Key Components of Bioinformatics
May 17, 2023 Biological Databases
There are numerous varieties of biological databases that serve as valuable storage, organisation, and retrieval resources for biological data. Here are some prominent examples:
The National Centre for Biotechnology Information (NCBI) manages GenBank, a comprehensive public database. It is a database of nucleotide sequences, including DNA and RNA sequences from numerous organisms. In addition to sequence annotations, organism information, and references to scientific publications, GenBank contains associated metadata.
UniProt is an exhaustive database of protein sequences that provides a wealth of information regarding protein sequences, structures, functions, and annotations. It integrates information from multiple sources, such as Swiss-Prot, TrEMBL, and PIR, and provides a central repository for protein-related data.
PDB is a database that accumulates and maintains three-dimensional structural data of biological macromolecules, primarily proteins and nucleic acids. It contains structures determined experimentally using techniques such as X-ray crystallography and nuclear magnetic resonance (NMR). PDB provides researchers with access to structural data necessary for comprehending protein functions, interactions, and the design of drugs.
Gene Expression Omnibus (GEO) is an NCBI-managed public repository for high-throughput gene expression data, such as microarray and RNA-Seq datasets. It enables the investigation of gene expression patterns and the identification of differentially expressed genes by allowing researchers to deposit, access, and analyse gene expression profiles from various organisms and experimental conditions.
The Cancer Genome Atlas (TCGA): TCGA is a database that focuses on cancer genomics and provides detailed molecular profiles of numerous cancer types. It includes genomic, transcriptomic, epigenomic, and proteomic data from thousands of tumour samples, allowing researchers to examine cancer-related genetic alterations, biomarkers, and potential therapeutic targets.
KEGG is the Kyoto Encyclopaedia of Genes and Genomes: KEGG is a database that incorporates functional and biological pathway information. It provides an extensive assortment of molecular interaction networks, signalling pathways, metabolic pathways, and disease-related pathways. KEGG facilitates the comprehension of the functional context of genes, proteins, and small molecules, as well as their roles in diverse biological processes.
InterPro is a database that incorporates information from multiple resources regarding protein family, domain, and function. It employs predictive models and annotation techniques to classify protein sequences into families and to infer their functional domains and characteristics. InterPro is a valuable resource for understanding protein structure, function, and evolution.
Reactome is a knowledgebase of biological pathways and processes that has been curated. It details the molecular events, reactions, and interactions involved in a variety of biological processes, such as metabolism, signalling, and disease-related pathways. Reactome facilitates the analysis and interpretation of high-throughput data, thereby enhancing the comprehension of biological mechanisms.
These are only a few of the numerous biological databases available to scientists. Each database serves a distinct function and contributes to the comprehensive comprehension of biological data, thereby facilitating research in a variety of fields, including genomics, proteomics, pathway analysis, and functional annotation.
Importance of biological databases in storing and retrieving biological information
Biological databases play an essential role in the storage and retrieval of biological information, providing researchers, scientists, and professionals in various biological disciplines with valuable resources. Here are a few reasons that emphasise the significance of biological databases:
Data Centralization: Biological databases serve as centralised repositories, consolidating immense quantities of biological data in one easily accessible location. This centralization eliminates the need for researchers to search for and collect information from disparate sources, thereby sparing them time and effort. It provides researchers with centralised access to a vast array of data, including genetic sequences, protein structures, functional annotations, and experimental results.
Organisation and Standardisation of Data Biological databases use standardised data formats and annotations to ensure consistency and compatibility among various data types and sources. This standardisation and organisation facilitate data integration and comparison, allowing researchers to incorporate data from multiple sources and draw meaningful conclusions. Researchers can readily exchange and analyse data using standardised formats, which promotes data sharing and collaboration.
Effective Data Retrieval Biological databases provide robust search and retrieval capabilities, enabling researchers to rapidly locate pertinent data based on specific criteria. Researchers can search for genes, proteins, pathways, diseases, or particular biological characteristics and retrieve relevant data such as sequences, structures, annotations, and metadata. These effective retrieval mechanisms accelerate research and facilitate the investigation of diverse biological datasets.
Data Integration and Cross-References: Biological databases frequently combine data from multiple sources to provide a comprehensive view of biological data. They enable researchers to cross-reference and link diverse data types, including genetic sequences, protein structures, gene expression profiles, and functional annotations. This integration facilitates the identification of gene functions, protein-protein interactions, and disease mechanisms by enhancing our knowledge of the relationships between various biological entities.
Numerous biological databases provide tools for data visualisation and analysis, allowing researchers to investigate and interpret complex biological datasets. Data can be represented in graphical formats by data visualisation tools, facilitating the identification of patterns, trends, and correlations. Researchers can use analysis tools to conduct statistical analyses, compare datasets, and extract meaningful insights. These features enhance data exploration and facilitate the generation and testing of hypotheses.
Community Contributions and Curation: Biological databases frequently rely on community contributions and curation processes to ensure the quality and accuracy of their data. Researchers can submit their data, such as newly discovered genes, protein sequences, or experimental results, for inclusion in the databases and dissemination to the scientific community. The curation process entails the evaluation and validation of data by specialists, ensuring its dependability and usability.
Supporting Evidence-Based Research Biological databases serve as the basis for research and scientific investigations based on evidence. In databases, researchers can access previously published data and findings, thereby validating their hypotheses and expanding existing knowledge. This evidence-based methodology improves the rigour of research, promotes reproducibility, and facilitates the advancement of scientific knowledge in numerous biological fields.
