ArticleDatabase : the journal of biological databases and curation2022
DGPD: a knowledge database of dense granule proteins of the Apicomplexa.
Article in Database : the journal of biological databases and curation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- Timing of dense granule biogenesis in asexual malaria parasites.Microbiology (Reading, England) · 2023Article
- Development and validation of a machine learning algorithm prediction for dense granule proteins in Apicomplexa.Parasites & vectors · 2023Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Apicomplexan parasites cause severe diseases in human and livestock. Dense granule proteins (GRAs), specific to the Apicomplexa, participate in the maintenance of intracellular parasitism of host cells. GRAs have better immunogenicity and they can be emerged as important players in vaccine development. Although studies on GRAs have increased gradually in recent years, due to incompleteness and complexity of data collection, biologists have difficulty in the comprehensive utilization of information. Thus, there is a desperate need of user-friendly resource to integrate with existing GRAs. In this paper, we developed the Dense Granule Protein Database (DGPD), the first knowledge database dedicated to the integration and analysis of typical GRAs properties. The current version of DGPD includes annotated GRAs metadata of 245 samples derived from multiple web repositories and literature mining, involving five species that cause common diseases (Plasmodium falciparum, Toxoplasma gondii, Hammondia hammondi, Neospora caninum and Cystoisospora suis). We explored the baseline characteristics of GRAs and found that the number of introns and transmembrane domains in GRAs are markedly different from those of non-GRAs. Furthermore, we utilized the data in DGPD to explore the prediction algorithms for GRAs. We hope DGPD will be a good database for researchers to study GRAs. Database URL: http://dgpd.tlds.cc/DGPD/index/.
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Registered trials
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