Chemistry Requirements

Total Page:16

File Type:pdf, Size:1020Kb

Chemistry Requirements OBO: chemistry requirements Colin Batchelor July 2, 2008 Abstract We list some urgent corrections which are needed to make ChEBI compatible with BFO, RO and the other OBO Foundry ontologies, as well as outlining developments which will minimize future curatorial effort. The urgent corrections are to ensure: is a completeness, that the is a relation is transitive, that the parthood relations are compatible with RO, that granular and determinate parthood are distinguished, that the names of important terms reflect biomedical usage rather than IUPAC's prescriptions, and that the top-level terms are rearranged to reflect the new parthood relations. The future developments outlined are to replace most of the asserted structure of ChEBI with an inferred structure, and to introduce a cross-product structure compatible with postcomposition of terms. 1 Introduction Not only are organisms made of chemical entities, but fundamental processes in biological organisms such as the Krebs cycle, DNA transcription, RNA transla- tion and post-translational modification are defined in terms of transformations of chemical entities. It is for this reason that the Open Biomedical Ontologies (OBO) need a biochemical ontology. In order to interoperate effectively with other OBO ontologies, any ontology should • be is a complete • have an is a relation that is transitive • reuse relations from RO where appropriate • distinguish between granular and determinate parthood (Rogers et al.) • distinguish between dependent and independent continuants (BFO) A biochemical ontology that is part of OBO we needs to provide a chemical framework for biomedical ontologies such as the molecular function and biolog- ical process ontologies in GO, the types of molecule that are implied by the 1 Sequence Ontology, and the chemical classes in the Systems Biology Ontology. In order that the ontologies can be aligned, the biochemical ontology should as far as possible reflect how entities are talked about in the literature. For example, calling a term \nucleus", which is polysemous, should be deprecated in favour of, for example, \atomic nucleus". The key problem with a general-purpose chemical ontology is that the enti- ties it will be called upon to describe is combinatorially large|more compounds are synthesizable using readily available materials than there are atoms in the universe by several orders of magnitude|therefore the curation task will always dwarf the curatorial effort. This is why machine-generated identifiers such as the InChI or the SMILES have been so successful in chemistry. However, there is no open algorithm that maps between chemical names and machine-generated identifiers, and this is a task to which a general-purpose chemical ontology would make an important contribution. In order to cope with the sheer size of chemical space, a general-purpose chemical ontology has to be: • Generous with its inferred structure and extremely parsimonious with its asserted structure. • A cross-product structure which can be used for recursive postcomposition of terms. In this squib we present some urgent fixes to ChEBI (Chemical Entities of Biological Interest) which should make it more effectively interoperable with the other OBO Foundry ontologies and in passing outline how to produce a general- purpose chemical ontology based largely on an inferred structure rather than an asserted structure. The squib is structured as follows: in section 2 we present an upper-level ontology for chemistry, based on previous work by this author and discuss the difference between atoms and elements. Section 3 discusses polysemy, subsumption and parthood relations in ChEBI, giving fixes for is a completeness, transitivity and the parthood relations, as well as proposing a new way of writing genus{differentia definitions for molecules. Finally, section 4 has a very brief account of the meaning of amino acid names in both ChEBI and the literature. 2 The upper level 2.1 An upper-level ontology Currently most terms in ChEBI are descendants of molecular entities CHEBI:23367, defined as A molecular entity is any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, con- former etc., identifiable as a separately distinguishable entity. 2 This has a wide range of descendants, whose identity may or may not be pre- served when parts are gained or lost. Chemistry, as the science of transforma- tions at the molecular level, needs an ontology which can distinguish identity- preserving transformations from identity-changing transformations. For exam- ple, salts CHEBI:24866, salts being collectives that can lose or gain ions without changing their identity, is a sibling of cooordination entities CHEBI:33240, even though if a coordination complex loses a metal ion it changes its identity. There are clearly different parthood relations that apply to these terms and their de- scendants, but it is impossible to work out what parthood relations apply other than by close inspection of the term. Elsewhere in the ontology, the parts of kanamycin CHEBI:6104 are ingredients of a mixture, though this is nowhere signalled in its ancestry. There is also no obvious mapping to BFO. Based on its children, molecular structure CHEBI:24431 looks like a snap:IndependentContinuant. However, its definition begins \A description..." Such a non-realist definition is completely inappropriate for an ontology which is to be aligned with realist biomedical ontologies. The classes from BFO that describe molecules of biomedical interest are snap:Object and snap:FiatObjectPart. (We defer the question of what to do with the application and biological role terms to another meeting.) With this in mind, we present a minimal set of terms for an upper-level chemistry ontology. This has been written up in more detail in Batchelor (2008, attached). None of the terms below should have an is a parent within ChEBI, or be part of anything else. snap:FiatObjectParts: • molecular part (in ChEBI as groups (CHEBI:24433), is a molecular en- tities (ChEBI:23367)) snap:Objects that only have determinate parts: • molecule (= polyatomic entities (CHEBI:36357) is a molecular entities (CHEBI:23367)) • atom (= atoms (CHEBI:33250), no is a parent) • subatomic particle (= subatomic particle (CHEBI:36342), no is a parent) snap:Objects that are collectives: • salt (= salts CHEBI:24866, is a heteroatomic molecular entities (CHEBI:37577), is a polyatomic entities (CHEBI:36357)) • mixture (not in ChEBI) • pure substance (not in ChEBI) I have chosen a set which is pairwise disjoint|everything in ChEBI should fit into one and only one of the above categories. This is why ion and radical do not appear in this list; a species may be both a molecule and an ion, or both an atom and a radical. 3 Further consequences of this are that molecular entities CHEBI:23367 should be obsoleted, as should terms of the general type Xium molecular entities, being replaced by Xium molecule or Xium salt or an appropriately-named term to describe the macroscopic, pure substance. 2.2 Atoms and elements The is a children of atoms CHEBI:33250 are main group elements CHEBI:33318, s-block elements CHEBI:33559, metals ChEBI:33521 and nonmetals CHEBI:25585. To handle the \elements" terms first, it is simply not true that a chemical element can stand in an is a relationship with an atom, or that \element" is a synonym for \atom". You cannot substitute the word \element" for \atom" in a sentence and expect the sentence to still make sense. Laser-cooled neutral atoms localized in a deeply confining optical potential satisfy this requirement. cannot be changed to Laser-cooled neutral elements localized in a deeply confining optical potential satisfy this requirement. The first definition in the IUPAC Gold Book of \chemical element" is: 1. A species of atoms; all atoms with the same number of protons in the atomic nucleus. The first part of that sentence implies strongly that \element" does not belong in a chemical or biochemical ontology, just as \species" does not belong in a biological taxonomy; the second part, which seems to mean the mereological sum of all atoms with the same number of protons in the universe, is too eccentric to include in an ontology. The second definition in the IUPAC Gold Book is: 2. A pure chemical substance composed of atoms with the same number of protons in the atomic nucleus. Sometimes this concept is called the elementary substance as distinct from the chemical el- ement as defined under 1, but mostly the term chemical element is used for both concepts. We can at least put something that fits this definition in a jar, but it is a collec- tive of atoms, and therefore can neither have an is a relationship to \atoms" nor be a synonym of \atoms". What unites all the manifestations of a chemical element, whether it be a meitnerium atom created in a particle accelerator, a lump of solid gold or atmospheric carbon, is the relevant atom itself. We know atoms exist. Consequence 1: main group elements CHEBI:33318 should be renamed to \main group atoms", and s-block elements CHEBI:33559 to \s-block atoms". Similarly all terms in ChEBI ending in \elements". 4 Consequence 2: Terms that have the name of a chemical element should have \atom" added to the end of the name. Likewise \chalcogens" and \pnictogens" should become \chalcogen atom" and \pnictogen atom". Secondly, it is not true that all metals are some atom. Metallicity is a prop- erty of collectives of atoms or molecules rather than individual atoms. Phrases like \metal{oxygen bonding" are examples of a regular polysemy, where the name of the collective stands for a grain. We should probably tolerate \metal atoms" and \nonmetal atoms" for a biochemical ontology, but their current is a children should become children of \atoms".. 3 Polysemy, subsumption and parthood 3.1 Inferred vs. asserted structure Asserted relationships are those that have been manually added by a human curator, while inferred relationships have been worked out by a reasoner.
Recommended publications
  • The Structure and Antioxidant Properties
    materials Review Recent Developments in Effective Antioxidants: The Structure and Antioxidant Properties Monika Parcheta 1 , Renata Swisłocka´ 1,* , Sylwia Orzechowska 2,3 , Monika Akimowicz 4 , Renata Choi ´nska 4 and Włodzimierz Lewandowski 1 1 Department of Chemistry, Biology and Biotechnology, Bialystok University of Technology, Wiejska 45E, 15-351 Bialystok, Poland; [email protected] (M.P.); [email protected] (W.L.) 2 Solaris National Synchrotron Radiation Centre, Jagiellonian University, Czerwone Maki 98, 30-392 Krakow, Poland; [email protected] 3 M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland 4 Prof. Waclaw Dabrowski Institute of Agriculture and Food Biotechnology–State Research Institute, Rakowiecka 36, 02-532 Warsaw, Poland; [email protected] (M.A.); [email protected] (R.C.) * Correspondence: [email protected] Abstract: Since the last few years, the growing interest in the use of natural and synthetic antioxidants as functional food ingredients and dietary supplements, is observed. The imbalance between the number of antioxidants and free radicals is the cause of oxidative damages of proteins, lipids, and DNA. The aim of the study was the review of recent developments in antioxidants. One of the crucial issues in food technology, medicine, and biotechnology is the excess free radicals reduction to obtain healthy food. The major problem is receiving more effective antioxidants. The study aimed to analyze the properties of efficient antioxidants and a better understanding of the molecular ´ Citation: Parcheta, M.; Swisłocka, R.; mechanism of antioxidant processes. Our researches and sparing literature data prove that the Orzechowska, S.; Akimowicz, M.; ligand antioxidant properties complexed by selected metals may significantly affect the free radical Choi´nska,R.; Lewandowski, W.
    [Show full text]
  • S41467-020-16549-2.Pdf
    ARTICLE https://doi.org/10.1038/s41467-020-16549-2 OPEN Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN ✉ Maria Masid 1, Meric Ataman 2 & Vassily Hatzimanikatis 1 Altered metabolism is associated with many human diseases. Human genome-scale meta- bolic models (GEMs) were reconstructed within systems biology to study the biochemistry 1234567890():,; occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the con- sistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the ther- modynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells. 1 Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. 2 Computational and ✉ Systems Biology, Biozentrum, University of Basel, Basel, Switzerland. email: vassily.hatzimanikatis@epfl.ch NATURE COMMUNICATIONS | (2020)11:2821 | https://doi.org/10.1038/s41467-020-16549-2 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-16549-2 n altered metabolism is a hallmark of several human compartmentalized as Recon 2 with an additional compartment diseases, such as cancer, diabetes, obesity, Alzheimer’s, for the mitochondria intermembrane space.
    [Show full text]
  • DATABASE Report April 2019 [email protected] TABLE of CONTENTS TABLE of CONTENTS
    DATABASE Report April 2019 [email protected] TABLE OF CONTENTS TABLE OF CONTENTS .................................................................................................................. 2 INTRODUCTION ............................................................................................................................. 3 GENERAL OVERVIEW OF DATABASE TEAM ACTIVITIES ......................................................... 3 PUBLIC ENGAGEMENT – PROMOTING OUR RESOURCES ............................................................ 3 PUBLICATIONS ................................................................................................................................. 3 OUTREACH AND SOCIAL MEDIA ......................................................................................................... 4 THE GUIDE TO PHARMACOLOGY DATABASE (GTOPDB) ........................................................ 6 GTOPDB WEB SITE ACCESS STATISTICS ............................................................................................ 6 ACQUISITION, BROWSERS & DEVICES ............................................................................................... 7 GTOPDB CONTENT .......................................................................................................................... 8 DOWNLOAD STATISTICS ............................................................................................................... 9 SUPPLIER LINKS ...........................................................................................................................
    [Show full text]
  • Using Natural Language Processing Methods to Support Curation of a Chemical Ontology., Doctor of Philosophy, May 2014 DECLARATION
    USINGNATURALLANGUAGEPROCESSING METHODSTOSUPPORTCURATIONOFA CHEMICAL ONTOLOGY. adam bernard Homerton College, University of Cambridge & European Bioinformatics Institute May 2014 This dissertation is submitted for the degree of Doctor of Philosophy. Adam Bernard: Using Natural Language Processing methods to support curation of a chemical ontology., Doctor of Philosophy, May 2014 DECLARATION This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indi- cated in the text. This dissertation does not exceed the word limit as specified by the Degree Committee for the Faculty of Biology. Cambridge, May 2014 Adam Bernard To Hugh R. S. Jones, who taught me the value of back-of-the-envelope calculations, and to the memory of my grandparents Elise Kersh and Sidney &Norma Bernard. SUMMARY Adam Bernard Using Natural Language Processing methods to support curation of a chemical on- tology. This thesis describes various techniques to assist the curation of a chemi- cal ontology (ChEBI) using a combination of textmining techniques and the resources of the ontology itself. ChEBI is an ontology of small molecules that are either produced by, or otherwise relevant to, biological organisms. It is manually expert-curated, and as such has high reliablity but incomplete coverage. To make efficient use of curator time, it is desirable to have auto- matic suggestions for chemical species and their properties, to be assessed for inclusion in ChEBI. Having developed a system to identify chemicals within biological text, I use a combination of a syntactic parser and a small set of textual patterns to extract hypernyms of these chemicals (categories of chemicals where there is an is-a relationship e.g.
    [Show full text]
  • The Experimental Factor Ontology Utilities in Knowledge Discovery and Sharing
    The Experimental Factor Ontology Utilities in Knowledge Discovery and Sharing Sirarat Sarntivijai, Tony Burdett, Simon Jupp, and Helen Parkinson http://www.ebi.ac.uk/efo/ [email protected] @SSirarat Outline • Ontologies at the EBI • Using and building ontologies with the real data at the Centre for Therapeutic Target Validation (CTTV) • The ontology toolkit • Challenges Pre-RDA Plenary – 22/09/2015 Data resources at EMBL-EBI Genes, genomes & variation European Nucleotide Archive Ensembl European Genome-phenome Archive 1000 Genomes Ensembl Genomes Metagenomics portal Gene, protein & metabolite expression ArrayExpress Metabolights Expression Atlas PRIDE Literature & ontologies Protein sequences, families & motifs Europe PubMed Central Gene Ontology InterPro Pfam UniProt Experimental Factor Ontology BioSamples Database Molecular structures Protein Data Bank in Europe Electron Microscopy Data Bank +~10, BAO, CTO, ORDO, ChEBI, Fly Anatomy, SWO, Chemical biology OBI, …. ChEMBL ChEBI Reactions, interactions & pathways Systems BioModels BioSamples IntAct Reactome MetaboLights Enzyme Portal Pre-RDA Plenary – 22/09/2015 The Experimental Factor Ontology • We build the EFO in OWL • EFO is an application ontology, built for use in production services • Lots of external dependencies - reuses where possible and appropriate other references Chemical Entities of Biological Interest Cell Type Ontology (ChEBI) Gene Ontology PATO Pre-RDA Plenary – 22/09/2015 EFO reuses existing knowledge Pre-RDA Plenary – 22/09/2015 Working the abundance of data • BioMedBridges,
    [Show full text]
  • SOT 2017 P225 AOP Poster Luettich Et Al Final
    The Adverse Outcome Pathway For Mucus Hypersecretion in Chronic Bronchitis Introduction Karsta Luettich1, Marja Talikka1, FrazerIntroduction J. Lowe2, Linsey E. Haswell2, Justyna Szostak1, Florian Martin1, Marianna Gaça2, Julia Hoeng1 Introduction Poster Board P225 Abstract 2823 1 Philip Morris Products S.A. (part of Philip Morris International group of companies), Neuchatel, Switzerland 2 British American Tobacco (Investments) Ltd, Southampton, UK Introduction Adverse Outcome Pathway - Description and Evaluation Biological Network Analysis as Potential Tool for Quantitative AOP Evaluation complex(SCOMP:"NADPH act(p(HGNC:"Proteases, composite(p(HGNC:TNF),p(HGNC:EGF)) Oxidase act(p(HGNC:ELANE)) p(HGNC:CAMP) serine")) Chronic obstructive pulmonary disease (COPD) is one of the adverse outcomes resulting from cigarette smoking composite(p(HGNC:TNF),p(HGNC:TGFA)) Complex") composite(p(HGNC:SPDEF),p(HGNC:FOXA3))p(HGNC:CLCA1) and manifests as obstruction of the small airways, chronic bronchitis, or emphysema. There is a need to establish p(HGNC:FOXA3) act(p(HGNC:P2RY2)) p(HGNC:ADAM17)p(HGNC:RPS6KA1,pmod(P)) a(CHEBI:"hyaluronic tloc(p(SFAM:"PRKC acid") a mechanistic understanding of COPD development in response to chronic exposure to inhaled toxicants for risk KE2 IL4/13 KE3 KE8 Family"),MESHCS:Cytoplasm,MESHCS:"Cell Decreased Membrane") p(SFAM:"PRKC assessment and regulatory decision-making. The Adverse Outcome Pathway (AOP) framework provides a means Transdifferentiation act(p(HGNC:FOXA3)) act(p(HGNC:ADAM17)) Apoptosis of Goblet cell Family") to outline a knowledge-driven sequence of events from exposure to adverse outcome (AO). Here, we describe CASP3↓ of Ciliated Epithelial Ciliated Epithelial metaplasia act(p(SFAM:"PRKC sec(p(HGNC:TGFA))sec(p(HGNC:AREG)) an AOP for EGFR-mediated mucus hypersecretion that leads to chronic bronchitis and COPD.
    [Show full text]
  • Expanding Opportunities for Mining Bioactive Chemistry from Patents, Drug Discov Today: Technol (2015), 10.1016/J.Ddtec.2014.12.001
    DDTEC-426; No of Pages 7 Vol. xxx, No. xx 2015 Drug Discovery Today: Technologies Editors-in-Chief Kelvin Lam – Simplex Pharma Advisors, Inc., Boston, MA, USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY TODAY TECHNOLOGIES From Chemistry to Biology Database Curation Expanding opportunities for mining bioactive chemistry from patents Christopher Southan IUPHAR/BPS Database and Guide to PHARMACOLOGY Web Portal Group, Centre for Integrative Physiology, School of Biomedical 1 Sciences, University of Edinburgh, Hugh Robson Building, Edinburgh EH8 9XD, UK Bioactive structures published in medicinal chemistry Section editor: patents typically exceed those in papers by at least Antony Williams – Royal Society of Chemistry, Wake twofold and may precede them by several years. The Forest, NC, USA. Big-Bang of open automated extraction since 2012 has contributed to over 15 million patent-derived com- (both commercial and academic) but also contain substan- tially more structure–activity relationship (SAR) results than pounds in PubChem. While mapping between chemical journals. This article will focus on exploring scientific value structures, assay results and protein targets from patent rather than intellectual property (IP) because, while both documents is challenging, these relationships can be aspects are intertwined, the analytical approaches diverge. harvested using open tools and are beginning to be Value curated into databases. The question needs to be posed as to what data-centric patent mining has to offer practitioners in cheminformatics, phar- Introduction macology, medicinal chemistry or chemical biology. To an- Compared to papers, patents in the biological sciences have swer this, it is necessary to compare the availability of data hitherto been an underexploited information source, prin- from non-patent sources.
    [Show full text]
  • The Role of Uniprot's Protein Sequence Databases in Biomedical Research
    Andrew Nightingale1, Tunca Dogan1, Diego Poggioli1, Maria Martin1 and the UniProt Consortium1,2,3 1 EMBL-European Bioinformatics Institute, Cambridge, UK 2 SIB Swiss Institute of Bioinformatics, Geneva, Switzerland 3 Protein Information Resource, Georgetown University, Washington DC & University od Delaware, USA The Role of UniProt's Protein Sequence Databases in Biomedical Research Introduction Mapping diseases to InterPro UniProt provides human disease information with extensive Domains, Variants and ChEMBL cross-references to disease relevant databases such as: Medical Subject Headings (MeSH)1 and Online Mendelian Inheritance in Man Compounds (OMIM)2, Figure 1. In order to further enhance the functional annotations relevance to biomedical research; UniProt has recently developed a pipeline for importing protein altering variants from globally ● 4,246 diseases mapped to 2,337 InterPro domains. recognised genetic variant repositories with the aim to extend the ● 316 InterPro domains from 510 protein entries matched to 3,601 ChEMBL manually curated set of natural protein altering variants provided by ligands. UniProt. By combining these resources UniProt has become a more relevant resource for biomedical research and drug target identification. ● Somatic variants have been found within the binding pockets of proteins Here we describe how users of UniProt can develop methodologies to associated to specific cancer types. utilise the described cross-references, protein structure and functional annotations to explore how structural, functional and chemical ligand annotations can be utilised to identify relationships between a protein and disease causing variants. Mitogen-activated Protein Kinase 4 Example Figure 1: Disease and natural variant annotation for UniProtKB/SwissProt entry for Human BRCA1 Figure 3: MAPK4 binding pocket with analogue inhibitor bound and p.Ser233Ala variant.
    [Show full text]
  • Carbon Dioxide - Wikipedia
    5/20/2020 Carbon dioxide - Wikipedia Carbon dioxide Carbon dioxide (chemical formula CO2) is a colorless gas with Carbon dioxide a density about 60% higher than that of dry air. Carbon dioxide consists of a carbon atom covalently double bonded to two oxygen atoms. It occurs naturally in Earth's atmosphere as a trace gas. The current concentration is about 0.04% (412 ppm) by volume, having risen from pre-industrial levels of 280 ppm.[8] Natural sources include volcanoes, hot springs and geysers, and it is freed from carbonate rocks by dissolution in water and acids. Because carbon dioxide is soluble in water, it occurs naturally in groundwater, rivers and lakes, ice caps, glaciers and seawater. It is present in deposits of petroleum and natural gas. Carbon dioxide is odorless at normally encountered concentrations, but at high concentrations, it has a sharp and acidic odor.[1] At such Names concentrations it generates the taste of soda water in the Other names [9] mouth. Carbonic acid gas As the source of available carbon in the carbon cycle, atmospheric Carbonic anhydride carbon dioxide is the primary carbon source for life on Earth and Carbonic oxide its concentration in Earth's pre-industrial atmosphere since late Carbon oxide in the Precambrian has been regulated by photosynthetic organisms and geological phenomena. Plants, algae and Carbon(IV) oxide cyanobacteria use light energy to photosynthesize carbohydrate Dry ice (solid phase) from carbon dioxide and water, with oxygen produced as a waste Identifiers product.[10] CAS Number 124-38-9 (http://ww w.commonchemistr CO2 is produced by all aerobic organisms when they metabolize carbohydrates and lipids to produce energy by respiration.[11] It is y.org/ChemicalDeta returned to water via the gills of fish and to the air via the lungs of il.aspx?ref=124-38- air-breathing land animals, including humans.
    [Show full text]
  • Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (Lexebi)
    Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI) Dietrich Rebholz-Schuhmann1,2*, Jee-Hyub Kim2, Ying Yan2, Abhishek Dixit2, Caroline Friteyre2, Robert Hoehndorf3, Rolf Backofen4, Ian Lewin2 1 Department of Computational Linguistics, University of Zu¨rich, Zu¨rich, Switzerland, 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL- EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom, 3 Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom, 4 Albert-Ludwigs-University Freiburg, Fahnenbergplatz, Freiburg, Germany Abstract Motivation: Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical ‘‘term space’’ (the ‘‘Lexeome’’), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness). Result: This study compiles a resource for lexical terms of biomedical interest in a standard format (called ‘‘LexEBI’’), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use.
    [Show full text]
  • Extension of Roles in the Chebi Ontology
    Extension of Roles in the ChEBI Ontology This manuscript (permalink) was automatically generated from chemical-roles/manuscript@03ed444 on June 30, 2020. Authors Charles Tapley Hoyt 0000-0003-4423-4370 · cthoyt · cthoyt Enveda Therapeutics Christopher J Mungall 0000-0002-6601-2165 · cmungall · chrismungall Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Nicole Vasilevsky 0000-0001-5208-3432 · nicolevasilevsky · N_Vasilevsky Oregon Health and Science University, Portland, OR 97239, USA Daniel Domingo-Fernández 0000-0002-2046-6145 · ddomingof · daniel_sunday Enveda Therapeutics Matthew Healy 0000-0001-6439-5038 Enveda Therapeutics Viswa Colluru 0000-0002-6208-9288 Enveda Therapeutics Motivation The Chemical Entities of Biological Interest [1,2] ontology models chemicals, their classes, their roles, and their interrelations (Figure 1, left). While many roles correspond to how their substituent chemicals aect proteins and other biological entities (see Figure 3), this information is not formalized nor structured in the ChEBI ontology. Ying et al. [3] previously described how these correspondences could be theoretically formalized. This article proposes a concrete schema and axioms through which these roles can be linked to their target entities (Figure 1, right), a suite of open source, reusable curation tools, and ultimately a manually curated database of relationships between chemical roles and their targets. Figure 1: Schema for inference of chemicals’ relations to targets via roles. Targets may be other chemicals, proteins, protein families, protein complexes, pathways, pathologies, or organisms. Throughout this article, the term role (in the context of the ChEBI ontology) will be used in the colloquial sense described by Batchelor et al.
    [Show full text]
  • Chebi Guidelines URL Description Chemical Entities of Biological Interest (Chebi) Is a Freely Avail
    ChEBI Guidelines URL https://www.ebi.ac.uk/chebi/ Description Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on 'small' chemical compounds. Small chemical compounds are usually drugs that help regulate biological processes. A Warning On Nomenclature As stated in other guideline sheets, some authors may use a word to describe a chemical compound that is not included in the ChEBI database. If you find that you are having a difficult time finding anything on a particular compound, consult Wikipedia or PubMed for alternate terms. Guidelines If your BioModel includes instances of pharmaceutical intervention, then this database will be of use to you. This guide will be using ‘caffeine’ as an example. The homepage contains a search bar at the center of the page, with two filters. The first filter says “Search for *** only”, and the second says “All in ChEBI”. You will be checking the circle that says “All in ChEBI”. The “***” indicate that the small molecule was curated by the ChEBI team, but we don’t want to limit our search to only this category. In the search bar, type in ‘caffeine’ (without the quotes). You will be taken to the search results page for your query. These results contain pure caffeine along with its metabolites. You’re interested in the “caffeine” hyperlink. When you click on this link, you will be taken to the record for caffeine. The record contains six tabs: Main, ChEBI Ontology, Automatic Xrefs, Reactions, Pathways, Models. Main This tab contains basic information, such as: definition, molecular weight, ChEBI ID (which is what you will be using to tag the BioModel), and a Wikipedia entry about the small molecule.
    [Show full text]