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arXiv 2017年

作者： Andersen, Jakob L. Flamm, Christoph Merkle, Daniel Stadler, Peter F. Earth-Life Science Institute Tokyo Institute of Technology Tokyo152-8550 Japan Department of Mathematics and Computer Science University of Southern Denmark Denmark Research Network Chemistry Meets Microbiology University of Vienna WienA-1090 Austria Institute for Theoretical Chemistry University of Vienna Austria Department of Computer Science University of Leipzig Germany Max Planck Institute for Mathematics in the Sciences Leipzig Germany Fraunhofer Institute for Cell Therapy and Immunology Leipzig Germany Center for non-coding RNA in Technology Health University of Copenhagen Denmark Santa Fe Institute Santa Fe United States

Computational techniques are required for narrowing down the vast space of possibilities to plausible prebiotic scenarios, since precise information on the molecular composition, the dominant reaction chemistry, and the conditions for that era are scarce. The exploration of large chemical reaction networks is a central aspect in this endeavour. While quantum chemical methods can accurately predict the structures and reactivities of small molecules, they are not efficient enough to cope with large-scale reaction systems. The formalization of chemical reactions as graph grammars provides a generative system, well grounded in category theory, at the right level of abstraction for the analysis of large and complex reaction networks. An extension of the basic formalism into the realm of integer hyperflows allows for the identification of complex reaction patterns, such as auto-catalysis, in large reaction networks using optimization techniques. Copyright © 2017, The Authors. All rights reserved.

关键词： Formal languages

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Publisher Correction: Regulation of cellular sterol homeostasis by the oxygen responsive noncoding rna lincNORS

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Nature communications 2020年第1期11卷 5737页

作者： Xue Wu Cristina M Niculite Mihai Bogdan Preda Annalisa Rossi Toma Tebaldi Elena Butoi Mattie K White Oana M Tudoran Daniela N Petrusca Amber S Jannasch William P Bone Xingyue Zong Fang Fang Alexandrina Burlacu Michelle T Paulsen Brad A Hancock George E Sandusky Sumegha Mitra Melissa L Fishel Aaron Buechlein Cristina Ivan Spyros Oikonomopoulos Myriam Gorospe Amber Mosley Milan Radovich Utpal P Davé Jiannis Ragoussis Kenneth P Nephew Bernard Mari Alan McIntyre Heiko Konig Mats Ljungman Diana L Cousminer Paolo Macchi Mircea Ivan Department of Microbiology and Immunology Indiana University School of Medicine Indianapolis IN 46202 USA. Department of Medicine Indiana University School of Medicine Indianapolis IN 46202 USA. "Victor Babes" National Institute of Pathology Bucharest Romania. Institute of Cellular Biology and Pathology "Nicolae Simionescu" Bucharest Romania. Laboratory of Molecular and Cellular Neurobiology Department of Cellular Computational and Integrative Biology-CIBIO University of Trento Trento Italy. Laboratory of Translational Genomics Department of Cellular Computational and Integrative Biology-CIBIO University of Trento Trento Italy. Yale Cancer Center Yale University School of Medicine New Haven CT 06520 USA. The Oncology Institute "Prof Dr. Ion Chiricuta" Cluj-Napoca Romania. Metabolite Profiling Facility Bindley Bioscience Center Purdue University West Lafayette IN 47907 USA. Department of Genetics Department of Systems Pharmacology and Translational Therapeutics Institute of Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA 19104 USA. Department of Cellular and Integrative Physiology Indiana University School of Medicine Indianapolis IN USA. Departments of Radiation Oncology and Environmental Health Sciences Center for RNA Biomedicine University of Michigan Ann Arbor MI 48109 USA. Department of Surgery Indiana University School of Medicine Indianapolis IN 46202 USA. Department of Pathology and Laboratory Medicine Indiana University School of Medicine Indianapolis IN 46202 USA. Melvin and Bren Simon Cancer Center Indiana University Indianapolis IN USA. Department of Obstetrics and Gynecology Indiana University School of Medicine Indianapolis IN 46202 USA. Department of Pharmacology and Toxicology Department of Pediatrics Wells Center for Pediatric Research Indiana University School of Medicine Indianapolis IN 46202 USA. Indiana University Center for Genomics and Bioinformatics

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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OUGENE： a disease associated over-expressed and under-expressed gene database

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Science Bulletin 2016年第10期61卷 752-754页

作者： Xiaoyong Pan Hong-Bin Shen Section for Animal Genetics Bioinformatics and Breeding Center for Non-Coding RNA in Technology and Health University of Copenhagen 1870 Frederiksberg Denmark Department of Disease Systems Biology Novo NordiskFoundation Center for Protein Research University ofCopenhagen 1870 Frederiksberg Denmark Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Gene over-expression or under-expression is closely associated with human diseases, which contributes to phenotypic variations and diversity. To our best knowledge, there is no single open specific resource available to provide the association information between gene over- or under-expression and various diseases. In this study, we presented a comprehensive disease-associated over- and under-expressed gene database （OUGene） based on our proposed text mining pipeline and several open curated databases. It contains total 41,269 unique associa- tions between 7,238 over- or under-expressed genes and 1,480 diseases, which are supported by 81,974 evidence sentences from 56,442 articles. The OUGene is compre- hensive and covers most important therapeutic areas. Meanwhile a new scoring system is designed to rank the associations based on benchmarking against hand-curated data. OUGene provides an easy-of-use web interface for researchers to analyze these data and visualize the associ- ated networks, which can give insights to the complex relationships between over- and under-expressed genes and diseases at a system level. It is available at ***. ***/bioinf/OUGene/.

关键词： Text mining Gene over- and under-expression Disease Database

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Chemical transformation motifs - Modelling pathways as integer hyperflows

arXiv

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arXiv 2017年

作者： Andersen, Jakob L. Flamm, Christoph Merkle, Daniel Stadler, Peter F. Earth-Life Science Institute Tokyo Institute of Technology Tokyo 152-8550 Japan Department of Mathematics and Computer Science University of Southern Denmark Odense M DK-5230 Denmark Research group Bioinformatics and Computational Biology Faculty of Computer Science University of Vienna Vienna 1090 Austria Institute for Theoretical Chemistry University of Vienna Wien 1090 Austria Halle-Jena-Leipzig and Competence Center for Scalable Data Services Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases University of Leipzig Leipzig D-04107 Germany Max Planck Institute for Mathematics in the Sciences Leipzig D-04103 Germany Fraunhofer Institute for Cell Therapy and Immunology Leipzig D-04103 Germany Center for non-coding RNA in Technology and Health University of Copenhagen Frederiksberg C DK-1870 Denmark Santa Fe Institute 1399 Hyde Park Rd Santa Fe NM 87501 United States Research Network Chemistry Meets Microbiology University of Vienna Wien A-1090 Austria

We present an elaborate framework for formally modelling pathways in chemical reaction networks on a mechanistic level. Networks are modelled mathematically as directed multi-hypergraphs, with vertices corresponding to molecules and hyperedges to reactions. Pathways are modelled as integer hyperflows and we expand the network model by detailed routing constraints. In contrast to the more traditional approaches like Flux Balance Analysis or Elementary Mode analysis we insist on integer-valued flows. While this choice makes it necessary to solve possibly hard integer linear programs, it has the advantage that more detailed mechanistic questions can be formulated. It is thus possible to query networks for general transformation motifs, and to automatically enumerate optimal and near-optimal pathways. Similarities and differences between our work and traditional approaches in metabolic network analysis are discussed in detail. To demonstrate the applicability of the mathematical framework to real-life problems we first explore the design space of possible non-oxidative glycolysis pathways and show that recent manually designed pathways can be further optimised. We then use a model of sugar chemistry to investigate pathways in the autocatalytic formose process. A graph transformation-based approach is used to automatically generate the reaction networks of interest. Copyright © 2017, The Authors. All rights reserved.

关键词： Integer programming

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Publisher Correction: Transcribed ultraconserved region 339 promotes carcinogenesis by modulating tumor suppressor micrornas

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Nature communications 2018年第1期9卷 160页

作者： Ivan Vannini Petra M Wise Kishore B Challagundla Meropi Plousiou Mirco Raffini Erika Bandini Francesca Fanini Giorgia Paliaga Melissa Crawford Manuela Ferracin Cristina Ivan Linda Fabris Ramana V Davuluri Zhiyi Guo Maria Angelica Cortez Xinna Zhang Lu Chen Shuxing Zhang Cecilia Fernandez-Cymering Leng Han Silvia Carloni Samanta Salvi Hui Ling Mariam Murtadha Paolo Neviani Barbara J Gitlitz Ite A Laird-Offringa Patrick Nana-Sinkam Massimo Negrini Han Liang Dino Amadori Amelia Cimmino George A Calin Muller Fabbri Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) S.r.l. IRCCS Gene Therapy Unit Meldola FC 47014 Italy. Departments of Pediatrics and Molecular Microbiology & Immunology Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Children's Center for Cancer and Blood Diseases and The Saban Research Institute Children's Hospital Los Angeles Los Angeles CA 90027 USA. Department of Biochemistry & Molecular Biology University of Nebraska Medical Center 985870 Nebraska Medical Center Omaha NE 68198 USA. Department of Internal Medicine Division of Pulmonary Allergy Critical Care and Sleep Medicine The Ohio State University Columbus OH 43210 USA. Department of Experimental Diagnostic and Specialty Medicine- DIMES University of Bologna Bologna 40126 Italy. Department of Experimental Therapeutics The University of Texas MD Anderson Cancer Center Houston TX 77030 USA. The Center for RNA Interference and Non-coding RNAs The University of Texas MD Anderson Cancer Center Houston TX USA. Departments of Preventive Medicine and Neurological Surgery Northwestern University-Feinberg School of Medicine Chicago IL 60611 USA. Department of Experimental Radiation Oncology 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. Integrated Molecular Discovery Laboratory Department of Experimental Therapeutics The University of Texas MD Anderson Cancer Center Houston TX 77030 USA. Division of Pre-Clinical Innovation National Center for Advancing Translational Sciences (NCATS) Rockville MD 20850 USA. Department of Molecular Virology Immunology and Medical Genetics Comprehensive Cancer Center Ohio State University Columbus OH 43210 USA. Department of Biochemistry and Molecular Biology McGovern Medical School The University of Texas Health Science Center at Hou

In the originally published version of this Article, the positions of the final two authors in the author list were inadvertently inverted during the production process. This error has now been corrected in both the P...

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50 shades of rule composition from chemical reactions to higher levels of abstraction

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8738卷 115-135页

作者： Andersen, Jakob Lykke Flamm, Christoph Merkle, Daniel Stadler, Peter F. Department of Mathematics and Computer Science University of Southern Denmark Denmark Institute for Theoretical Chemistry University of Vienna Austria Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics Leipzig Germany Max Planck Institute for Mathematics in the Sciences Leipzig Germany Fraunhofer Institute for Cell Therapy and Immunology Leipzig Germany Center for Non-coding RNA in Technology and Health University of Copenhagen Denmark Santa Fe Institute United States

ISBN: (纸本)9783319103976

Graph rewriting has been applied quite successfully to model chemical and biological systems at different levels of abstraction. A particularly powerful feature of rule-based models that are rigorously grounded in category theory, is, that they admit a well-defined notion of rule composition, hence, provide their users with an intrinsic mechanism for compressing trajectories and coarse grained representations of dynamical aspects. The same formal framework, however, also allows the detailed analysis of transitions in which the final and initial states are known, but the detailed stepwise mechanism remains hidden. To demonstrate the general principle we consider here how rule composition is used to determine accurate atom maps for complex enzyme reactions. This problem not only exemplifies the paradigm but is also of considerable practical importance for many down-stream analyses of metabolic networks and it is a necessary prerequisite for predicting atom traces for the analysis of isotope labelling experiments. © Springer International Publishing Switzerland 2014.

关键词： Biological systems

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BlockClust: Efficient clustering and classification of non-coding rnas from short read rna-Seq profiles

BlockClust: Efficient clustering and classification of non-c...

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International Conference on German Conference on Bioinformatics, GCB 2014

作者： Videm, Pavankumar Rose, Dominic Costa, Fabrizio Backofen, Rolf Bioinformatics Group Department of Computer Science Albert-Ludwigs-University Freiburg Georges-Köhler-Allee 106 FreiburgD-79110 Germany Munich Germany Albert-Ludwigs-University Freiburg FreiburgD-79104 Germany Albert-Ludwigs-University Freiburg Habsburgerstr. 49 FreiburgD-79104 Germany Centre for Non-Coding RNA in Technology and Health University of Copenhagen Gronnegardsvej 3 Frederiksberg CDK-1870 Denmark

ISBN: (纸本)9783885796299

Sequence and secondary structure analysis can be used to assign putative functions to non-coding rnas. However sequence information is changed by post-transcriptional modifications and secondary structure is only a proxy for the true 3D conformation of the rna polymer. In order to tackle these issues we can extract a different type of description using the pattern of processing that can be observed through the traces left in small rna-seq reads data. To obtain an efficient and scalable procedure, we propose to encode expression profiles in discrete structures, and process them using fast graph-kernel techniques. We present BlockClust for both clustering and classification of small non-coding rna transcripts with similar processing patterns. We show how the proposed approach is scalable, accurate and robust across different organisms, tissues and cell lines. BlockClust was successfully applied on a comprehensive set of eukaryotic data. It is the first tool for eukaryotic non-coding rna analysis available on the galaxy framework.

关键词： Cell culture

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

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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3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 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 USA Graduate Field of Computational Biology Cornell University Ithaca 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 Antwerp Belgium Advanced Database Research and Modeling (ADReM) research group University of Antwerp Antwerpen Belgium Department of Computer Science University of Arizona Tucson 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 Minds 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 India Philips Research North America Briarcliff Manor USA Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Tokyo Japan Systems Biology Centre University of Warwick Senate House Coventry UK Systems Biology Doctoral Training Centre University of Warwick Senate House Coventry UK Center for B

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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Profiling of Ribose Methylations in rna by High‐Throughput Sequencing†

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Angewandte Chemie 2014年第2期127卷

作者： Ulf Birkedal Mikkel Christensen‐Dalsgaard Nicolai Krogh Radhakrishnan Sabarinathan Jan Gorodkin Henrik Nielsen Department of Cellular and Molecular Medicine University of Copenhagen Blegdamsvej 3B 2200 Copenhagen N (Denmark) Center for Non‐coding RNA in Technology and Health Department of Veterinary Clinical and Animal Science University of Copenhagen Grønnegårdsvej 3 1870 Frederiksberg C (Denmark)

Ribose methylations are the most abundant chemical modifications of ribosomal rna and are critical for ribosome assembly and fidelity of translation. Many aspects of ribose methylations have been difficult to study due to lack of efficient mapping methods. Here, we present a sequencing‐based method (RiboMeth‐seq) and its application to yeast ribosomes, presently the best‐studied eukaryotic model system. We demonstrate detection of the known as well as new modifications, reveal partial modifications and unexpected communication between modification events, and determine the order of modification at several sites during ribosome biogenesis. Surprisingly, the method also provides information on a subset of other modifications. Hence, RiboMeth‐seq enables a detailed evaluation of the importance of rna modifications in the cells most sophisticated molecular machine. RiboMeth‐seq can be adapted to other rna classes, for example, mrna, to reveal new biology involving rna modifications.

关键词： Ribosome Ribozyme rna‐Erkennung rna‐Strukturen Transferasen

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