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Article ; Online: Obol

Christopher J. Mungall

International Journal of Genomics, Vol 5, Iss 6-7, Pp 509-

Integrating Language and Meaning in Bio-Ontologies

2006 Volume 520

Abstract: Ontologies are intended to capture and formalize a domain of knowledge. The ontologies comprising the Open Biological Ontologies (OBO) project, which includes the Gene Ontology (GO), are formalizations of various domains of biological knowledge. ... ...

Abstract	Ontologies are intended to capture and formalize a domain of knowledge. The ontologies comprising the Open Biological Ontologies (OBO) project, which includes the Gene Ontology (GO), are formalizations of various domains of biological knowledge. Ontologies within OBO typically lack computable definitions that serve to differentiate a term from other similar terms. The computer is unable to determine the meaning of a term, which presents problems for tools such as automated reasoners. Reasoners can be of enormous benefit in managing a complex ontology. OBO term names frequently implicitly encode the kind of definitions that can be used by computational tools, such as automated reasoners. The definitions encoded in the names are not easily amenable to computation, because the names are ostensibly natural language phrases designed for human users. These names are highly regular in their grammar, and can thus be treated as valid sentences in some formal or computable language.With a description of the rules underlying this formal language, term names can be parsed to derive computable definitions, which can then be reasoned over. This paper describes the effort to elucidate that language, called Obol, and the attempts to reason over the resulting definitions. The current implementation finds unique non-trivial definitions for around half of the terms in the GO, and has been used to find 223 missing relationships, which have since been added to the ontology. Obol has utility as an ontology maintenance tool, and as a means of generating computable definitions for a whole ontology.
Keywords	Genetics ; QH426-470 ; Biology (General) ; QH301-705.5 ; Science ; Q ; DOAJ:Genetics ; DOAJ:Biology ; DOAJ:Biology and Life Sciences
Subject code	004
Language	English
Publishing date	2006-04-01T00:00:00Z
Publisher	Hindawi Publishing Corporation
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Knowledge Beacons

Lance M Hannestad / Vlado Dančík / Meera Godden / Imelda W Suen / Kenneth C Huellas-Bruskiewicz / Benjamin M Good / Christopher J Mungall / Richard M Bruskiewich

PLoS ONE, Vol 16, Iss 3, p e

Web services for data harvesting of distributed biomedical knowledge.

2021 Volume 0231916

Abstract: Availability The API and associated software is open source and currently available for access at https://github.com/NCATS-Tangerine/translator-knowledge-beacon. ...

Abstract	Availability The API and associated software is open source and currently available for access at https://github.com/NCATS-Tangerine/translator-knowledge-beacon.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: ROBOT

Rebecca C. Jackson / James P. Balhoff / Eric Douglass / Nomi L. Harris / Christopher J. Mungall / James A. Overton

BMC Bioinformatics, Vol 20, Iss 1, Pp 1-

A Tool for Automating Ontology Workflows

2019 Volume 10

Abstract: Abstract Background Ontologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and ... ...

Abstract	Abstract Background Ontologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and application-specific subsets, generating standard reports, and generating release files in multiple formats. Similar to more general software development, automation is the key to executing and managing these tasks effectively and to releasing more robust products in standard forms. For ontologies using the Web Ontology Language (OWL), the OWL API Java library is the foundation for a range of software tools, including the Protégé ontology editor. In the Open Biological and Biomedical Ontologies (OBO) community, we recognized the need to package a wide range of low-level OWL API functionality into a library of common higher-level operations and to make those operations available as a command-line tool. Results ROBOT (a recursive acronym for “ROBOT is an OBO Tool”) is an open source library and command-line tool for automating ontology development tasks. The library can be called from any programming language that runs on the Java Virtual Machine (JVM). Most usage is through the command-line tool, which runs on macOS, Linux, and Windows. ROBOT provides ontology processing commands for a variety of tasks, including commands for converting formats, running a reasoner, creating import modules, running reports, and various other tasks. These commands can be combined into larger workflows using a separate task execution system such as GNU Make, and workflows can be automatically executed within continuous integration systems. Conclusions ROBOT supports automation of a wide range of ontology development tasks, focusing on OBO conventions. It packages common high-level ontology development functionality into a convenient library, and makes it easy to configure, combine, and execute individual tasks in comprehensive, automated workflows. This helps ontology developers to ...
Keywords	Ontology development ; Automation ; Ontology release ; Reasoning ; Workflows ; Quality control ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2019-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: SvAnna

Daniel Danis / Julius O. B. Jacobsen / Parithi Balachandran / Qihui Zhu / Feyza Yilmaz / Justin Reese / Matthias Haimel / Gholson J. Lyon / Ingo Helbig / Christopher J. Mungall / Christine R. Beck / Charles Lee / Damian Smedley / Peter N. Robinson

Genome Medicine, Vol 14, Iss 1, Pp 1-

efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing

2022 Volume 13

Abstract: Abstract Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches ... ...

Abstract	Abstract Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory sequences, relating predicted effects on gene function with clinical phenotype data. SvAnna places 87% of deleterious SVs in the top ten ranks. The interpretable prioritizations offered by SvAnna will facilitate the widespread adoption of long-read sequencing in diagnostic genomics. SvAnna is available at https://github.com/TheJacksonLaboratory/SvAnn a .
Keywords	Long-read sequencing ; Whole genome sequencing ; Structural variant ; Medicine ; R ; Genetics ; QH426-470
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Brain Data Standards - A method for building data-driven cell-type ontologies

Scientific Data, Vol 10, Iss 1, Pp 1-

2023 Volume 11

Abstract: Abstract Large-scale single-cell ‘omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we ... ...

Abstract	Abstract Large-scale single-cell ‘omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.
Keywords	Science ; Q
Subject code	004
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Phenopacket-tools

Daniel Danis / Julius O B Jacobsen / Alex H Wagner / Tudor Groza / Martha A Beckwith / Lauren Rekerle / Leigh C Carmody / Justin Reese / Harshad Hegde / Markus S Ladewig / Berthold Seitz / Monica Munoz-Torres / Nomi L Harris / Jordi Rambla / Michael Baudis / Christopher J Mungall / Melissa A Haendel / Peter N Robinson

PLoS ONE, Vol 18, Iss 5, p e

Building and validating GA4GH Phenopackets.

2023 Volume 0285433

Abstract: The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that ... ...

Abstract	The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.
Keywords	Medicine ; R ; Science ; Q
Subject code	020
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Obol

Christopher J. Mungall

Comparative and Functional Genomics, Vol 5, Iss 6-7, Pp 509-

Integrating Language and Meaning in Bio-Ontologies

2004 Volume 520

Abstract	Ontologies are intended to capture and formalize a domain of knowledge. The ontologies comprising the Open Biological Ontologies (OBO) project, which includes the Gene Ontology (GO), are formalizations of various domains of biological knowledge. Ontologies within OBO typically lack computable definitions that serve to differentiate a term from other similar terms. The computer is unable to determine the meaning of a term, which presents problems for tools such as automated reasoners. Reasoners can be of enormous benefit in managing a complex ontology. OBO term names frequently implicitly encode the kind of definitions that can be used by computational tools, such as automated reasoners. The definitions encoded in the names are not easily amenable to computation, because the names are ostensibly natural language phrases designed for human users. These names are highly regular in their grammar, and can thus be treated as valid sentences in some formal or computable language.With a description of the rules underlying this formal language, term names can be parsed to derive computable definitions, which can then be reasoned over. This paper describes the effort to elucidate that language, called Obol, and the attempts to reason over the resulting definitions. The current implementation finds unique non-trivial definitions for around half of the terms in the GO, and has been used to find 223 missing relationships, which have since been added to the ontology. Obol has utility as an ontology maintenance tool, and as a means of generating computable definitions for a whole ontology.
Keywords	Science ; Q ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
Subject code	004
Language	English
Publishing date	2004-01-01T00:00:00Z
Publisher	Hindawi Publishing Corporation
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Transforming the study of organisms

Anne E Thessen / Ramona L Walls / Lars Vogt / Jessica Singer / Robert Warren / Pier Luigi Buttigieg / James P Balhoff / Christopher J Mungall / Deborah L McGuinness / Brian J Stucky / Matthew J Yoder / Melissa A Haendel

PLoS Computational Biology, Vol 16, Iss 11, p e

Phenomic data models and knowledge bases.

2020 Volume 1008376

Abstract: The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by ...

Abstract	The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.
Keywords	Biology (General) ; QH301-705.5
Subject code	310
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Term Matrix

Valerie Wood / Seth Carbon / Midori A. Harris / Antonia Lock / Stacia R. Engel / David P. Hill / Kimberly Van Auken / Helen Attrill / Marc Feuermann / Pascale Gaudet / Ruth C. Lovering / Sylvain Poux / Kim M. Rutherford / Christopher J. Mungall

Open Biology, Vol 10, Iss

a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

2020 Volume 9

Abstract: Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, ... ...

Abstract	Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.
Keywords	gene ontology ; quality control ; annotation ; biocuration ; Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2020-09-01T00:00:00Z
Publisher	The Royal Society
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale

Bin Hu / Shane Canon / Emiley A. Eloe-Fadrosh / Anubhav / Michal Babinski / Yuri Corilo / Karen Davenport / William D. Duncan / Kjiersten Fagnan / Mark Flynn / Brian Foster / David Hays / Marcel Huntemann / Elais K. Player Jackson / Julia Kelliher / Po-E. Li / Chien-Chi Lo / Douglas Mans / Lee Ann McCue /

Nigel Mouncey / Christopher J. Mungall / Paul D. Piehowski / Samuel O. Purvine / Montana Smith / Neha Jacob Varghese / Donald Winston / Yan Xu / Patrick S. G. Chain

Frontiers in Bioinformatics, Vol

2022 Volume 1

Abstract: The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools ... ...

Abstract	The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.
Keywords	microbiome ; microbial ecology ; omics ; bioinformatics ; infrastructure ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	020
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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