What are the different types of biological databases used in bioinformatics?

There are several types of biological databases used in bioinformatics. Some of the most commonly used ones include:

1. Sequence Databases: These databases store nucleotide and protein sequences, such as GenBank, EMBL, and DDBJ. They provide a vast collection of genetic information from various organisms.

2. Structure Databases: These databases store three-dimensional structures of biomolecules, such as the Protein Data Bank (PDB). They provide information on the spatial arrangement of atoms in proteins, nucleic acids, and other macromolecules.

3. Genomic Databases: These databases store complete genomes or large portions of genomes, such as the National Center for Biotechnology Information (NCBI) Genome database. They provide information on the organization, function, and evolution of genes and genomes.

4. Expression Databases: These databases store gene expression data, such as the Gene Expression Omnibus (GEO) and ArrayExpress. They provide information on the levels of gene expression under different conditions or in different tissues.

5. Protein Interaction Databases: These databases store information on protein-protein interactions, such as the Biological General Repository for Interaction Datasets (BioGRID) and the Database of Interacting Proteins (DIP). They provide information on the physical interactions between proteins and their functional associations.

6. Metabolic Pathway Databases: These databases store information on metabolic pathways and their components, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Reactome database. They provide information on the biochemical reactions and pathways involved in cellular metabolism.

7. Disease Databases: These databases store information on genetic variations associated with diseases, such as the Online Mendelian Inheritance in Man (OMIM) and the Human Gene Mutation Database (HGMD). They provide information on the genetic basis of various diseases and their associated phenotypes.

These databases play a crucial role in bioinformatics research by providing a wealth of biological information that can be analyzed, integrated, and interpreted to gain insights into various biological processes and phenomena.