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  1. Article ; Online: Enrichment on steps, not genes, improves inference of differentially expressed pathways.

    Markarian, Nicholas / Van Auken, Kimberly M / Ebert, Dustin / Sternberg, Paul W

    PLoS computational biology

    2024  Volume 20, Issue 3, Page(s) e1011968

    Abstract: Enrichment analysis is frequently used in combination with differential expression data to investigate potential commonalities amongst lists of genes and generate hypotheses for further experiments. However, current enrichment analysis approaches on ... ...

    Abstract Enrichment analysis is frequently used in combination with differential expression data to investigate potential commonalities amongst lists of genes and generate hypotheses for further experiments. However, current enrichment analysis approaches on pathways ignore the functional relationships between genes in a pathway, particularly OR logic that occurs when a set of proteins can each individually perform the same step in a pathway. As a result, these approaches miss pathways with large or multiple sets because of an inflation of pathway size (when measured as the total gene count) relative to the number of steps. We address this problem by enriching on step-enabling entities in pathways. We treat sets of protein-coding genes as single entities, and we also weight sets to account for the number of genes in them using the multivariate Fisher's noncentral hypergeometric distribution. We then show three examples of pathways that are recovered with this method and find that the results have significant proportions of pathways not found in gene list enrichment analysis.
    MeSH term(s) Gene Expression Profiling/methods
    Language English
    Publishing date 2024-03-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1011968
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Correction: Semantic representation of neural circuit knowledge in Caenorhabditis elegans.

    Prakash, Sharan J / Van Auken, Kimberly M / Hill, David P / Sternberg, Paul W

    Brain informatics

    2024  Volume 11, Issue 1, Page(s) 13

    Language English
    Publishing date 2024-05-15
    Publishing country Germany
    Document type Published Erratum
    ISSN 2198-4018
    ISSN 2198-4018
    DOI 10.1186/s40708-024-00226-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways.

    Hill, David P / Drabkin, Harold J / Smith, Cynthia L / Van Auken, Kimberly M / D'Eustachio, Peter

    Genetics

    2023  Volume 225, Issue 2

    Abstract: Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a ... ...

    Abstract Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.
    MeSH term(s) Mice ; Humans ; Animals ; Databases, Genetic ; Gene Ontology ; Mutation ; Phenotype ; Computational Biology/methods
    Language English
    Publishing date 2023-08-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2167-2
    ISSN 1943-2631 ; 0016-6731
    ISSN (online) 1943-2631
    ISSN 0016-6731
    DOI 10.1093/genetics/iyad152
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Semantic Representation of Neural Circuit Knowledge in

    Prakash, Sharan J / Van Auken, Kimberly M / Hill, David P / Sternberg, Paul W

    bioRxiv : the preprint server for biology

    2023  

    Abstract: In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that ... ...

    Abstract In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology Causal Activity Modelling, or GO-CAM). In this study, we explored whether the GO-CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode
    Language English
    Publishing date 2023-09-26
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.04.28.538760
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Biochemical Pathways Represented by Gene Ontology Causal Activity Models Identify Distinct Phenotypes Resulting from Mutations in Pathways.

    Hill, David P / Drabkin, Harold J / Smith, Cynthia L / Van Auken, Kimberly M / D'Eustachio, Peter

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a ... ...

    Abstract Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase, and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a connected and well-defined way. To test whether individual genes from well-defined pathways result in similar and distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of two related but distinct pathways, gluconeogenesis and glycolysis, we can identify causal paths in gene networks that give rise to discrete phenotypic outcomes for perturbations of glycolysis and gluconeogenesis. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.
    Language English
    Publishing date 2023-07-13
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.05.22.541760
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Semantic representation of neural circuit knowledge in Caenorhabditis elegans.

    Prakash, Sharan J / Van Auken, Kimberly M / Hill, David P / Sternberg, Paul W

    Brain informatics

    2023  Volume 10, Issue 1, Page(s) 30

    Abstract: In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that ... ...

    Abstract In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology-Causal Activity Modelling, or GO-CAM). In this study, we explored whether the GO-CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans [C. elegans Neural-Circuit Causal Activity Modelling (CeN-CAM)]. We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO
    Language English
    Publishing date 2023-11-10
    Publishing country Germany
    Document type Journal Article
    ISSN 2198-4018
    ISSN 2198-4018
    DOI 10.1186/s40708-023-00208-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Annotation of gene product function from high-throughput studies using the Gene Ontology.

    Attrill, Helen / Gaudet, Pascale / Huntley, Rachael P / Lovering, Ruth C / Engel, Stacia R / Poux, Sylvain / Van Auken, Kimberly M / Georghiou, George / Chibucos, Marcus C / Berardini, Tanya Z / Wood, Valerie / Drabkin, Harold / Fey, Petra / Garmiri, Penelope / Harris, Midori A / Sawford, Tony / Reiser, Leonore / Tauber, Rebecca / Toro, Sabrina

    Database : the journal of biological databases and curation

    2019  Volume 2019

    Abstract: High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The ... ...

    Abstract High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.
    MeSH term(s) Animals ; Databases, Genetic ; Gene Ontology ; Genomics/methods ; High-Throughput Nucleotide Sequencing ; Humans ; Molecular Sequence Annotation/methods ; Sequence Analysis, DNA
    Language English
    Publishing date 2019-01-01
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2496706-3
    ISSN 1758-0463 ; 1758-0463
    ISSN (online) 1758-0463
    ISSN 1758-0463
    DOI 10.1093/database/baz007
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Representing ontogeny through ontology: a developmental biologist's guide to the gene ontology.

    Hill, David P / Berardini, Tanya Z / Howe, Douglas G / Van Auken, Kimberly M

    Molecular reproduction and development

    2009  Volume 77, Issue 4, Page(s) 314–329

    Abstract: Developmental biology, like many other areas of biology, has undergone a dramatic shift in the perspective from which developmental processes are viewed. Instead of focusing on the actions of a handful of genes or functional RNAs, we now consider the ... ...

    Abstract Developmental biology, like many other areas of biology, has undergone a dramatic shift in the perspective from which developmental processes are viewed. Instead of focusing on the actions of a handful of genes or functional RNAs, we now consider the interactions of large functional gene networks and study how these complex systems orchestrate the unfolding of an organism, from gametes to adult. Developmental biologists are beginning to realize that understanding ontogeny on this scale requires the utilization of computational methods to capture, store and represent the knowledge we have about the underlying processes. Here we review the use of the Gene Ontology (GO) to study developmental biology. We describe the organization and structure of the GO and illustrate some of the ways we use it to capture the current understanding of many common developmental processes. We also discuss ways in which gene product annotations using the GO have been used to ask and answer developmental questions in a variety of model developmental systems. We provide suggestions as to how the GO might be used in more powerful ways to address questions about development. Our goal is to provide developmental biologists with enough background about the GO that they can begin to think about how they might use the ontology efficiently and in the most powerful ways possible.
    MeSH term(s) Animals ; Cell Differentiation ; Computational Biology/methods ; Database Management Systems ; Databases, Genetic ; Developmental Biology/methods ; Morphogenesis ; Software ; Terminology as Topic ; Vocabulary, Controlled
    Language English
    Publishing date 2009-11-18
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Review
    ZDB-ID 20321-x
    ISSN 1098-2795 ; 1040-452X
    ISSN (online) 1098-2795
    ISSN 1040-452X
    DOI 10.1002/mrd.21130
    Database MEDical Literature Analysis and Retrieval System OnLINE

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