Center for microRNA Biology and Biotechnology State Key Laboratory of Pharmaceutical Biotechnology School of Life Sciences Nanjing University Nanjing 210093 *** Department of Growth and Reproduction Rigshospitalet and Faculty of Health Sciences University of Copenhagen 2100 Copenhagen Denmarkku.dk Glostrup Research Institute Glostrup Hospital and Center for Non-Coding RNA in Technology and Health University of Copenhagen 2600 Glostrup Denmarkku.dk Department of Statistics University of Southern Denmark 5000 Odense Denmarksdu.dk

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Microrna or not microrna?

MicroRNA or not microRNA?

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Lecture Notes in Computer Science

作者： Langenberger, David Bartschat, Sebastian Hertel, Jana Hoffmann, Steve Tafer, Hakim Stadler, Peter F. Bioinformatics Group Department of Computer Science University of Leipzig D-04107 Leipzig United Kingdom Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 D-04107 Leipzig Germany LIFE - Leipzig Research Center for Civilization Diseases University of Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 D-04103 Leipzig Germany Fraunhofer Institut für Zelltherapie und Immunologie Perlickstraße 1 D-04103 Leipzig Germany Department of Theoretical Chemistry University of Vienna Währingerstraße 17 A-1090 Wien Austria Center for Non-coding RNA in Technology and Health University of Copenhagen Grønnegårdsvej 3 DK-1870 Frederiksberg C Denmark Santa Fe Institute 1399 Hyde Park Rd. Santa Fe NM 87501 United States

ISBN: (纸本)9783642228247

The avalanche of next generation sequencing data has led to a rapid increase of annotated micrornas in the last few years. Many of them are specific to individual species or rather narrow clades. A closer inspection of the current version of miRBase shows that dozens of entries conflict with other ncrnas, in particular snornas. With few exceptions, these cases show little similarities to canonical micrornas, however, and thus they should be considered as mis-annotations. © 2011 Springer-Verlag.

关键词： rna

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