Introduction to bioinformatics (databases)

Table of Content:

• Aims of bioinformatics
• Biological databases
• Importance of biological database
• Primary Databases
• Secondary Databases
• Primary database types
• Secondary database types
• Some Examples of Primary Databases
• 1. GenBank
• 2. EMBL (European Molecular Biology Laboratory)
• 3. Swiss-Port
• 4. Protein Information Resource (PIR)
• Some Examples of Secondary Databases:
• 1. Motif Databases
• 2. Domain Databases
• 3. 3D structure databases
• 4. Gene Expression Databases
• 5.  Metabolic pathway databases
• 6. Genome Databases
• 7. Virological databases
• 8. World biodiversity databases

Aims of bioinformatics

• Development and use of database containing all biological information.
• Development and use of better tools for data designing, annotation and mining.
• Design and development of drugs by using simulation software.
• Design and development of software tools for protein structure prediction function, annotation and docking analysis.
• Creation and development of software to improve tools for analysing sequences for their function and similarity with other sequences
• Analyze vast data to understand cellular functions, growth, development, and disease.
• Use sequence or structural data to predict the role of new genes and proteins.
• Analyze protein structures to identify drug targets and simulate drug interactions.
• Analyze genomes to understand relationships, conserved genes, and life's history.
• Develop methods for storing and retrieving massive biological datasets.
• Analyze individual genetic makeup to predict disease risk and tailor treatments.

Biological databases

• Biological data are complex, vast, and incomplete. Therefore, several databases has been created and interpreted to ensure unambiguous results. 
• A collection of biological data arranged in computer readable form that enhances the speed of search and retrieval and convenient to use is called biological database. 
• A good database must have updated information.

Importance of biological database

A range of information can be retrieved by using biological databases like 

• biological sequences
• structures, binding sites
• metabolic interactions
• molecular action
• functional relationships
• protein families
• motifs. 

The main purpose of a biological database is to store and manage biological data and information in computer readable forms.

Primary Databases:

• Origin of Data: Store original, experimental data directly submitted by researchers. This data could be:
• DNA or protein sequences
• Macromolecular structures
• Microarray data
• Genotype data
• Examples:
• GenBank (DNA sequences)
• UniProtKB (protein sequences and annotations)
• PDB (Protein Data Bank) (3D structures of proteins and nucleic acids)
• GEO (Gene Expression Omnibus) (microarray data)
• dbSNP (Single Nucleotide Polymorphism database) (genotype data)
• Purpose: Act as an archive for raw experimental data, ensuring its accessibility and reproducibility for future research.
• Data Format: Structured and standardized format to facilitate easy search and retrieval.
• Data Annotation: Minimal annotations, focusing on data description and provenance (origin).

Secondary Databases:

• Origin of Data: Derive data from primary databases and integrate it with information from other sources. They perform various analyses and interpretations on the raw data.
• Examples:
• KEGG (Kyoto Encyclopedia of Genes and Genomes) (pathway maps and biological networks)
• SWISS-PROT (protein function, structure, and sequence information)
• Ensembl (annotated genomes)
• ViruSITE (viral genomics database)
• Purpose: Provide a higher level of analysis and interpretation of the data, offering insights into biological functions, relationships, and processes.
• Data Format: Structured and integrated with additional annotations and functionalities for searching, browsing, and analysis.
• Data Annotation: Rich annotations, including functional information, protein-protein interactions, and links to relevant publications.

Here's an analogy to illustrate the difference:

• Primary Database: Like a library's archive, where original research papers are stored.
• Secondary Database: Like a literature review article or textbook, summarizing and analyzing information from various primary sources.

Choosing the Right Database:

• Use Primary Databases: When you need access to the raw, uninterpreted experimental data for further analysis or verification.
• Use Secondary Databases: When you need a more comprehensive view of biological information, with interpretations, annotations, and functionalities for exploring relationships and functions.

Primary database types

Primary database types in bioinformatics encompass a wide range of resources that store biological data and provide tools for analysis and retrieval. Here are some of the main types:

1. Sequence Databases: These databases store nucleotide and protein sequences obtained from various organisms. Examples include the National Center for Biotechnology Information (NCBI) GenBank, European Molecular Biology Laboratory (EMBL) Nucleotide Sequence Database (ENA), and UniProt for protein sequences.
2. Structure Databases: These databases store three-dimensional structures of biological macromolecules, such as proteins, nucleic acids, and complexes. Examples include the Protein Data Bank (PDB) and the Electron Microscopy Data Bank (EMDB).
3. Genomic Databases: These databases contain genomic information, including assembled genomes, annotations, and genetic variation data. Examples include the Ensembl Genome Browser, UCSC Genome Browser, and the Genomic Variants Database (dbSNP).
4. Expression Databases: These databases store data on gene expression levels across different conditions, tissues, and developmental stages. Examples include the Gene Expression Omnibus (GEO), ArrayExpress, and the Cancer Genome Atlas (TCGA).
5. Protein Interaction Databases: These databases contain information on protein-protein interactions, protein complexes, and signaling pathways. Examples include the Biological General Repository for Interaction Datasets (BioGRID), STRING, and IntAct.
6. Metabolic Pathway Databases: These databases store information on metabolic pathways, including enzyme reactions, metabolites, and pathway maps. Examples include KEGG, Reactome, and MetaCyc.
7. Ontology Databases: These databases store controlled vocabularies and hierarchical relationships to standardize the annotation of biological data. Examples include the Gene Ontology (GO) database and the Human Disease Ontology (DO).
8. Literature Databases: These databases index scientific literature and provide tools for searching and accessing research articles relevant to specific topics. Examples include PubMed, PubMed Central (PMC), and Google Scholar.
9. Clinical Databases: These databases store clinical and healthcare data, including patient records, disease registries, and clinical trial information. Examples include ClinVar for clinical variants, ClinicalTrials.gov for clinical trials, and OMIM for human genetics and phenotypes.
10. Microarray and Next-Generation Sequencing (NGS) Databases: These databases store data generated from high-throughput technologies, such as microarray experiments and NGS studies. Examples include GEO for microarray data and the Sequence Read Archive (SRA) for NGS data.

These primary database types serve as valuable resources for researchers in bioinformatics and related fields, providing access to diverse biological data and facilitating scientific discovery and knowledge integration.

Secondary database types

Secondary databases in bioinformatics are resources that aggregate, curate, and annotate data derived from primary sources, often providing specialized or integrated analyses to facilitate research in specific areas. Here are some examples of secondary databases:

1. InterPro: Integrates protein sequences into families and predicts the presence of domains and important sites within them.
2. Swiss-Prot and TrEMBL: Annotated protein sequence databases that provide comprehensive information on protein function, structure, and interactions.
3. RefSeq: Curates and annotates a comprehensive collection of reference sequences for genomes, transcripts, and proteins from various organisms.
4. OMIM (Online Mendelian Inheritance in Man): Curates information on human genes and genetic disorders, including phenotypic descriptions, inheritance patterns, and molecular mechanisms.
5. UCSC Genome Browser: Provides a comprehensive collection of genome sequences and annotations for various organisms, along with tools for visualizing and analyzing genomic data.
6. ENSEMBL: Integrates genomic, transcriptomic, and proteomic data for various organisms, providing genome browsing, gene annotation, and comparative genomics tools.
7. Reactome: Curates and annotates human biological pathways, providing detailed information on molecular interactions, signaling cascades, and metabolic pathways.
8. dbGaP (database of Genotypes and Phenotypes): Archives and distributes genotype and phenotype data from human genetic studies, facilitating research on the genetic basis of complex traits and diseases.
9. Pfam: Curates a comprehensive collection of protein families and domains, providing annotations, alignments, and hidden Markov models for protein sequence analysis.
10. KEGG (Kyoto Encyclopedia of Genes and Genomes): Integrates genomic, chemical, and functional information for various organisms, including pathways, diseases, and drugs, to facilitate systems biology and drug discovery research.

These secondary databases play a critical role in bioinformatics research by organizing and annotating data from primary sources, providing valuable resources and tools for data analysis, interpretation, and hypothesis generation. They serve as essential components of the bioinformatics infrastructure, supporting a wide range of biological and biomedical studies.

Some Examples of Primary Databases:

1. GenBank:

• One of the fastest growing repositories of known nucleotide sequences, GeneBank (Genetic Sequence Databank), has a flat file structure. It is an ASCII text file, readable by both humans and computers. Besides sequence data, GeneBank files contain information such as accession numbers and gene names, phylogenetic classification and references to published literature.
• While GenBank's data was originally stored in a flat file format, it has since transitioned to a more complex structure using XML and ASN.1 formats for improved manageability and data exchange.
• This database has been developed and maintained at the NCBI, Bethesda, MD, USA, as a part of International Sequence Database Collaboration (INSDC).
• It is an open access sequence database.
• It coordinates with individual laboratories and other sequence databases like EMBL and DDBJ.
• It is an annotated collection of all nucleotide sequences that are available to the public.
• The nucleotide database was divided into three databases at NCBI: CoreNucleotide database, Expressed Sequence Tag (EST) and Genome Survey Sequence (GSS).
• CoreNucleotide database has most of the nucleotide sequences used. It also encloses all nucleotide records that are not in the EST and GSS databases.
• Submission of sequences to GeneBank can be done using BankIt, Sequin and tbl2asn tools.

2. EMBL (European Molecular Biology Laboratory):

• A comprehensive database of DNA and RNA sequences, EMBL nucleotide sequence database is collected from scientific literature, patient offices and is directly submitted by researchers. EMBL has been prepared in collaboration with GeneBank (USA) and the DNA Database of Japan (DDBJ).
• It is established in 1980.
• It is maintained by EBI (European Bioinformatics Institute)

3. Swiss-Port

• This is a curated protein sequence database that offers a high level of integration with other databases and also has a very low level of redundancy.
• Swiss-Port strives to provide protein sequences with a high level of annotation (for instance, the description of protein function, domain structure and post translational modifications, etc.).
• It is established in 1986 and maintained collaboratively , since 1987,  by  the  department  of  Medical  Biochemistry  of  the University of Geneva and the EMBL data Library.
• TrEMBL is a computer–annotated supplement of Swiss-Port that contains all translations of EMBL nucleotide sequence entries, which is not yet integrated in Swiss-Port.
• Currently Swiss-Port have 0.5 and TrEMBL have 7.6 milliom sequences.

4. Protein Information Resource (PIR)

• PIR is an integrated public bioinformatics resource to support genomic and proteomic research and scientific studies. Nowadays, PIR offers a wide variety of resources mainly oriented to assisting the propagation and consistency of protein annotations like PIRSF, ProClass and ProLINK.
• The current successor to PIR is UniProtKB, which combines information from Swiss-Prot (highly curated protein sequences), TrEMBL (computer-annotated sequences), and PIR's legacy data.

Some Examples of Secondary Databases:

1. Motif Databases:

• Secondary databases that focus on identifying and classifying short, recurring patterns of amino acids (motifs) within protein sequences.
• These motifs are often associated with specific protein functions or structural features.
• Motif databases offer valuable tools for predicting protein function, especially for uncharacterized proteins.

Examples of Motif Databases:

• PROSITE:
• Provides curated documentation entries describing protein domains, families, and functional sites.
• Includes associated patterns and profiles (signature sequences) to help identify these motifs in new protein sequences.
• PRINT:
• Focuses on protein fingerprints, which are groups of conserved motifs that uniquely characterize a protein family.
• By identifying these fingerprint motifs in a new protein sequence, researchers can gain insights into the protein's family and potential function.

2. Domain Databases:

• Secondary databases that focus on identifying and classifying protein domains within protein sequences.
• Protein domains are independently folded, structurally stable units often associated with specific functions or evolutionary relationships.

Examples of Domain Databases:

• ProDom:
• ProDom is an automatically generated protein domain database derived from the Swiss-Prot and TrEMBL sequence databases.
• It identifies domains based on sequence similarity searches.
• SMART:
• SMART (Simple Modular Architecture Research Tool) is a highly reliable and sensitive tool for domain identification.
• It uses a combination of hidden Markov models (HMMs) and profile searches to identify domains in protein sequences.
• COG (Clusters of Orthologous Groups):
• Primarily a functional classification tool - While COG can identify domains and motifs, it's primarily a functional classification tool.
• It groups proteins with likely orthologous relationships (genes from different species that evolved from a common ancestor) across various organisms.
• By identifying shared domains or motifs within COG groups, researchers can infer functional similarities.

3. 3D structure databases

1. Primary vs. Secondary Databases:

• PDB (Protein Data Bank): This is indeed a primary database. It stores the raw experimental data (3D atomic coordinates) for protein and nucleic acid structures determined by X-ray crystallography, NMR, and other methods.
• SCOP (Structural Classification of Proteins) & CATH (Class, Architecture, Topology, Homology): These are both secondary databases. They don't store the raw structural data but use the information from PDB to classify protein structures based on different hierarchical schemes.

Here's a breakdown of each database:

• PDB:
• Primary database for 3D macromolecular structures.
• Offers raw experimental data (atomic coordinates) and related information.
• SCOP:
• Secondary database for protein structure classification.
• Classifies structures based on hierarchy of structural and evolutionary relationships (folds, superfamilies, families).
• Uses PDB data for classification.
• CATH:
• Secondary database for protein domain structure classification.
• Classifies structures based on a hierarchy of Class, Architecture, Topology, and Homology.
• Uses PDB data for classification.

4. Gene Expression Databases:

• These databases store and organize data on gene expression levels in various biological samples.
• Focus: Understanding how genes are regulated and their roles in different cell types, tissues, and conditions.

Examples of Gene Expression Databases:

• GEO (Gene Expression Omnibus):
• A curated online repository for gene expression data from various high-throughput functional genomics experiments, including microarrays and sequencing data.
• Users can browse, query, and retrieve data for specific genes or experiments.
• GXD (Gene Expression Database):
• A resource specifically focused on gene expression information in the laboratory mouse.
• It integrates data from various sources and provides annotations to aid in understanding gene function and regulation during development and other biological processes.
• MGED (Microarray Gene Expression Data Society):
• While MGED itself is not a database, it functioned as a community resource that established standards and guidelines for data management and exchange related to microarray experiments.
• This standardization has facilitated the creation and use of gene expression databases like GEO.
• ArrayExpress (European Bioinformatics Institute):
• A repository for transcriptomics data, which refers to the study of an organism's entire RNA profile (including mRNA, tRNA, rRNA, etc.).
• ArrayExpress accepts data from various high-throughput sequencing technologies used to analyze gene expression.

5.  Metabolic pathway databases

• KEGG PATHWAY Database contains graphical pathway maps for all known metabolic pathways from various organisms. Or A comprehensive database with graphical maps for various metabolic pathways across organisms.
• EcoCyc is an E. coli specific type of metabolic pathway database, stores information and focuses on the genome, biochemical pathways, and enzymes of E. coli.
• LIGAND is a component of KEGG that stores information on compounds, drugs, reactions, enzymes, at the Institute for Chemical Research, Kyoto. It is composite database currently consisting of the COMPOUND, DRUG, GLYCAN, REACTION, RPAIR and ENZYME databases.
• MetaCyc: A well-curated database focused on experimentally verified metabolic pathways from all domains of life. It is a non-redundant, experimentally elucidated metabolic pathway database.
• BRENDA is an enzyme database tat contains information on all aspects of enzymes and enzymatic reactions. Provides comprehensive information on all aspects of enzymes and enzymatic reactions, including function, regulation, kinetics, and 3D structures.

6. Genome Databases:

• Store and organize information about the complete DNA sequence (genome) of various organisms.
• Benefits: Offer insights into genes, their functions, and an organism's heritable traits.
• Links to Organism Databases: Some genome databases provide links to specific organism databases that offer more detailed information about genes, proteins, and phenotypes within that organism.

Examples of Genome Databases:

• GOLD (Genomes Online Database):
• A comprehensive listing of complete and ongoing genome sequencing projects worldwide.
• Provides information on the organism, sequencing method, and links to relevant data resources.
• Genomes at NCBI (National Center for Biotechnology Information):
• A resource within the NCBI website that offers access to complete genome sequences and related annotations for various organisms.
• Users can search for specific genomes, download data, and explore gene features.

7. Virological databases

• A virological database contains all the sequences and related information of viruses of animals, plants, bacteria, fungi and archaea; 
• for example, the HIV protease database. 
• A committee called The International Committee on Taxonomy of Viruses (ICTV) authorises and organises the taxonomic classification of viruses. ICTVdB contains taxonomic information for over thousands of virus species.
• ViPR (Virus Pathogen Resource), Virus-Host Database, and NCBI Viral Genomes.

8. World biodiversity databases

• Taxonomic databases are essential tools for documenting and classifying all known species.
• They act as a global repository of information on biodiversity, providing details like:
• Taxonomic hierarchies (classification of organisms into categories like kingdom, phylum, class, etc.)
• Species names (scientific and potentially common names)
• Synonyms (alternative names for a species)
• Descriptions (morphological and other characteristics)
• Illustrations (images or diagrams of the species)
• References (links to scientific literature about the species)

World Biodiversity Databases:

• While CCINFO, STRAIN, and ALGAE are relevant resources, they are not necessarily "world biodiversity databases" in the broadest sense. These seem to be more specific databases focused on:
• CCINFO: Potentially a database related to cultivated or commercially important plant species (needs further research for confirmation).
• STRAIN: Likely a database focused on microbial strains (needs further confirmation).
• ALGAE: This could be a database specifically for information on algae species.

Examples of World Biodiversity Databases:

• Here are some examples of databases that encompass a broader range of biodiversity:
• Global Biodiversity Information Facility (GBIF): A global network and data infrastructure that provides open access to information on all types of life on Earth.
• SpeciesLink: A portal providing access to a network of species databases across various taxonomic groups.
• Catalogue of Life: An integrated online species catalogue that aims to list all known species of animals, plants, fungi, and microorganisms.
• Encyclopedia of Life (EOL): A species-centric online resource that aggregates information from various biodiversity databases and provides species pages with rich content.

Model Organism Databases:

• Specialized databases dedicated to providing comprehensive information about a particular species widely used for biological research.
• These databases act as central repositories for data on:
• Genome sequence and annotations
• Genes and their functions
• Proteins and their structures
• Experimental data (phenotypes, mutations, gene expression)
• Literature references and other relevant resources

Benefits:

• Facilitate research on biological processes, gene function, and development using these model organisms.
• Promote data sharing and collaboration among researchers worldwide.

Examples of Model Organism Databases:

• Escherichia coli: E. Coli Genome Centre, The E.coli index
• Arabidopsis thaliana: TAIR (The Arabidopsis Information Resource)
• Homo sapiens: Human Genome Resources at NCBI
• Oryza sativa (rice): RGP (Rice Genome Research Programme)
• Drosophila melanogaster: FlyBase (Drosophila Genome Database)
• Mus musculus (mouse): Mouse Genome Informatics
• Danio rerio (zebrafish): ZFIN (Zebrafish Information Network)
• Saccharomyces cerevisiae (baker's yeast): SGD (Saccharomyces Genome Database)

Annotation of Gene:

Genes and Genomes:

• Genomes are the complete genetic makeup of an organism, containing both coding (genes) and non-coding DNA regions.
• Coding regions, or genes, are the parts of the genome that contain instructions for building proteins, which are essential for life processes.

Gene Annotation:

• Gene annotation is the process of adding meaning and interpretation to raw DNA sequences, particularly those encoding genes.
• It involves analyzing the sequence and predicting its potential functions.
• This annotation process adds valuable information to databases, making them more informative and useful for researchers.

Benefits of Gene Annotation:

• Helps identify genes and their locations within the genome.
• Predicts the function of genes based on sequence similarity and other features.
• Provides insights into gene regulation and expression.
• Enables researchers to understand how genes contribute to biological processes and diseases.

Accession Numbers:

• Unique Identifiers: accession numbers are unique codes assigned to biological sequences (DNA, RNA, protein) deposited in databases.
• Permanent Identification: Once assigned, an accession number remains permanently linked to the specific sequence, allowing for clear and consistent reference across different resources.
• Fast Assignment: databases typically assign accession numbers within a short timeframe (often a couple of days) after receiving a sequence submission.

Additional Information:

• Accession numbers often follow a specific format that varies depending on the database.
• For example, GenBank accession numbers typically start with a letter followed by five digits (e.g., U12345).
• Accession numbers are crucial for researchers to:
• Find and retrieve specific sequences from databases.
• Cite sequences accurately in scientific publications.
• Track changes or updates to a sequence in the database (some databases use version numbers alongside accession numbers).

Overall, accession numbers are a fundamental component of bioinformatics databases, ensuring clear identification and retrieval of biological sequences.

Full abbreviations, their origin, and a brief description of their use or purpose:

1. Genebank: 

• Origin: National Center for Biotechnology Information (NCBI), USA. 
• Purpose: A database of nucleotide sequences, including DNA sequences from various organisms, with annotations and metadata.

2. DDBJ: 

• Origin: DNA Data Bank of Japan. 
• Purpose: One of the three major nucleotide sequence databases, alongside GenBank and EMBL-Bank, for archiving and sharing DNA sequence data.

3. EMB: 

• Origin: European Molecular Biology Laboratory (EMBL). 
• Purpose: EMBL-Bank is a nucleotide sequence database that stores DNA sequences submitted by researchers worldwide.

4. Swissport: 

• Origin: Swiss Institute of Bioinformatics (SIB). 
• Purpose: Swiss-Prot is a manually curated protein sequence database providing comprehensive information on protein function, structure, and interactions.

5. PIR: 

• Origin: Protein Information Resource. 
• Purpose: A comprehensive protein sequence database containing annotated and classified protein sequences, used for protein sequence analysis and annotation.

6. GenePept: 

• Origin: National Center for Biotechnology Information (NCBI), USA. 
• Purpose: A protein sequence database containing translations of all coding sequences (CDS) from GenBank, used for protein sequence analysis and annotation.

7. PDB: 

• Origin: Protein Data Bank. 
• Purpose: A repository of experimentally determined three-dimensional structures of proteins, nucleic acids, and complexes, used for structural biology and drug discovery.

8. ESB-MSD: 

• Origin: European Bioinformatics Institute (EBI). 
• Purpose: Electron Microscopy Data Bank - MSD (Macromolecular Structure Database) stores 3D EM maps and models, primarily for macromolecular complexes.

9. MMDB: 

• Origin: National Center for Biotechnology Information (NCBI), USA. 
• Purpose: Molecular Modeling Database, containing 3D structures of macromolecules derived from the Protein Data Bank (PDB), used for structure visualization and analysis.

10. PROSITE:

• Origin: Swiss Institute of Bioinformatics (SIB). 
• Purpose: A database of protein families and domains, containing patterns, profiles, and motifs characteristic of different protein families, used for sequence analysis and annotation.

11. BLOCKS: 

• Origin: Blocks Database Group. 
• Purpose: A database of conserved protein sequence motifs, or "blocks," derived from multiple sequence alignments, used for protein family classification and motif discovery.

12. COG:

• Origin: Clusters of Orthologous Groups.
• Purpose: A database of orthologous protein groups across multiple organisms, used for evolutionary and functional analysis of genes and proteins.

13. SCOP:

• Origin: Structural Classification of Proteins.
• Purpose: A database of protein structural domains, organized into a hierarchical classification scheme based on structural and evolutionary relationships.

14. CATH:

• Origin: Class, Architecture, Topology, Homologous superfamily.
• Purpose: A database of protein domain structures, classified into hierarchical levels based on their structural and functional characteristics.

15. GEO:

• Origin: National Center for Biotechnology Information (NCBI), USA.
• Purpose: Gene Expression Omnibus, a public repository for high-throughput gene expression data, including microarray and RNA-seq experiments.

16. GXD: 

• Origin: Mouse Genome Informatics (MGI).
• Purpose: Gene Expression Database, focusing on gene expression patterns during mouse development, providing spatial and temporal expression data.

17. MGED:

• Origin: Microarray and Gene Expression Database.
• Purpose: An organization promoting standards and best practices for microarray experiments and gene expression data analysis.

18. KEGG:

• Origin: Kyoto Encyclopedia of Genes and Genomes.
• Purpose: A database of biological pathways, diseases, drugs, and organisms, providing integrated information for systems biology and metabolic pathway analysis.

19. PathDB: 

• Origin: National Center for Biotechnology Information (NCBI), USA. 
• Purpose: A database of biochemical pathways and networks, integrating information from various sources for pathway analysis and visualization.

20. EMP:

• Origin: Environmental Microbial Profiling.
• Purpose: A database of microbial communities and their functional profiles, derived from environmental sequencing data, used for studying microbial ecology and biogeochemistry.

21. TGI:

• Origin: The Gene Index Project.
• Purpose: A collection of gene indices, representing transcript sequences from various organisms, used for gene discovery and expression analysis.

22. GSDB:

• Origin: Genome Sequence DataBase.
• Purpose: A database of complete genome sequences and annotations, providing resources for comparative genomics and evolutionary studies.

23. GPCRD: 

• Origin: G Protein-Coupled Receptor Database. Purpose: A database of G protein-coupled receptors (GPCRs), including sequence information, structural data, and ligand interactions, used for drug discovery and pharmacology research.