4.1 Types of biological databases and their applications
4 min read•august 9, 2024
Biological databases are essential tools for storing and organizing vast amounts of biological information. They come in various types, including primary, secondary, sequence, structure, and , each serving specific research needs in molecular biology and genetics.
These databases play a crucial role in modern biological research, enabling scientists to access, analyze, and interpret complex data. From genomic sequences to and metabolic pathways, biological databases provide a foundation for advancing our understanding of life at the molecular level.
Types of Biological Databases
Primary and Secondary Databases
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Mapping proteomics data to UniProt, RefSeq and gene symbols View original
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Mapping proteomics data to UniProt, RefSeq and gene symbols View original
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Mapping proteomics data to UniProt, RefSeq and gene symbols View original
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Mapping proteomics data to UniProt, RefSeq and gene symbols View original
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Mapping proteomics data to UniProt, RefSeq and gene symbols View original
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Top images from around the web for Primary and Secondary Databases
Mapping proteomics data to UniProt, RefSeq and gene symbols View original
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Mapping proteomics data to UniProt, RefSeq and gene symbols View original
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Mapping proteomics data to UniProt, RefSeq and gene symbols View original
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Mapping proteomics data to UniProt, RefSeq and gene symbols View original
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contain experimentally derived data directly submitted by researchers
Include raw sequence data, structural information, and functional annotations
Examples include , EMBL, and DDBJ for
compile and analyze information from primary databases
Provide curated and value-added information
Often include computational analyses, predictions, and cross-references
Examples include RefSeq for curated gene sequences and UniProtKB/Swiss-Prot for manually annotated protein information
Sequence and Structure Databases
Sequence databases store and organize biological sequence information
Contain DNA, RNA, or protein sequences
Allow researchers to compare and analyze genetic material across species
Examples include GenBank (nucleotide sequences) and UniProtKB (protein sequences)
Structure databases focus on three-dimensional molecular structures
Store information about the spatial arrangement of atoms in biological molecules
Crucial for understanding protein function and drug design
Examples include (PDB) for macromolecular structures and Nucleic Acid Database (NaDB) for nucleic acid structures
Functional Databases
Functional databases provide information on biological roles and interactions
Contain data on gene expression, protein-protein interactions, and metabolic pathways
Help researchers understand the complex relationships between biological components
Examples include Gene Ontology (GO) for gene function annotations and KEGG for metabolic pathways
Specific Database Categories
Genomic Databases
Store and organize information related to genomes of various organisms
Include whole genome sequences, gene annotations, and genetic variations
Facilitate comparative genomics and evolutionary studies
Examples include Ensembl for vertebrate genomes and FlyBase for Drosophila genomics
Provide tools for genome browsing, , and variant analysis
Allow researchers to visualize genomic features and compare across species
Support identification of disease-associated genes and genetic markers
Proteomic Databases
Focus on protein-related information and analyses
Store protein sequences, structures, functions, and interactions
Support research in protein characterization and functional genomics
Examples include UniProtKB for comprehensive protein information and IntAct for protein interaction data
Offer tools for protein sequence analysis and structure prediction
Enable researchers to identify protein domains, motifs, and post-translational modifications
Facilitate studies on protein evolution and structure-function relationships
Metabolomic Databases
Contain information on metabolites and metabolic pathways
Store data on small molecules involved in cellular processes
Support research in metabolomics and systems biology
Examples include HMDB (Human Metabolome Database) for human metabolites and MetaCyc for metabolic pathways
Provide tools for metabolite identification and pathway analysis
Allow researchers to explore biochemical reactions and metabolic networks
Facilitate studies on cellular metabolism and metabolic disorders
Major Database Resources
National Center for Biotechnology Information (NCBI)
Comprehensive resource for molecular biology and genetics information
Hosts numerous databases covering various aspects of biological research
Provides tools for data analysis, visualization, and retrieval
Key databases within NCBI include:
GenBank for nucleotide sequences
PubMed for biomedical literature
BLAST for sequence similarity searches
Offers resources for researchers, clinicians, and educators
Supports genomic research, drug discovery, and personalized medicine
Provides educational materials and training resources
Universal Protein Resource (UniProt)
Central repository for protein sequence and functional information
Combines data from Swiss-Prot, TrEMBL, and PIR databases
Provides comprehensive and non-redundant protein information
Features of include:
UniProtKB for curated and automatically annotated protein entries
UniRef for clustered sets of sequences
UniParc for comprehensive archive of protein sequences
Offers tools for protein sequence analysis and classification
Supports proteomics research and functional genomics studies
Facilitates cross-referencing with other biological databases
Protein Data Bank (PDB)
Primary repository for three-dimensional structures of biological macromolecules
Contains experimentally determined structures of proteins, nucleic acids, and complexes
Crucial for structural biology and drug design research
Features of PDB include:
Atomic coordinates and related information for each structure
Tools for structure visualization and analysis
Links to related literature and functional annotations
Supports various fields of research:
Structural biology and protein folding studies
Structure-based drug design and rational protein engineering
Comparative structural genomics and evolutionary studies
Kyoto Encyclopedia of Genes and Genomes (KEGG)
Integrated database resource for understanding high-level functions of biological systems
Focuses on molecular interaction networks and biochemical pathways
Combines genomic, chemical, and systemic functional information
Key components of KEGG include:
KEGG PATHWAY for metabolic and signaling pathway maps
KEGG GENES for gene catalogs of sequenced genomes
KEGG LIGAND for information on chemical compounds and reactions
Supports systems biology and research:
Facilitates interpretation of large-scale molecular datasets
Enables pathway mapping and functional annotation of genes
Supports studies on metabolic engineering and drug target identification
Key Terms to Review (27)
Bioinformatics: Bioinformatics is the application of computational tools and techniques to analyze, interpret, and manage biological data, particularly in the fields of genomics, proteomics, and other omics sciences. It plays a crucial role in integrating large datasets from various biological sources to derive meaningful insights about complex biological systems and their functions.
Data integration: Data integration is the process of combining data from different sources to provide a unified view, enabling a more comprehensive understanding of biological systems. This approach is crucial for connecting various datasets, such as genomic, proteomic, and metabolic information, facilitating the analysis of complex biological interactions. It plays a key role in advancing research by allowing scientists to derive insights from diverse data types and improving decision-making in areas like drug discovery and systems biology.
Data Mining: Data mining is the process of discovering patterns, correlations, and insights from large sets of data using various analytical and statistical techniques. This process is crucial in transforming raw biological data into meaningful information, making it essential for analyzing complex biological systems, integrating diverse data sources, and advancing drug discovery efforts.
Functional databases: Functional databases are specialized biological databases that provide curated information on the functions of genes and proteins, often integrating data from multiple sources to enable researchers to understand the roles these molecules play in biological systems. These databases not only store sequence data but also include annotations related to molecular functions, biological processes, and cellular components, making them essential tools for systems biology research.
GenBank: GenBank is a comprehensive public database that stores and provides access to nucleotide sequences and their associated biological information. It serves as a crucial resource for researchers in genomics, molecular biology, and related fields, enabling them to retrieve genetic data for various organisms and study their relationships, functions, and evolution.
Gene expression analysis: Gene expression analysis is a method used to measure the activity or expression levels of genes within a cell or tissue. It provides insights into which genes are turned on or off under specific conditions, helping researchers understand cellular functions and responses. This analysis is crucial for studying biological processes, disease mechanisms, and the effects of treatments, as it links genetic information to phenotypic outcomes.
Genomic databases: Genomic databases are organized collections of biological information that specifically focus on the sequences and functional information of genomes. These databases play a crucial role in storing, retrieving, and analyzing genomic data from various organisms, enabling researchers to conduct comparative studies, identify genetic variations, and understand the underlying mechanisms of biological processes.
Kyoto Encyclopedia of Genes and Genomes: The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive database resource that provides information on biological systems, including genes, proteins, pathways, and diseases. KEGG serves as a vital tool for understanding the complex interactions within biological systems and is widely used in bioinformatics, systems biology, and genomics research to analyze biological data and infer functional relationships.
Metabolomic databases: Metabolomic databases are specialized repositories that store and provide access to data on small molecule metabolites present in biological samples. These databases facilitate the analysis and comparison of metabolic profiles across different organisms, conditions, and diseases, playing a critical role in understanding metabolic pathways and their functions.
National Center for Biotechnology Information: The National Center for Biotechnology Information (NCBI) is a key resource that provides access to a vast array of biological data, databases, and tools for researchers in the fields of genomics, molecular biology, and bioinformatics. It plays a crucial role in the storage and dissemination of genetic information, making it an essential hub for data integration and analysis in systems biology and related disciplines.
NoSQL: NoSQL refers to a category of database management systems that are designed to handle large volumes of unstructured or semi-structured data, allowing for more flexibility and scalability than traditional relational databases. These databases are often schema-less, meaning they don't require a fixed structure for data storage, which makes them particularly useful in biological databases that manage diverse and rapidly evolving datasets, such as genomic sequences and protein structures.
NoSQL databases: NoSQL databases are a category of database management systems that provide a mechanism for storage and retrieval of data that is modeled in ways other than the traditional tabular relations used in relational databases. They are particularly well-suited for handling large volumes of unstructured or semi-structured data, which makes them valuable for various applications in biological research, where data can be diverse and complex.
Nucleotide sequences: Nucleotide sequences are the ordered arrangement of nucleotides in a DNA or RNA molecule, which serves as the fundamental blueprint for genetic information and biological functions. These sequences are critical in various applications, including the analysis of genes and their functions, understanding evolutionary relationships, and the development of biotechnology tools. The precise arrangement of nucleotides determines the structure and function of proteins through processes like transcription and translation.
Phylogenetics: Phylogenetics is the study of the evolutionary relationships among biological entities, often species, based on their genetic information and physical characteristics. This field uses various methods, such as molecular data analysis and morphological comparisons, to construct evolutionary trees or phylogenies that depict how different organisms are related through common ancestry. Phylogenetics plays a crucial role in understanding biodiversity and the evolutionary history of life on Earth.
Primary databases: Primary databases are the foundational repositories that store raw data collected from biological research, including experimental results, genomic sequences, and protein structures. These databases serve as the first point of access for researchers seeking to analyze and interpret biological data, often providing unique and original datasets that are crucial for advancing scientific knowledge. They play a critical role in various applications, from drug discovery to evolutionary studies, by enabling users to perform queries and retrieve specific biological information.
Protein Data Bank: The Protein Data Bank (PDB) is a comprehensive online repository that collects, organizes, and disseminates three-dimensional structural data of biological macromolecules, particularly proteins and nucleic acids. This database serves as a crucial resource for researchers in fields like structural biology, bioinformatics, and molecular biology, allowing them to access detailed structural information that is essential for understanding molecular functions and interactions.
Protein Databases: Protein databases are organized collections of biological information specifically focused on protein sequences, structures, functions, and interactions. These databases serve as vital resources for researchers, enabling the analysis of protein-related data which is essential for understanding biological processes and disease mechanisms. They support various applications, including protein identification, functional annotation, and comparative analysis across different organisms.
Protein Structures: Protein structures refer to the unique three-dimensional shapes formed by proteins, which are essential for their function in biological processes. The structure of a protein is organized into four levels: primary, secondary, tertiary, and quaternary, each contributing to the protein's stability and functionality. Understanding these structures is crucial for exploring how proteins interact with other biomolecules and their role in various biological databases, which catalog and analyze protein information for research and applications.
Proteomic databases: Proteomic databases are specialized biological databases designed to store, manage, and provide access to data related to proteins, including their sequences, structures, functions, and interactions. These databases facilitate the analysis of protein expression profiles and the understanding of biological processes by enabling researchers to compare proteomic data across different organisms and conditions.
Relational databases: Relational databases are a type of database management system that stores data in structured formats using tables, where each table consists of rows and columns. This organization allows for efficient data retrieval and manipulation through relationships defined between different tables, making them particularly useful in managing complex biological data and enabling various applications in bioinformatics.
Secondary databases: Secondary databases are repositories that provide organized collections of biological data derived from primary sources, which include research articles and experimental results. These databases offer curated information that has been processed and analyzed, making it easier for researchers to access relevant data without going through original publications. They serve as vital resources for bioinformatics, allowing scientists to conduct analyses and draw conclusions based on a comprehensive aggregation of existing knowledge.
Sequence Alignment: Sequence alignment is a method used to arrange the sequences of DNA, RNA, or proteins to identify regions of similarity that may indicate functional, structural, or evolutionary relationships. This process is crucial in genomics and next-generation sequencing as it helps in comparing genetic information from different organisms or individuals. Sequence alignment also plays a vital role in biological databases, aiding in the classification and retrieval of biological data.
SQL: SQL, or Structured Query Language, is a standardized programming language specifically designed for managing and manipulating relational databases. It allows users to create, read, update, and delete data efficiently, making it a crucial tool for interacting with biological databases that store vast amounts of biological information. SQL's capabilities enable researchers to perform complex queries, extract meaningful insights, and manage data effectively in the context of various biological applications.
Standardization: Standardization refers to the process of establishing and implementing common protocols, guidelines, or measures to ensure consistency, reliability, and accuracy in various biological practices and data management. It is crucial in facilitating communication and collaboration across diverse fields, enabling researchers to compare results, share data effectively, and maintain quality control in experimental procedures and data analysis.
Systems genomics: Systems genomics is an interdisciplinary field that combines genomic data with systems biology approaches to understand the complex interactions between genes, proteins, and metabolic pathways in biological systems. It integrates high-throughput sequencing technologies and computational methods to analyze and interpret the vast amounts of data generated from genomic studies, ultimately aiming to provide insights into how genetic variation affects phenotypic traits and disease susceptibility.
UniProt: UniProt is a comprehensive protein sequence and functional information database that provides detailed annotations of proteins, including their roles, structures, and functions. It serves as a vital resource for researchers in genomics and proteomics, enabling the understanding of protein sequences and their biological implications.
Universal Protein Resource: The Universal Protein Resource (UniProt) is a comprehensive protein sequence and functional information database that provides a central hub for protein data. It integrates information from multiple sources, including literature and other biological databases, to offer a unified view of protein sequences, structures, functions, and interactions. This resource is essential for researchers in various fields, facilitating the understanding of proteins and their roles in biological processes.