Abstract
The Gene Ontology (GO) is widely recognised as the gold standard bioinformatics resource for summarizing functional knowledge of gene products in a consistent and computable, information-rich language. GO describes cellular and organismal processes across all species, yet until now there has been a considerable gene annotation deficit within the neurological and immunological domains, both of which are relevant to Parkinson’s disease. Here we introduce the Parkinson’s disease GO Annotation Project, funded by Parkinson’s UK and supported by the GO Consortium, which is addressing this deficit by providing GO annotation to Parkinson’s-relevant human gene products, principally through expert literature curation. We discuss the steps taken to prioritise proteins, publications and cellular processes for annotation, examples of how GO annotations capture Parkinson’s-relevant information, and the advantages that a topic-focused annotation approach offers to users. Building on the existing GO resource, this project collates a vast amount of Parkinson’s-relevant literature into a set of high-quality annotations to be utilized by the research community.
Highlights
Parkinson’s disease is the second most common neurodegenerative disorder after Alzheimer’s disease
One such fullyannotated protein is human PARK7 (DJ-1, Q99497), which has 226 annotations in total, 135 of which were created by our project (Table 1 provides a selection of these annotations, note UniProtKB/Swiss-Prot protein IDs are used throughout this article)
The role of a gene product can vary depending on its environment or interactors, and in a Gene Ontology (GO) Consortium-led approach (Huntley et al 2014), we extend the core GO annotation to accommodate additional context, such as the cell type the process occurs in, or the substrate of a catalytic activity
Summary
Parkinson’s disease is the second most common neurodegenerative disorder after Alzheimer’s disease. An increased understanding of Parkinson’s disease has resulted from the identification and characterization of genes that cause or influence the risk of developing this condition (Gasser et al 2011). Much of this knowledge has accumulated from ‘big data’ analyses, such as genome wide association (GWA) studies, large-scale sequencing, proteomics and transcriptomic analyses. GO is widely recognised as the gold standard bioinformatics resource for summarizing functional knowledge of gene products across all kingdoms of life.
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