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ICBO 2018 Poster - Current Development in the Evidence and Conclusion Ontology (ECO)

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The Evidence & Conclusion Ontology (ECO) has been developed to provide standardized descriptions for types of evidence within the biological domain. Best
practices in biocuration require that when a biological assertion is made (e.g. linking a Gene Ontology (GO) term for a molecular function to a protein), the type of evidence
supporting it is captured. In recent development efforts, we have been working with other ontology groups to ensure that ECO classes exist for the types of curation they
support. These include the Ontology for Microbial Phenotypes and GO. In addition, we continue to support user-level class requests through our GitHub issue tracker. To
facilitate the addition and maintenance of new classes, we utilize ROBOT (a command line tool for working with Open Biomedical Ontologies) as part of our standard workflow.
ROBOT templates allow us to define classes in a spreadsheet and convert them to Web Ontology Language (OWL) axioms, which can then be merged into ECO. ROBOT is
also part of our automated release process. Additionally, we are engaged in ongoing work to map ECO classes to Ontology for Biomedical Investigation classes using logical
definitions. ECO is currently in use by dozens of groups engaged in biological curation and the number of ECO users continues to grow. The ontology, in OWL and Open
Biomedical Ontology (OBO) formats, and associated resources can be accessed through our GitHub site (https://github.com/evidenceontology/evidenceontology) as well as
the ECO web page (http://evidenceontology.org/).

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ICBO 2018 Poster - Current Development in the Evidence and Conclusion Ontology (ECO)

  1. 1. The Evidence and Conclusion Ontology systematically describes scientific evidence types in biological research. Biocurators, researchers, and data managers use evidence to support conclusions, such as an assertion that a protein has a particular function. ECO terms, as ontology classes, contain standard definitions and are networked with relationships. Associating research data with ECO evidence terms allows projects to manage large volumes of annotations, e.g. UniProt-Gene Ontology Annotation1 (UniProt-GOA) has >365 million evidence-linked GO annotations2. Recently, CollecTF3 has begun work on natural language processing training to automatically correspond text to ECO classes. Rebecca C. Tauber*, James B. Munro, Suvarna Nadendla, Marcus C. Chibucos, & Michelle Giglio University of Maryland School of Medicine *Contact: email - rebecca.tauber@som.umaryland.edu Abstract: The Evidence & Conclusion Ontology (ECO) has been developed to provide standardized descriptions for types of evidence within the biological domain. Best practices in biocuration require that when a biological assertion is made (e.g. linking a Gene Ontology (GO) term for a molecular function to a protein), the type of evidence supporting it is captured. In recent development efforts, we have been working with other ontology groups to ensure that ECO classes exist for the types of curation they support. These include the Ontology for Microbial Phenotypes and GO. In addition, we continue to support user-level class requests through our GitHub issue tracker. To facilitate the addition and maintenance of new classes, we utilize ROBOT (a command line tool for working with Open Biomedical Ontologies) as part of our standard workflow. ROBOT templates allow us to define classes in a spreadsheet and convert them to Web Ontology Language (OWL) axioms, which can then be merged into ECO. ROBOT is also part of our automated release process. Additionally, we are engaged in ongoing work to map ECO classes to Ontology for Biomedical Investigation classes using logical definitions. ECO is currently in use by dozens of groups engaged in biological curation and the number of ECO users continues to grow. The ontology, in OWL and Open Biomedical Ontology (OBO) formats, and associated resources can be accessed through our GitHub site (https://github.com/evidenceontology/evidenceontology) as well as the ECO web page (http://evidenceontology.org/). /evidenceontology Thank you to the OBI working group for collaboration on the ECO-OBI harmonization project. Thank you also to our various user groups for supporting the growth of ECO. Between January 2016 and July 2018, we added almost 800 new classes to ECO. Sources of New Classes: • The Gene Ontology Consortium5, for “High Throughput Experiment” GO Evidence Codes • The Ontology for Microbial Phenotypes6, to support phenotype annotations • CollecTF7, to validate transcription factor binding sites • Almost 100 GitHub tickets submitted during this time • And more! ECO developers use ROBOT3 to create new classes with the following process: 1. Create ROBOT template as CSV spreadsheet 2. Add details to the class(es) as cells in the template 3. Run the ROBOT template command to generate modules which are imported into eco-edit.owl Our monthly automated release also uses ROBOT to: 1. Run a report (as a series of SPARQL queries) to ensure changes are compliant 2. Generate modules, if the templates have changed 3. Extract and merge classes from OBI & GO 4. Run the ELK reasoner to assert all inferred relationships 5. Convert the edit file into more-verbose RDF/XML 6. Annotate the release file with the version date 7. Query to generate the ECO-GO mapping file 8. Create an eco-basic file without complex axioms 9. Create subsets by removing any classes not annotated with the specific subset property Background: We are actively working with the Ontology for Biomedical Investigations (OBI)4 to achieve the following goals: • Harmonize ECO and OBI, also increasing interoperability • Increase logic in ECO • Increase breadth of terminology in OBI • Reconcile ECO structure Evidence classes under ‘experimental evidence’ have been mapped to logical definitions made up of classes from OBI and GO. Recent Work: Hard-coding logical axioms into the main ontology can lead to inconsistencies due to human error, so all ECO-OBI axioms are now generated from a ROBOT template. This template generates a new module, which is then imported into the editor’s file and merged into the release file. This module contains 188 logical axioms as of July 2018. This material (the ontology & related resources) is based upon work supported by the National Science Foundation (NSF) Division of Biological Infrastructure (DBI) under Award Number 1458400 to MCC. Find us at http://evidenceontology.org/ 1. E.C. Dimmer, R.P. Huntley, Y. Alam-Faruque, T. Sawford, C. O'Donovan, M.J. Martinet, … R. Apweiler. (2012). The UniProt-GO Annotation database in 2011. Nucleic Acids Res., 40, D565–D570. 2. M.C. Chibucos, D.A. Siegele, J.C. Hu, M. Giglio (2017). The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations. Methods in Mol. Biol., 1446, 245-259. 3. ROBOT is an OBO tool. [Software]. Available from http://robot.obolibrary.org/ 4. A. Bandrowski, R. Brinkman, M. Brochhausen, M.H. Brush, B. Bug, M.C. Chibucos, … J. Zheng. (2016). The Ontology for Biomedical Investigations, PLoS One, 11(4):e0154556. 5. The Gene Ontology Consortium. (2015). Gene Ontology Consortium: going forward. Nucleic Acids Research, 43, D1049-D1056. 6. M.C. Chibuocos, A.E. Zweifel, J.C. Herrera, W. Meza, S. Eslamfam, P. Uetz, … M.G. Giglio. (2014). An ontology for microbial phenotypes. BMC Microbiology, 14, 294. 7. S. Kilic, E.R. White, D.M. Sagitova, J.P. Cornish, & I. Erill. (2014). CollecTF: A database of experimentally validated transcription factor-binding sites in bacteria. Nucleic Acids Res., 42, D156-D160. 800 900 1000 1100 1200 1300 1400 1500 1600 Collaboration with: • CollecTF, on natural language processing for paper annotation with ECO classes • The Human Disease Ontology, to incorporate classes representing definition sources • The Gene Ontology Synapse and Annotation Initiative, to include necessary classes for their annotations • The Ontology for Microbial Phenotypes, to expand classes for phenotype annotations • The Ontology for Biomedical Investigations, to complete the harmonization project Internal projects: • Increasing logic between ECO classes • Improving logical consistency by focusing on the information represented by the evidence, rather than the technology behind it

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