12 results on '"Van Auken, Kimberly M."'
Search Results
2. Semantic representation of neural circuit knowledge in Caenorhabditis elegans
- Author
-
Prakash, Sharan J., Van Auken, Kimberly M., Hill, David P., and Sternberg, Paul W.
- Published
- 2023
- Full Text
- View/download PDF
3. Enrichment on steps, not genes, improves inference of differentially expressed pathways.
- Author
-
Markarian, Nicholas, Van Auken, Kimberly M., Ebert, Dustin, and Sternberg, Paul W.
- 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. Author summary: Genome-scale experiments typically identify sets of genes which are primarily analyzed by enrichment analysis to identify relevant pathways that may be perturbed. Curated pathway models have rich structure that we believe can be exploited to get better results. Some pathway steps are enabled by sets of interchangeable genes which inflate the gene count of their respective pathways relative to the number of steps. We improve sensitivity towards these pathways in enrichment analysis by performing enrichment on steps. We then use this approach to identify pathways that would otherwise be missed in medically relevant datasets to gain new insights. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways
- Author
-
Hill, David P, primary, Drabkin, Harold J, additional, Smith, Cynthia L, additional, Van Auken, Kimberly M, additional, and D’Eustachio, Peter, additional
- Published
- 2023
- Full Text
- View/download PDF
5. Identification of Biochemical Pathways Responsible for Distinct Phenotypes Using Gene Ontology Causal Activity Models
- Author
-
Hill, David P., Drabkin, Harold J., Smith, Cynthia L., Van Auken, Kimberly M., D’Eustachio, Peter, Hill, David P., Drabkin, Harold J., Smith, Cynthia L., Van Auken, Kimberly M., and D’Eustachio, Peter
- 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.SummaryGenes act in interconnected biological pathways, so single mutations can yield diverse phenotypes. To use the large body of mouse functional gene annotations, we converted human Gene Ontology-Causal Activity Models (GO-CAMs) of glucose metabolism to orthologo
- Published
- 2023
6. Annotation of gene product function from high-throughput studies using the Gene Ontology
- Author
-
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, and Toro, Sabrina
- 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.
- Published
- 2019
7. Representing Ontogeny Through Ontology: A Developmental Biologist’s Guide to The Gene Ontology
- Author
-
Hill, David P., Berardini, Tanya Z., Howe, Douglas G., and Van Auken, Kimberly M.
- 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.
- Published
- 2010
8. Representing ontogeny through ontology: A developmental biologist's guide to the gene ontology
- Author
-
Hill, David P., primary, Berardini, Tanya Z., additional, Howe, Douglas G., additional, and Van Auken, Kimberly M., additional
- Published
- 2009
- Full Text
- View/download PDF
9. Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans .
- Author
-
Prakash SJ, Van Auken KM, Hill DP, and Sternberg PW
- 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 ( Ce N-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
2 ) avoidance behaviors could be faithfully represented with Ce N-CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge., Competing Interests: Competing Interests The authors declare that they have no competing interests- Published
- 2023
- Full Text
- View/download PDF
10. Biochemical Pathways Represented by Gene Ontology Causal Activity Models Identify Distinct Phenotypes Resulting from Mutations in Pathways.
- Author
-
Hill DP, Drabkin HJ, Smith CL, Van Auken KM, and D'Eustachio P
- 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.
- Published
- 2023
- Full Text
- View/download PDF
11. Annotation of gene product function from high-throughput studies using the Gene Ontology.
- Author
-
Attrill H, Gaudet P, Huntley RP, Lovering RC, Engel SR, Poux S, Van Auken KM, Georghiou G, Chibucos MC, Berardini TZ, Wood V, Drabkin H, Fey P, Garmiri P, Harris MA, Sawford T, Reiser L, Tauber R, and Toro S
- Subjects
- Animals, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA, Databases, Genetic, Gene Ontology, Genomics methods, Molecular Sequence Annotation methods
- 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.
- Published
- 2019
- Full Text
- View/download PDF
12. Representing ontogeny through ontology: a developmental biologist's guide to the gene ontology.
- Author
-
Hill DP, Berardini TZ, Howe DG, and Van Auken KM
- Subjects
- Animals, Cell Differentiation, Database Management Systems, Terminology as Topic, Vocabulary, Controlled, Computational Biology methods, Databases, Genetic, Developmental Biology methods, Morphogenesis, Software
- 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., (Copyright 2009 Wiley-Liss, Inc.)
- Published
- 2010
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.