A database is a repository/warehouse of data where a collection of data or information is stored in an organized manner for rapid search and retrieval of stored information by the computer system. A simple database might be a single file containing many records, each of which includes the same set of information.
Likewise, a biological database is a large, organized repository of persistent data usually associated with software and computational tools designed to update, search for a query, and retrieve components of the data stored within the system.
The Need of Biological Databases
With the advancements in modern genomic research has resulted in the generation of vast amounts of raw sequence data. Hence, with the increasing amount of genomic data, it becomes essential to use sophisticated computational methodologies to store and manage the data deluge.
Thereby, the very first challenge in the era of genomics is to store and manage the bewildering amounts of information through establishment and use of computer databases. The main objective of developing such databases is to organize the data as structured records for easy retrieval of information from the stored records.
Types of Biological Databases
There are basically two types of biological databases:
1. Primary Databases
Also known as archival databases, are populated with the data derived from various experiments, e.g., nucleotide sequence, amino acid sequence or macromolecular structure. Researchers directly submit their experimental findings in primary databases which is usually archival in nature. Once the data has become a part of the scientific record, i.e., they’ve been given their own accession IDs in the primary databases, they are never changed. Examples include:
GenBank (NCBI) and DDBJ for nucleotide sequences.
Protein Data Bank (PDB) for macromolecular structures.
UniProt and PIR for protein sequences.
2. Secondary Databases
These databases are composed of data driven from the analysis of data stored in primary databases. Secondary databases often contain information from numerous sources including other databases (primary & secondary), the scientific literature, and controlled vocabularies. Secondary databases are usually highly curated and use complex combinations of computational algorithms and contain data driven from manual analysis and interpretation of experimental work. They store information such as conserved sequences, signature sequences and active site residue information. Examples include:
3. Organism-Specific Databases
The specialized biological databases that cater to particular research interest around a specific organism or species, are known as “organism-specific or species-specific databases”. Examples include:
FlyBase for Drosophila.
HIV sequence database for HIV.
TIAR for Arabidopsis.
4. However, some biological resources possess the characteristics of both the primary and secondary databases, e.g., UniProt. It accepts the primary sequence derived from peptide sequencing experiments. Even so, UniProt infers peptide sequence from genomic information, and also provides a lot of additional information as well, some derived from automated annotation of TrEMBL and more careful manual analysis from SwissProt.
Data is submitted directly into a biological database for indexing, data optimization and data organization. Biological databases help the researchers and the field professionals to find relevant biological data by making it available in computer-readable formats. By using the data mining tools and software, all the biological information is made readily accessible to save time and resources.
It allows knowledge discovery, which refers to the identification of connections between pieces of information that were not known when the information was first entered. This facilitates the discovery of new biological insights from raw data. Biological databases help to solve cases where many users want to access the same entries of data and also remove redundancy of data.
Applications of Biological Databases
Due to the high-performance computational platforms, these biological databases have become important in providing the infrastructure needed for biological research, from data preparation level to data extraction. The simulation of biological systems also requires computational platforms, which further highlights the need for biological databases.
For research purposes, bioinformatics tools and databases are highly efficient and rationalized for analyzing the growing amount of data generated from genomics, metabolomics, proteomics, and metagenomics. Since, a large number of biological databases are available, the need for integration, advancements, and improvements in bioinformatics is paramount.
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