J

Judith A. Blake

Australian Institute of Tropical Health and Medicine

ORCID: 0000-0001-8522-334X

Publishes on Biomedical Text Mining and Ontologies, Bioinformatics and Genomic Networks, Genomics and Phylogenetic Studies. 247 papers and 77.9k citations.

247Publications
77.9kTotal Citations

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Top publicationsby citations

The Gene Ontology (GO) database and informatics resource
Midori A. Harris, J Clark, Amelia Ireland et al.|Nucleic Acids Research|2003
Cited by 4.7kOpen Access

The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

The Gene Ontology resource: enriching a GOld mine
Seth Carbon, Eric Douglass, Benjamin M. Good et al.|Nucleic Acids Research|2020
Cited by 3.8kOpen Access

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

The Gene Ontology knowledgebase in 2023
Cited by 2.7kOpen Access

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

Complete Genome Sequence of the Methanogenic Archaeon, <b> <i>Methanococcus jannaschii</i> </b>
Carol J. Bult, Owen White, Gary J. Olsen et al.|Science|1996
Cited by 2.1k

The complete 1.66-megabase pair genome sequence of an autotrophic archaeon, Methanococcus jannaschii, and its 58- and 16-kilobase pair extrachromosomal elements have been determined by whole-genome random sequencing. A total of 1738 predicted protein-coding genes were identified; however, only a minority of these (38 percent) could be assigned a putative cellular role with high confidence. Although the majority of genes related to energy production, cell division, and metabolism in M. jannaschii are most similar to those found in Bacteria, most of the genes involved in transcription, translation, and replication in M. jannaschii are more similar to those found in Eukaryotes.