Biological Pathways Exchange Language Level 3, Release Version 1 Documentation
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RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY
RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press ISSN: 1790-5125 www.wseas.org ISBN: 978-960-474-141-0 RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press www.wseas.org Copyright © 2009, by WSEAS Press All the copyright of the present book belongs to the World Scientific and Engineering Academy and Society Press. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Editor of World Scientific and Engineering Academy and Society Press. All papers of the present volume were peer reviewed -
Applied Category Theory for Genomics – an Initiative
Applied Category Theory for Genomics { An Initiative Yanying Wu1,2 1Centre for Neural Circuits and Behaviour, University of Oxford, UK 2Department of Physiology, Anatomy and Genetics, University of Oxford, UK 06 Sept, 2020 Abstract The ultimate secret of all lives on earth is hidden in their genomes { a totality of DNA sequences. We currently know the whole genome sequence of many organisms, while our understanding of the genome architecture on a systematic level remains rudimentary. Applied category theory opens a promising way to integrate the humongous amount of heterogeneous informations in genomics, to advance our knowledge regarding genome organization, and to provide us with a deep and holistic view of our own genomes. In this work we explain why applied category theory carries such a hope, and we move on to show how it could actually do so, albeit in baby steps. The manuscript intends to be readable to both mathematicians and biologists, therefore no prior knowledge is required from either side. arXiv:2009.02822v1 [q-bio.GN] 6 Sep 2020 1 Introduction DNA, the genetic material of all living beings on this planet, holds the secret of life. The complete set of DNA sequences in an organism constitutes its genome { the blueprint and instruction manual of that organism, be it a human or fly [1]. Therefore, genomics, which studies the contents and meaning of genomes, has been standing in the central stage of scientific research since its birth. The twentieth century witnessed three milestones of genomics research [1]. It began with the discovery of Mendel's laws of inheritance [2], sparked a climax in the middle with the reveal of DNA double helix structure [3], and ended with the accomplishment of a first draft of complete human genome sequences [4]. -
Kinetic Modeling Using Biopax Ontology
Kinetic Modeling using BioPAX ontology Oliver Ruebenacker, Ion. I. Moraru, James C. Schaff, and Michael L. Blinov Center for Cell Analysis and Modeling, University of Connecticut Health Center Farmington, CT, 06030 {oruebenacker,moraru,schaff,blinov}@exchange.uchc.edu Abstract – e.g. Cytoscape (http://cytoscape.org, [5]), cPath database http://cbio.mskcc.org/software/cpath, [6]), Thousands of biochemical interactions are PathCase (http://nashua.case.edu/PathwaysWeb), available for download from curated databases such VisANT (http://visant.bu.edu, [7]). However, the as Reactome, Pathway Interaction Database and other current standard for kinetic modeling is Systems sources in the Biological Pathways Exchange Biology Markup Language, SBML ([8], (BioPAX) format. However, the BioPAX ontology http://sbml.org). Both BioPAX and SBML are used to does not encode the necessary information for kinetic encode key information about the participants in modeling and simulation. The current standard for biochemical pathways, their modifications, locations kinetic modeling is the System Biology Markup and interactions, but only SBML can be used directly Language (SBML), but only a small number of models for kinetic modeling, because elements are included in are available in SBML format in public repositories. SBML specifically for the context of a quantitative Additionally, reusing and merging SBML models theory. In contrast, concepts in BioPAX are more presents a significant challenge, because often each abstract. SBML-encoded models typically contain all element has a value only in the context of the given data necessary for simulations, such as molecular model, and information encoding biological meaning species and their concentrations, reactions among is absent. We describe a software system that enables these species, and kinetic laws for these reactions. -
Ontology-Based Methods for Analyzing Life Science Data
Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective . -
Ploidetect Enables Pan-Cancer Analysis of the Causes and Impacts of Chromosomal Instability
bioRxiv preprint doi: https://doi.org/10.1101/2021.08.06.455329; this version posted August 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Ploidetect enables pan-cancer analysis of the causes and impacts of chromosomal instability Luka Culibrk1,2, Jasleen K. Grewal1,2, Erin D. Pleasance1, Laura Williamson1, Karen Mungall1, Janessa Laskin3, Marco A. Marra1,4, and Steven J.M. Jones1,4, 1Canada’s Michael Smith Genome Sciences Center at BC Cancer, Vancouver, British Columbia, Canada 2Bioinformatics training program, University of British Columbia, Vancouver, British Columbia, Canada 3Department of Medical Oncology, BC Cancer, Vancouver, British Columbia, Canada 4Department of Medical Genetics, Faculty of Medicine, Vancouver, British Columbia, Canada Cancers routinely exhibit chromosomal instability, resulting in tumors mutate, these variants are considerably more difficult the accumulation of changes in the abundance of genomic ma- to detect accurately compared to other types of mutations terial, known as copy number variants (CNVs). Unfortunately, and consequently they may represent an under-explored the detection of these variants in cancer genomes is difficult. We facet of tumor biology. 20 developed Ploidetect, a software package that effectively iden- While small mutations can be determined through base tifies CNVs within whole-genome sequenced tumors. Ploidetect changes embedded within aligned sequence reads, CNVs was more sensitive to CNVs in cancer related genes within ad- are variations in DNA quantity and are typically determined vanced, pre-treated metastatic cancers than other tools, while also segmenting the most contiguously. -
BIOINFORMATICS APPLICATIONS NOTE Pages 380-381
Vol. 14 no. 4 1998 BIOINFORMATICS APPLICATIONS NOTE Pages 380-381 MView: a web-compatible database search or multiple alignment viewer NigelP. Brown, ChristopheLeroy and Chris Sander European Bioinformatics Institute (EMBLĆEBI), Wellcome Genome Campus, CambridgeCB10 1SD, UK Received on December 10, 1997; revised and accepted on January 15, 1998 Abstract may be hyperlinked to the SRS system (Etzold et al., 1996), a text field, a field of scoring information from searches, and Summary: MView is a tool for converting the results of a a field reporting the per cent identity of each sequence with sequence database search into the form of a coloured multiple respect to a preferred sequence in the alignment, usually the alignment of hits stacked against the query. Alternatively, an query in the case of a search. existing multiple alignment can be processed. In either case, Multiple alignments require minimal parsing and are the output is simply HTML, so the result is platform independent and does not require a separate application or subjected only to formatting stages. Search hits are first applet to be loaded. stacked against the ungapped query sequence and require Availability: Free from http://www.sander.ebi.ac.uk/mview/ special processing. Ungapped search (e.g. BLAST) hit subject to copyright restrictions. fragments are assembled into a single string by overlaying Contact: [email protected] them preferentially by score onto a template string, while gapped search (e.g. FASTA) hits have columns corresponding Often when running FASTA (Pearson, 1990) or BLAST to query gaps excised. Consequently, the stacked alignment is (Altschul et al., 1990), it is desired to visualize the database a patchwork of reconstituted sequences that nevertheless is hits stacked against the query sequence. -
Microblogging the ISMB: a New Approach to Conference Reporting
Message from ISCB Microblogging the ISMB: A New Approach to Conference Reporting Neil Saunders1*, Pedro Beltra˜o2, Lars Jensen3, Daniel Jurczak4, Roland Krause5, Michael Kuhn6, Shirley Wu7 1 School of Molecular and Microbial Sciences, University of Queensland, St. Lucia, Brisbane, Queensland, Australia, 2 Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California, United States of America, 3 Novo Nordisk Foundation Center for Protein Research, Panum Institute, Copenhagen, Denmark, 4 Department of Bioinformatics, University of Applied Sciences, Hagenberg, Freistadt, Austria, 5 Max-Planck-Institute for Molecular Genetics, Berlin, Germany, 6 European Molecular Biology Laboratory, Heidelberg, Germany, 7 Stanford Medical Informatics, Stanford University, Stanford, California, United States of America Cameron Neylon entitled FriendFeed for Claire Fraser-Liggett opened the meeting Scientists: What, Why, and How? (http:// with a review of metagenomics and an blog.openwetware.org/scienceintheopen/ introduction to the human microbiome 2008/06/12/friendfeed-for-scientists-what- project (http://friendfeed.com/search?q = why-and-how/) for an introduction. room%3Aismb-2008+microbiome+OR+ We—a group of science bloggers, most fraser). The subsequent Q&A session of whom met in person for the first time at covered many of the exciting challenges The International Conference on Intel- ISMB 2008—found FriendFeed a remark- for those working in this field. Clearly, ligent Systems for Molecular Biology -
Academic Year 2007 Computing Project Group Report SBML-ABC, A
Academic year 2007 Computing project Group report SBML-ABC, a package for data simulation, parameter inference and model selection Imperial College- Division of Molecular Bioscience MSc. Bioinformatics Nathan Harmston, David Knowles, Sarah Langley, Hang Phan Supervisor: Professor Michael Stumpf June 13, 2008 Contents 1 Introduction 1 1.1 Background ...................................... 1 2 Features and dependencies 2 2.1 Project outline ..................................... 2 2.2 Key features ...................................... 2 2.2.1 SBML ..................................... 2 2.2.2 Stochastic simulation ............................. 4 2.2.3 Deterministic simulation ........................... 4 2.2.4 ABC inference ................................ 5 2.3 Interfaces ....................................... 5 2.3.1 CAPI ..................................... 5 2.3.2 Command line interface ........................... 5 2.3.3 Python .................................... 5 2.3.4 R ....................................... 6 2.4 Dependencies ..................................... 6 3 Methods 8 3.1 SBML Adaptor .................................... 8 3.2 Stochastic simulation ................................. 8 3.2.1 Random number generator .......................... 9 3.2.2 Multicompartmental Gillespie algorithm ................... 9 3.2.3 Tau leaping .................................. 10 3.2.4 Chemical Langevin Equation ......................... 11 3.3 Deterministic algorithms ............................... 11 3.3.1 ODE solvers ................................ -
The Biopax Community Standard for Pathway Data Sharing
Supplementary Table S1. An example BioPAX file describing the phosphorylation and activation of CHK2 by ATM in human. Data was originally obtained from the Reactome database8. <?xml version="1.0"?> <rdf:RDF xmlns="http://www.biopax.org/examples/myExample#" xmlns:bp="http://www.biopax.org/release/biopax-level3.owl#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:owl="http://www.w3.org/2002/07/owl#" xml:base="http://www.biopax.org/examples/myExample"> <owl:Ontology rdf:about=""> <owl:imports rdf:resource="http://www.biopax.org/release/biopax-level3.owl"/> </owl:Ontology> <rdf:Property rdf:about="http://www.biopax.org/release/biopax- level3.owl#direction"/> <bp:Protein rdf:ID="Protein_5"> <bp:dataSource> <bp:Provenance rdf:ID="Provenance_3"> <bp:displayName rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >Reactome (http://reactome.org)</bp:displayName> <bp:xref> <bp:PublicationXref rdf:ID="pubmed"> <bp:year rdf:datatype="http://www.w3.org/2001/XMLSchema#int" >2003</bp:year> <bp:db rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >pubmed</bp:db> <bp:title rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >The Genome Knowledgebase: a resource for biologists and bioinformaticists.</bp:title> <bp:id rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >15338623</bp:id> </bp:PublicationXref> </bp:xref> </bp:Provenance> </bp:dataSource> <bp:cellularLocation> <bp:CellularLocationVocabulary rdf:ID="CellularLocationVocabulary_6"> -
Modeling Without Borders: Creating and Annotating Vcell Models Using the Web
Modeling without Borders: Creating and Annotating VCell Models Using the Web Michael L. Blinov, Oliver Ruebenacker, James C. Schaff, and Ion I. Moraru Center for Cell Analysis and Modeling University of Connecticut Health Center Farmington, CT 06030, USA {blinov,oruebenacker,schaff,moraru}@exchange.uchc.edu http:vcell.org/sybil Abstract. Biological research is becoming increasingly complex and data-rich, with multiple public databases providing a variety of resources: hundreds of thousands of substances and interactions, hundreds of ready to use models, controlled terms for locations and reaction types, links to reference materials (data and/or publications), etc. Mathematical mod- eling can be used to integrate this complex data and create quantita- tive, testable predictions based on the current state of knowledge of a biological process. Data retrieval, visualization, flexible querying, and model annotation for future reuse, are some of the important require- ments for modeling-based research in the modern age. Here we describe an approach that we implement within the popular Virtual Cell (VCell) modeling and simulation framework in order to help connect the mod- eling community with the web of machine-processable systems biology knowledge. A new software application, called SyBiL (Systems Biology Linker), has been designed and developed for simultaneous querying of multiple systems biology knowledge bases and data sources, such as web repositories, databases, and user files, and converting the extracted and refined data into model elements. Integration of SyBiL as a component of VCell makes these capabilities easily available to a wide modeling community. Keywords: Biological databases, mathematical modeling, VCell, data conversion. 1 Introduction Mathematical modeling is increasingly necessarry to investigate the function of molecular pathways and networks, and a growing number of resources that fa- cilitate computational approaches are becoming available. -
The Basic Units of Life How Cell Atlases Can Shed Light on Disease Mechanisms with Remarkable Accuracy
Press Information, July 28, 2021 Cells: The Basic Units of Life How cell atlases can shed light on disease mechanisms with remarkable accuracy The Schering Stiftung awards the Ernst Schering Prize 2021 to Aviv Regev. A pioneer in the field of single-cell analysis, she successfully combines approaches from biology and computer science and thus revolutionizes the field of precision medicine. Aviv Regev is considered a pioneer in the field of single-cell biology and has broken new ground by combining the disciplines of biology, computation, and genetic engineering. She has uniquely succeeded in combining and refining some of the most important experimental and analytical tools in such a way that she can analyze the genome of hundreds of thousands of single cells simultaneously. This single-cell genome analysis makes it possible to map and characterize a large number of individual tissue cells. Aviv Regev was the first to apply these single-cell technologies to solid tumors and to successfully identify those cells and genes that influence tumor growth and resistance to treatment. In addition, she discovered rare cell types that are involved in cystic fibrosis and ulcerative colitis. Last but not least, together with international research groups, and her colleague Sarah Teichmann she built the Human Cell Atlas and inspired Prof. Aviv Regev, PhD scientists all over the world to use these tools to create a Photo: Casey Atkins comprehensive atlas of all cell types in the human body. These cell atlases of parts of the human body illuminate disease mechanisms with remarkable accuracy and have recently also been used successfully to study disease progression in COVID-19. -
Reactome Pathway Analysis
Fabregat et al. BMC Bioinformatics (2017) 18:142 DOI 10.1186/s12859-017-1559-2 SOFTWARE Open Access Reactome pathway analysis: a high- performance in-memory approach Antonio Fabregat1,2, Konstantinos Sidiropoulos1, Guilherme Viteri1, Oscar Forner1, Pablo Marin-Garcia3,4, Vicente Arnau5,6, Peter D’Eustachio7, Lincoln Stein8,9 and Henning Hermjakob1,10* Abstract Background: Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Results: Here, we present a new high-performance in-memory implementation of the well-established over- representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user’s sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. Conclusion: Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data.