Enhance Multi-Omics Research with DisGeNET

Introduction to DisGeNET: Your Multi-Omics Research Ally

Are you grappling with questions like which proteins are crucial in proteomics, or whether a candidate protein is truly significant? Are you searching for reliable protein databases? Look no further! Today, we're unveiling a game-changer: DisGeNET, your ultimate solution for enhancing background research and data mining efficiency.

Exploring the DisGeNET Database

DisGeNET stands as a discovery platform, housing one of the largest publicly available collections of genes and variants related to human diseases. It provides key metrics to assist in prioritizing genotype-phenotype relationships. The current version of DisGeNET (v7.0) encompasses 1,134,942 gene-disease associations (GDAs), spanning between 21,671 genes and 30,170 disease traits, clinical or abnormal human phenotypes, along with 369,554 variant-disease associations (VDAs) (spanning between 194,515 variants and 14,155 disease traits and phenotypes).

Classification of Proteins in the DisGeNET Database

In the DisGeNET database, over 80% of the genes encode proteins (nearly 14,000 genes), while the remaining genes are pseudogenes, ncRNAs, and other categories. Recent studies indicate that the number of protein-coding genes in the human genome is 19,000. Therefore, DisGeNET encompasses annotations for approximately 70% of human protein-coding genes associated with diseases. The largest protein class in DisGeNET is nucleic acid-binding proteins, accounting for 12% of all disease-associated proteins. The next best-performing categories are hydrolases, receptors, and transcription factors, each comprising 8%.

Annotation of GDAs in the DisGeNET Database

DisGeNET categorizes GDAs (Gene-Disease Associations) using the "DisGeNET Association Type Ontology". For example, the Biomarker category can be further subdivided into genetic mutations, expression changes, and post-translational modifications, among others, with annotations provided using DisGeNET scores. GDA scores effectively aid in sorting and selecting GDAs, assisting in the screening of target proteins/genes. These scores take into account the number of reported association sources, the type of each source, the inclusion of studies with animal models, and the quantity of supportive publications based on literature mining sources, with scores ranging from 0 to 1.

Functions and Applications of DisGeNET

DisGeNET integrates public repositories, GWAS catalogs, animal models, and scientific literature data, serving various research purposes. These include investigating the molecular basis of human diseases and their comorbidities, analyzing the characteristics of disease genes, generating hypotheses for drug therapeutic effects and side effects, calculating predictions for disease gene validation, and assessing the performance of text mining methods.

DisGeNET database facilitates background research and data mining for omics studies. MetwareBio offers innovative multi-omics research solutions. Feel free to contact us if needed!

Conclusion: Enhancing Multi-Omics Research with DisGeNET

In conclusion, DisGeNET stands as a powerful tool for multi-omics research, offering extensive data on gene-disease associations and protein-coding genes. By leveraging DisGeNET, researchers can enhance their background research, streamline data mining, and drive impactful scientific discoveries.

Reference

Janet Piñero, Núria Queralt-Rosinach, Àlex Bravo, Jordi Deu-Pons, Anna Bauer-Mehren, Martin Baron, Ferran Sanz, Laura I. Furlong, DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes, Database, Volume 2015, 2015, bav028, https://doi.org/10.1093/database/bav028

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