Publications by authors named "Alexander D Diehl"

28 Publications

  • Page 1 of 1

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project.

Nat Rev Nephrol 2020 11 16;16(11):686-696. Epub 2020 Sep 16.

Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype. The development of two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.
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http://dx.doi.org/10.1038/s41581-020-00335-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012202PMC
November 2020

Reporting and connecting cell type names and gating definitions through ontologies.

BMC Bioinformatics 2019 Apr 25;20(Suppl 5):182. Epub 2019 Apr 25.

Division for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.

Background: Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. 'T cells'), and the description of the marker pattern utilized (e.g. CD14-, CD3+).

Results: We here describe our attempts to use ontologies to cross-compare cell types and marker patterns (also referred to as gating definitions). We used a large set of such gating definitions and corresponding cell types submitted by different investigators into ImmPort, a central database for immunology studies, to examine the ability to parse gating definitions using terms from the Protein Ontology (PRO) and cell type descriptions, using the Cell Ontology (CL). We then used logical axioms from CL to detect discrepancies between the two.

Conclusions: We suggest adoption of our proposed format for describing gating and cell type definitions to make comparisons easier. We also suggest a number of new terms to describe gating definitions in flow cytometry that are not based on molecular markers captured in PRO, but on forward- and side-scatter of light during data acquisition, which is more appropriate to capture in the Ontology for Biomedical Investigations (OBI). Finally, our approach results in suggestions on what logical axioms and new cell types could be considered for addition to the Cell Ontology.
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http://dx.doi.org/10.1186/s12859-019-2725-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509839PMC
April 2019

OSCI: standardized stem cell ontology representation and use cases for stem cell investigation.

BMC Bioinformatics 2019 Apr 25;20(Suppl 5):180. Epub 2019 Apr 25.

Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.

Background: Stem cells and stem cell lines are widely used in biomedical research. The Cell Ontology (CL) and Cell Line Ontology (CLO) are two community-based OBO Foundry ontologies in the domains of in vivo cells and in vitro cell line cells, respectively.

Results: To support standardized stem cell investigations, we have developed an Ontology for Stem Cell Investigations (OSCI). OSCI imports stem cell and cell line terms from CL and CLO, and investigation-related terms from existing ontologies. A novel focus of OSCI is its application in representing metadata types associated with various stem cell investigations. We also applied OSCI to systematically categorize experimental variables in an induced pluripotent stem cell line cell study related to bipolar disorder. In addition, we used a semi-automated literature mining approach to identify over 200 stem cell gene markers. The relations between these genes and stem cells are modeled and represented in OSCI.

Conclusions: OSCI standardizes stem cells found in vivo and in vitro and in various stem cell investigation processes and entities. The presented use cases demonstrate the utility of OSCI in iPSC studies and literature mining related to bipolar disorder.
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http://dx.doi.org/10.1186/s12859-019-2723-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509805PMC
April 2019

Cells in ExperimentaL Life Sciences (CELLS-2018): capturing the knowledge of normal and diseased cells with ontologies.

BMC Bioinformatics 2019 Apr 25;20(Suppl 5):183. Epub 2019 Apr 25.

Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.

Cell cultures and cell lines are widely used in life science experiments. In conjunction with the 2018 International Conference on Biomedical Ontology (ICBO-2018), the 2nd International Workshop on Cells in ExperimentaL Life Science (CELLS-2018) focused on two themes of knowledge representation, for newly-discovered cell types and for cells in disease states. This workshop included five oral presentations and a general discussion session. Two new ontologies, including the Cancer Cell Ontology (CCL) and the Ontology for Stem Cell Investigations (OSCI), were reported in the workshop. In another representation, the Cell Line Ontology (CLO) framework was applied and extended to represent cell line cells used in China and their Chinese representation. Other presentations included a report on the application of ontologies to cross-compare cell types and marker patterns used in flow cytometry studies, and a presentation on new experimental findings about novel cell types based on single cell RNA sequencing assay and their corresponding ontological representation. The general discussion session focused on the ontology design patterns in representing newly-discovered cell types and cells in disease states.
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http://dx.doi.org/10.1186/s12859-019-2721-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509796PMC
April 2019

An ontology for representing hematologic malignancies: the cancer cell ontology.

BMC Bioinformatics 2019 Apr 25;20(Suppl 5):181. Epub 2019 Apr 25.

Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.

Background: Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data.

Results: We have developed the Cancer Cell Ontology according to OBO Foundry principles as an extension of the Cell Ontology. We define classes in Cancer Cell Ontology by using a genus-differentia approach using logical axioms capturing the expression of cellular surface markers in order to represent types of hematologic malignancies. By adopting conventions used in the Cell Ontology, we have created human and computer-readable definitions for 300 classes of blood cancers, based on the EGIL classification system for leukemias, and relying upon additional classification approaches for multiple myelomas and other hematologic malignancies.

Conclusion: We have demonstrated a proof of concept for leveraging the built-in logical axioms of the ontology in order to classify patient surface marker data into appropriate diagnostic categories. We plan to integrate our ontology into existing tools for flow cytometry data analysis to facilitate the automated diagnosis of hematologic malignancies.
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http://dx.doi.org/10.1186/s12859-019-2722-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509834PMC
April 2019

Cell type discovery using single-cell transcriptomics: implications for ontological representation.

Hum Mol Genet 2018 05;27(R1):R40-R47

J. Craig Venter Institute, La Jolla, CA 92037, USA.

Cells are fundamental function units of multicellular organisms, with different cell types playing distinct physiological roles in the body. The recent advent of single-cell transcriptional profiling using RNA sequencing is producing 'big data', enabling the identification of novel human cell types at an unprecedented rate. In this review, we summarize recent work characterizing cell types in the human central nervous and immune systems using single-cell and single-nuclei RNA sequencing, and discuss the implications that these discoveries are having on the representation of cell types in the reference Cell Ontology (CL). We propose a method, based on random forest machine learning, for identifying sets of necessary and sufficient marker genes, which can be used to assemble consistent and reproducible cell type definitions for incorporation into the CL. The representation of defined cell type classes and their relationships in the CL using this strategy will make the cell type classes being identified by high-throughput/high-content technologies findable, accessible, interoperable and reusable (FAIR), allowing the CL to serve as a reference knowledgebase of information about the role that distinct cellular phenotypes play in human health and disease.
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http://dx.doi.org/10.1093/hmg/ddy100DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5946857PMC
May 2018

Cells in experimental life sciences - challenges and solution to the rapid evolution of knowledge.

BMC Bioinformatics 2017 12 21;18(Suppl 17):560. Epub 2017 Dec 21.

Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.

Cell cultures used in biomedical experiments come in the form of both sample biopsy primary cells, and maintainable immortalised cell lineages. The rise of bioinformatics and high-throughput technologies has led us to the requirement of ontology representation of cell types and cell lines. The Cell Ontology (CL) and Cell Line Ontology (CLO) have long been established as reference ontologies in the OBO framework. We have compiled a series of the challenges and the proposals of solutions in this CELLS (Cells in ExperimentaL Life Sciences) thematic series that cover the grounds of standing issues and the directions, which were discussed in the First International Workshop on CELLS at the the International Conference on Biomedical Ontology (ICBO). This workshop focused on the extension of the current CL and CLO to cover a wider set of biological questions and challenges needing semantic infrastructure for information modeling. We discussed data-driven use cases that leverage linkage of CL, CLO and other bio-ontologies. This is an established approach in data-driven ontologies such as the Experimental Factor Ontology (EFO), and the Ontology for Biomedical Investigation (OBI). The First International Workshop on CELLS at the International Conference on Biomedical Ontology has brought together experimental biologists and biomedical ontologists to discuss solutions to organizing and representing the rapidly evolving knowledge gained from experimental cells. The workshop has successfully identified the areas of challenge, and the gap in connecting the two domains of knowledge. The outcome of this workshop yielded practical implementation plans to filled in this gap.This CELLS workshop also provided a venue for panel discussions of innovative solutions as well as challenges in the development and applications of biomedical ontologies to represent and analyze experimental cell data.
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http://dx.doi.org/10.1186/s12859-017-1976-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763506PMC
December 2017

Ontological Realism for the Research Domain Criteria for Mental Disorders.

Stud Health Technol Inform 2017 ;235:431-435

Department of Biomedical Informatics, University at Buffalo, Buffalo NY, USA.

At the heart of the Research Domain Criteria for Mental Disorders is a matrix in which functional aspects of behavior are related to genotypic and (endo-)phenotypic research findings, and the various techniques through which they can been observed. The matrix is work in progress. As such it currently suffers from several shortcomings, the resolution of which, we contend, are essential to success of NIMH's goal of fostering translational science on mental disorders. Using well-established criteria for assessing the terminological and ontological quality of biomedical representations we identified the major problems to be (1) the abundant presence of terms that lack face value, (2) the absence of what the exact nature of the represented relationships are, and (3) referential imprecision with respect to the intended granularity of what the terms denote. We propose to eliminate these shortcomings by resorting to definitions and formal representations under the umbrella of Ontological Realism as they already have been developed in the areas of mental health, anatomy and biological functions.
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April 2018

Protein Ontology (PRO): enhancing and scaling up the representation of protein entities.

Nucleic Acids Res 2017 01 28;45(D1):D339-D346. Epub 2016 Nov 28.

Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA.

The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translational modification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities.
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http://dx.doi.org/10.1093/nar/gkw1075DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210558PMC
January 2017

The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability.

J Biomed Semantics 2016 07 4;7(1):44. Epub 2016 Jul 4.

Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.

Background: The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies.

Construction And Content: Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning.

Utility And Discussion: The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs.

Conclusions: The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.
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http://dx.doi.org/10.1186/s13326-016-0088-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4932724PMC
July 2016

Representing vision and blindness.

J Biomed Semantics 2016 30;7:15. Epub 2016 Mar 30.

New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, Buffalo, NY USA ; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA.

Background: There have been relatively few attempts to represent vision or blindness ontologically. This is unsurprising as the related phenomena of sight and blindness are difficult to represent ontologically for a variety of reasons. Blindness has escaped ontological capture at least in part because: blindness or the employment of the term 'blindness' seems to vary from context to context, blindness can present in a myriad of types and degrees, and there is no precedent for representing complex phenomena such as blindness.

Methods: We explore current attempts to represent vision or blindness, and show how these attempts fail at representing subtypes of blindness (viz., color blindness, flash blindness, and inattentional blindness). We examine the results found through a review of current attempts and identify where they have failed.

Results: By analyzing our test cases of different types of blindness along with the strengths and weaknesses of previous attempts, we have identified the general features of blindness and vision. We propose an ontological solution to represent vision and blindness, which capitalizes on resources afforded to one who utilizes the Basic Formal Ontology as an upper-level ontology.

Conclusions: The solution we propose here involves specifying the trigger conditions of a disposition as well as the processes that realize that disposition. Once these are specified we can characterize vision as a function that is realized by certain (in this case) biological processes under a range of triggering conditions. When the range of conditions under which the processes can be realized are reduced beyond a certain threshold, we are able to say that blindness is present. We characterize vision as a function that is realized as a seeing process and blindness as a reduction in the conditions under which the sight function is realized. This solution is desirable because it leverages current features of a major upper-level ontology, accurately captures the phenomenon of blindness, and can be implemented in many domain-specific ontologies.
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http://dx.doi.org/10.1186/s13326-016-0058-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4815270PMC
October 2016

Gateways to the FANTOM5 promoter level mammalian expression atlas.

Genome Biol 2015 Jan 5;16:22. Epub 2015 Jan 5.

The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). This resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas.
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http://dx.doi.org/10.1186/s13059-014-0560-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4310165PMC
January 2015

Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.

Science 2015 Feb 12;347(6225):1010-4. Epub 2015 Feb 12.

Although it is generally accepted that cellular differentiation requires changes to transcriptional networks, dynamic regulation of promoters and enhancers at specific sets of genes has not been previously studied en masse. Exploiting the fact that active promoters and enhancers are transcribed, we simultaneously measured their activity in 19 human and 14 mouse time courses covering a wide range of cell types and biological stimuli. Enhancer RNAs, then messenger RNAs encoding transcription factors, dominated the earliest responses. Binding sites for key lineage transcription factors were simultaneously overrepresented in enhancers and promoters active in each cellular system. Our data support a highly generalizable model in which enhancer transcription is the earliest event in successive waves of transcriptional change during cellular differentiation or activation.
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http://dx.doi.org/10.1126/science.1259418DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681433PMC
February 2015

flowCL: ontology-based cell population labelling in flow cytometry.

Bioinformatics 2015 Apr 6;31(8):1337-9. Epub 2014 Dec 6.

Molecular Biology and Biochemistry Department, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY 14203, USA, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA, Center for Human Immunology, Autoimmunity and Inflammation, National Institutes of Health, Bethesda, MD 20892, USA, School of Dental Medicine, University at Buffalo, NY 14214-8006, USA, J. Craig Venter Institute, La Jolla, CA 92037, USA, Department of Pathology, University of California, San Diego, CA 92093, USA.

Motivation: Finding one or more cell populations of interest, such as those correlating to a specific disease, is critical when analysing flow cytometry data. However, labelling of cell populations is not well defined, making it difficult to integrate the output of algorithms to external knowledge sources.

Results: We developed flowCL, a software package that performs semantic labelling of cell populations based on their surface markers and applied it to labelling of the Federation of Clinical Immunology Societies Human Immunology Project Consortium lyoplate populations as a use case.

Conclusion: By providing automated labelling of cell populations based on their immunophenotype, flowCL allows for unambiguous and reproducible identification of standardized cell types.

Availability And Implementation: Code, R script and documentation are available under the Artistic 2.0 license through Bioconductor (http://www.bioconductor.org/packages/devel/bioc/html/flowCL.html).

Contact: [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btu807DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393520PMC
April 2015

A promoter-level mammalian expression atlas.

Nature 2014 Mar;507(7493):462-70

Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal. Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body. We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles. TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved. Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs. The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses. The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research.
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http://dx.doi.org/10.1038/nature13182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4529748PMC
March 2014

CLO: The cell line ontology.

J Biomed Semantics 2014 13;5:37. Epub 2014 Aug 13.

University of Michigan, Ann Arbor, MI, USA.

Background: Cell lines have been widely used in biomedical research. The community-based Cell Line Ontology (CLO) is a member of the OBO Foundry library that covers the domain of cell lines. Since its publication two years ago, significant updates have been made, including new groups joining the CLO consortium, new cell line cells, upper level alignment with the Cell Ontology (CL) and the Ontology for Biomedical Investigation, and logical extensions.

Construction And Content: Collaboration among the CLO, CL, and OBI has established consensus definitions of cell line-specific terms such as 'cell line', 'cell line cell', 'cell line culturing', and 'mortal' vs. 'immortal cell line cell'. A cell line is a genetically stable cultured cell population that contains individual cell line cells. The hierarchical structure of the CLO is built based on the hierarchy of the in vivo cell types defined in CL and tissue types (from which cell line cells are derived) defined in the UBERON cross-species anatomy ontology. The new hierarchical structure makes it easier to browse, query, and perform automated classification. We have recently added classes representing more than 2,000 cell line cells from the RIKEN BRC Cell Bank to CLO. Overall, the CLO now contains ~38,000 classes of specific cell line cells derived from over 200 in vivo cell types from various organisms.

Utility And Discussion: The CLO has been applied to different biomedical research studies. Example case studies include annotation and analysis of EBI ArrayExpress data, bioassays, and host-vaccine/pathogen interaction. CLO's utility goes beyond a catalogue of cell line types. The alignment of the CLO with related ontologies combined with the use of ontological reasoners will support sophisticated inferencing to advance translational informatics development.
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http://dx.doi.org/10.1186/2041-1480-5-37DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4387853PMC
April 2015

The neurological disease ontology.

J Biomed Semantics 2013 Dec 6;4(1):42. Epub 2013 Dec 6.

Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 701 Ellicott Street, Buffalo, NY 14203, USA.

Background: We are developing the Neurological Disease Ontology (ND) to provide a framework to enable representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated with the treatment and study of neurological diseases. ND is being developed in compliance with the Open Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General Medical Science (OGMS) for the representation of entities in the domain of disease and medical practice. Initial applications of ND will include the annotation and analysis of large data sets and patient records for Alzheimer's disease, multiple sclerosis, and stroke.

Description: ND is implemented in OWL 2 and currently has more than 450 terms that refer to and describe various aspects of neurological diseases. ND directly imports the development version of OGMS, which uses BFO 2. Term development in ND has primarily extended the OGMS terms 'disease', 'diagnosis', 'disease course', and 'disorder'. We have imported and utilize over 700 classes from related ontology efforts including the Foundational Model of Anatomy, Ontology for Biomedical Investigations, and Protein Ontology. ND terms are annotated with ontology metadata such as a label (term name), term editors, textual definition, definition source, curation status, and alternative terms (synonyms). Many terms have logical definitions in addition to these annotations. Current development has focused on the establishment of the upper-level structure of the ND hierarchy, as well as on the representation of Alzheimer's disease, multiple sclerosis, and stroke. The ontology is available as a version-controlled file at http://code.google.com/p/neurological-disease-ontology along with a discussion list and an issue tracker.

Conclusion: ND seeks to provide a formal foundation for the representation of clinical and research data pertaining to neurological diseases. ND will enable its users to connect data in a robust way with related data that is annotated using other terminologies and ontologies in the biomedical domain.
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http://dx.doi.org/10.1186/2041-1480-4-42DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4028878PMC
December 2013

Protein Ontology: a controlled structured network of protein entities.

Nucleic Acids Res 2014 Jan 21;42(Database issue):D415-21. Epub 2013 Nov 21.

Protein Information Resource, Georgetown University Medical Center, WA 20007, USA, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA, Department of Bioinformatics and Computational Biology, The Jackson Laboratory, Bar Harbor, ME 04609, USA, Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, NY 10016, USA, Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY 14203, USA, Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, Buffalo, NY 14203, USA, Department of Immunology, Duke University Medical Center, Durham, NC 27705, USA and Department of Oral Diagnostic Sciences, University at Buffalo School of Dental Medicine, Buffalo, NY 14214, USA.

The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to UniProtKB in that PRO's organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO's representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments.
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http://dx.doi.org/10.1093/nar/gkt1173DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3964965PMC
January 2014

Ontology based molecular signatures for immune cell types via gene expression analysis.

BMC Bioinformatics 2013 Aug 30;14:263. Epub 2013 Aug 30.

Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA.

Background: New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an 'Ontologically BAsed Molecular Signature' (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type's identity.

Results: We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types.

Conclusions: This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis - providing a new method for defining novel biomarkers and providing an opportunity for new biological insights.
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http://dx.doi.org/10.1186/1471-2105-14-263DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3844401PMC
August 2013

A unified anatomy ontology of the vertebrate skeletal system.

PLoS One 2012 10;7(12):e51070. Epub 2012 Dec 10.

Department of Biology, University of South Dakota, Vermillion, SD, USA.

The skeleton is of fundamental importance in research in comparative vertebrate morphology, paleontology, biomechanics, developmental biology, and systematics. Motivated by research questions that require computational access to and comparative reasoning across the diverse skeletal phenotypes of vertebrates, we developed a module of anatomical concepts for the skeletal system, the Vertebrate Skeletal Anatomy Ontology (VSAO), to accommodate and unify the existing skeletal terminologies for the species-specific (mouse, the frog Xenopus, zebrafish) and multispecies (teleost, amphibian) vertebrate anatomy ontologies. Previous differences between these terminologies prevented even simple queries across databases pertaining to vertebrate morphology. This module of upper-level and specific skeletal terms currently includes 223 defined terms and 179 synonyms that integrate skeletal cells, tissues, biological processes, organs (skeletal elements such as bones and cartilages), and subdivisions of the skeletal system. The VSAO is designed to integrate with other ontologies, including the Common Anatomy Reference Ontology (CARO), Gene Ontology (GO), Uberon, and Cell Ontology (CL), and it is freely available to the community to be updated with additional terms required for research. Its structure accommodates anatomical variation among vertebrate species in development, structure, and composition. Annotation of diverse vertebrate phenotypes with this ontology will enable novel inquiries across the full spectrum of phenotypic diversity.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0051070PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3519498PMC
June 2013

How the gene ontology evolves.

BMC Bioinformatics 2011 Aug 5;12:325. Epub 2011 Aug 5.

ESRC Centre for Genomics in Society, University of Exeter, EX4 4PJ Exeter, UK.

Background: Maintaining a bio-ontology in the long term requires improving and updating its contents so that it adequately captures what is known about biological phenomena. This paper illustrates how these processes are carried out, by studying the ways in which curators at the Gene Ontology have hitherto incorporated new knowledge into their resource.

Results: Five types of circumstances are singled out as warranting changes in the ontology: (1) the emergence of anomalies within GO; (2) the extension of the scope of GO; (3) divergence in how terminology is used across user communities; (4) new discoveries that change the meaning of the terms used and their relations to each other; and (5) the extension of the range of relations used to link entities or processes described by GO terms.

Conclusion: This study illustrates the difficulties involved in applying general standards to the development of a specific ontology. Ontology curation aims to produce a faithful representation of knowledge domains as they keep developing, which requires the translation of general guidelines into specific representations of reality and an understanding of how scientific knowledge is produced and constantly updated. In this context, it is important that trained curators with technical expertise in the scientific field(s) in question are involved in supervising ontology shifts and identifying inaccuracies.
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http://dx.doi.org/10.1186/1471-2105-12-325DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166943PMC
August 2011

Logical development of the cell ontology.

BMC Bioinformatics 2011 Jan 5;12. Epub 2011 Jan 5.

Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA.

Background: The Cell Ontology (CL) is an ontology for the representation of in vivo cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL.

Results: Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types.

Conclusion: Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.
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http://dx.doi.org/10.1186/1471-2105-12-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3024222PMC
January 2011

Hematopoietic cell types: prototype for a revised cell ontology.

J Biomed Inform 2011 Feb 1;44(1):75-9. Epub 2010 Feb 1.

The Jackson Laboratory, Bar Harbor, ME 04609, USA.

The Cell Ontology (CL) aims for the representation of in vivo and in vitro cell types from all of biology. The CL is a candidate reference ontology of the OBO Foundry and requires extensive revision to bring it up to current standards for biomedical ontologies, both in its structure and its coverage of various subfields of biology. We have now addressed the specific content of one area of the CL, the section of the ontology dealing with hematopoietic cells. This section has been extensively revised to improve its content and eliminate multiple inheritance in the asserted hierarchy, and the groundwork has been laid for structuring the hematopoietic cell type terms as cross-products incorporating logical definitions built from relationships to external ontologies, such as the Protein Ontology and the Gene Ontology. The methods and improvements to the CL in this area represent a paradigm for improvement of the entire ontology over time.
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http://dx.doi.org/10.1016/j.jbi.2010.01.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2892030PMC
February 2011

Novel sequence feature variant type analysis of the HLA genetic association in systemic sclerosis.

Hum Mol Genet 2010 Feb 18;19(4):707-19. Epub 2009 Nov 18.

Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX 75390-8884, USA.

We describe a novel approach to genetic association analyses with proteins sub-divided into biologically relevant smaller sequence features (SFs), and their variant types (VTs). SFVT analyses are particularly informative for study of highly polymorphic proteins such as the human leukocyte antigen (HLA), given the nature of its genetic variation: the high level of polymorphism, the pattern of amino acid variability, and that most HLA variation occurs at functionally important sites, as well as its known role in organ transplant rejection, autoimmune disease development and response to infection. Further, combinations of variable amino acid sites shared by several HLA alleles (shared epitopes) are most likely better descriptors of the actual causative genetic variants. In a cohort of systemic sclerosis patients/controls, SFVT analysis shows that a combination of SFs implicating specific amino acid residues in peptide binding pockets 4 and 7 of HLA-DRB1 explains much of the molecular determinant of risk.
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http://dx.doi.org/10.1093/hmg/ddp521DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2807365PMC
February 2010

An improved ontological representation of dendritic cells as a paradigm for all cell types.

BMC Bioinformatics 2009 Feb 25;10:70. Epub 2009 Feb 25.

Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.

Background: Recent increases in the volume and diversity of life science data and information and an increasing emphasis on data sharing and interoperability have resulted in the creation of a large number of biological ontologies, including the Cell Ontology (CL), designed to provide a standardized representation of cell types for data annotation. Ontologies have been shown to have significant benefits for computational analyses of large data sets and for automated reasoning applications, leading to organized attempts to improve the structure and formal rigor of ontologies to better support computation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL's utility for computation and for cross-species data integration.

Results: To enhance the CL's utility for computational analyses, we developed a method for the ontological representation of cells and applied this method to develop a dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. We avoid multiple uses of is_a by linking DC-CL terms to terms in other ontologies via additional, formally defined relations such as has_function.

Conclusion: This approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. Accordingly, we propose our method as a general strategy for the ontological representation of cells. DC-CL is available from http://www.obofoundry.org.
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http://dx.doi.org/10.1186/1471-2105-10-70DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2662812PMC
February 2009

Muscle Research and Gene Ontology: New standards for improved data integration.

BMC Med Genomics 2009 Jan 29;2. Epub 2009 Jan 29.

CRIBI- Interdepartmental Biotechnology Center, University of Padua, Padua, Italy.

Background: The Gene Ontology Project provides structured controlled vocabularies for molecular biology that can be used for the functional annotation of genes and gene products. In a collaboration between the Gene Ontology (GO) Consortium and the muscle biology community, we have made large-scale additions to the GO biological process and cellular component ontologies. The main focus of this ontology development work concerns skeletal muscle, with specific consideration given to the processes of muscle contraction, plasticity, development, and regeneration, and to the sarcomere and membrane-delimited compartments. Our aims were to update the existing structure to reflect current knowledge, and to resolve, in an accommodating manner, the ambiguity in the language used by the community.

Results: The updated muscle terminologies have been incorporated into the GO. There are now 159 new terms covering critical research areas, and 57 existing terms have been improved and reorganized to follow their usage in muscle literature.

Conclusion: The revised GO structure should improve the interpretation of data from high-throughput (e.g. microarray and proteomic) experiments in the area of muscle science and muscle disease. We actively encourage community feedback on, and gene product annotation with these new terms. Please visit the Muscle Community Annotation Wiki http://wiki.geneontology.org/index.php/Muscle_Biology.
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http://dx.doi.org/10.1186/1755-8794-2-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657163PMC
January 2009

Access to immunology through the Gene Ontology.

Immunology 2008 Oct;125(2):154-60

Department of Medicine, University College London, Rayne Institute, London, UK.

The Gene Ontology (GO) is widely recognized as the premier tool for the organization and functional annotation of molecular aspects of cellular systems. However, for many immunologists the use of GO is a very foreign concept. Indeed, as a controlled vocabulary, GO can almost be considered a new language, and it can be difficult to appreciate the use and value of this approach for understanding the immune system. This review reflects on the application of GO to the field of immunology and explains the process of GO annotation. Finally, this review hopes to inspire immunologists to invest time and energy in improving both the content of the GO and the quality of GO annotations associated with genes of immunological interest.
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http://dx.doi.org/10.1111/j.1365-2567.2008.02940.xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2561138PMC
October 2008

Ontology development for biological systems: immunology.

Bioinformatics 2007 Apr 31;23(7):913-5. Epub 2007 Jan 31.

Mouse Genome Informatics, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04605, USA.

Unlabelled: We recently implemented improvements to the representation of immunology content of the biological process branch of the Gene Ontology (GO). The aims of the revision were to provide a comprehensive representation of immunological processes and to improve the organization of immunology related terms in the GO to match current concepts in the field of immunology. With these improvements, the GO will better reflect current understanding in the field of immunology and thus prove to be a more valuable resource for knowledge representation in gene annotation and analysis in the areas of immunology related to genomics and bioinformatics.

Availability: http://www.geneontology.org.
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http://dx.doi.org/10.1093/bioinformatics/btm029DOI Listing
April 2007
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