Secondary Databases Definition

Secondary databases are repositories or resources that are specialized in storing and providing access to specific types of biological data within the field of bioinformatics. The creation of these databases is commonly achieved through the curation, organization, and integration of data from diverse primary sources, including experimental studies, literature, and other primary databases.

Secondary databases are considered to be significant resources for researchers, as they enable them to retrieve and scrutinize data that has been gathered and annotated by specialists in the relevant domain. Specialized databases are frequently centered on distinct domains of biological inquiry, such as genomics, proteomics, metabolomics, or pathways. These resources offer a centralized repository for scholars to obtain a plethora of data pertaining to distinct biological entities, including genes, proteins, pathways, or illnesses.

Secondary databases are typically characterized by comprehensive annotations and standardization, which facilitate the execution of comparative analyses, data mining, and other bioinformatics procedures by researchers. The databases in question have the potential to furnish a wealth of information pertaining to gene sequences, protein structures, functional annotations, expression patterns, genetic variations, protein-protein interactions, and other related areas.

Several prominent secondary databases utilized in bioinformatics are UniProt, NCBI’s Gene Expression Omnibus (GEO), Kyoto Encyclopedia of Genes and Genomes (KEGG), The Cancer Genome Atlas (TCGA), and the STRING database, which specializes in protein-protein interactions. Each of the aforementioned databases is tailored to specific categories of data or biological processes, enabling researchers to investigate and amalgamate information from a variety of sources in order to acquire a deeper understanding of diverse biological phenomena.

Secondary databases are of paramount importance in bioinformatics research due to their ability to offer centralized, curated, and readily accessible collections of biological data. This facilitates the utilization of pre-existing knowledge by researchers, thereby expediting their investigations.

Secondary Databases Types

The categorization of secondary databases in bioinformatics can be based on either the nature of the data they contain or their particular area of emphasis. Presented below is a taxonomy of secondary databases that are frequently encountered in the field of bioinformatics.

1. Nucleotide Sequence Databases: Nucleotide sequence databases are repositories that house nucleotide sequences procured from diverse sources, including but not limited to whole genome sequencing endeavors or individual research investigations. The nucleotide databases, namely GenBank, DDBJ (DNA Data Bank of Japan), and EMBL (European Molecular Biology Laboratory), are among the databases that can be cited as examples.
2. Protein Sequence Databases: Protein sequence databases comprise protein sequences that have been derived from experimental investigations or predicted from genomic information. Instances of such databases encompass UniProt, RefSeq, and Swiss-Prot.
3. Structure Databases: Structure databases are specialized databases that primarily deal with the tridimensional structures of biomolecules, including but not limited to proteins, nucleic acids, and complexes. The aforementioned resources furnish data pertaining to protein structures, their annotations, and concomitant functional information. The Protein Data Bank (PDB) stands as a prominent illustration.
4. Expression Databases: Expression databases are repositories that contain information regarding gene expression patterns. These databases encompass transcriptomic and proteomic datasets. The data furnished by the aforementioned sources pertains to the quantification of gene expression levels, expression patterns specific to certain tissues, and the variation in expression levels across different experimental conditions. Some instances comprise of Gene Expression Omnibus (GEO), ArrayExpress, and the Cancer Genome Atlas (TCGA).
5. Pathway Databases: Pathway databases are repositories of data pertaining to various biological pathways such as metabolic pathways, signaling pathways, and regulatory networks. The aforementioned materials furnish comprehensive information regarding molecular interactions, pathway diagrams, and related annotations. Instances of such databases comprise Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and BioCyc.
6. Variant Databases: Variant databases primarily concentrate on genetic variations, including but not limited to single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations. The information furnished pertains to genetic variations that are linked with diseases, population genetics, and the association between genotype and phenotype. Instances of such databases comprise dbSNP, ClinVar, and the Exome Aggregation Consortium (ExAC).
7. Drug Databases: Drug databases are repositories of data pertaining to drugs, encompassing their chemical compositions, pharmacological characteristics, targets, and interrelationships. These are utilized in the domains of drug discovery, drug repurposing, and pharmacogenomics investigation. Instances of such databases comprise DrugBank, PubChem, and ChEMBL.
8. Disease Databases: Disease databases are repositories of information that center on diseases, encompassing genetic mutations, clinical characteristics, and correlated genes. The information furnished by them pertains to the categorization of diseases, criteria for diagnosis, and targets for therapy. Some instances comprise of Online Mendelian Inheritance in Man (OMIM), Human Gene Mutation Database (HGMD), and Orphanet.
9. Interaction Databases: Interaction databases are repositories of data that collate information pertaining to molecular interactions. These interactions may include protein-protein interactions, protein-DNA interactions, and protein-ligand interactions. These tools facilitate the examination of intricate networks of molecular interactions occurring within cellular systems. Notable instances comprise STRING, BioGRID, and IntAct.
10. Literature Databases: Literature databases are utilized for indexing scientific literature, encompassing research articles, reviews, and conference proceedings. These platforms offer accessibility to scientific literature and streamline the process of retrieving information. Some of the examples of academic databases are PubMed, Scopus, and Web of Science.

The aforementioned taxonomy offers a comprehensive outline of secondary databases in the field of bioinformatics. However, it is crucial to acknowledge that there may exist certain intersections or amalgamations among various database categories, and novel databases are consistently being formulated to cater to evolving research requirements and data formats.

Examples of Secondary Databases

1. SWISS-PROT

• UniProtKB/Swiss-Prot, formerly referred to as SWISS-PROT, is a prominent biological repository that furnishes exhaustive and superior data on protein sequences and functional annotations, and is extensively utilized in the scientific community. This protein sequence database is widely recognized for its meticulous curation and has been a fundamental asset in the realm of bioinformatics for numerous years.
• In 1986, SWISS-PROT was founded through a partnership between the Swiss Institute of Bioinformatics (SIB) and the European Molecular Biology Laboratory (EMBL). The principal objective was to establish a repository comprising of protein sequences that have been manually annotated with a high degree of precision and comprehensive functional data.
• The database adheres to stringent curation protocols that entail the involvement of proficient biocurators. These experts meticulously scrutinize and provide explanatory notes on protein sequences, relying on experimental data from scholarly literature, sequence analysis, and other dependable resources. The process of manual curation guarantees the precision, excellence, and uniformity of the information contained in SWISS-PROT.
• UniProtKB/Swiss-Prot is a comprehensive resource that offers extensive data on proteins, encompassing their fundamental amino acid sequences, modifications that occur after translation, structural attributes, domains of proteins, functional domains, localization within subcellular structures, biological pathways, and interactions between proteins. The database comprises comprehensive data pertaining to protein functionalities, enzyme kinetics, disease correlations, and protein family categorizations.
• SWISS-PROT is characterized by the incorporation of succinct and skillfully crafted protein descriptions, commonly referred to as annotations. The act of annotating proteins offers significant contributions to the understanding of their functions, involvement in biological processes, and association with diseases. SWISS-PROT is a valuable resource for researchers who require reliable and comprehensive information on proteins, owing to its curated annotations.
• In addition, SWISS-PROT upholds a regulated lexicon for the purposeful annotations known as Gene Ontology (GO). The utilization of GO terms enables a uniform depiction of protein functionalities and their associations with molecular functions, cellular components, and biological processes.
• The UniProt Knowledgebase (UniProtKB) encompasses SWISS-PROT and TrEMBL (Translated EMBL Nucleotide Sequence Data Library). TrEMBL is a database that comprises protein sequences that have been predicted computationally. It functions as a supplementary resource to SWISS-PROT, thereby broadening the scope of protein sequence coverage.
• The UniProtKB/Swiss-Prot database is readily available to the scientific community, and its information can be obtained through diverse channels, such as web interfaces, FTP downloads, and APIs (Application Programming Interfaces) for programmatic access. The database undergoes periodic updates to integrate novel data, annotations, and enhancements in curation techniques.
• UniProtKB/Swiss-Prot is an essential resource in bioinformatics research due to its provision of precise, curated, and extensive data on protein sequences and their associated functionalities. The utilization of this technique has played a pivotal role in a multitude of research endeavors pertaining to the characterization of proteins, functional analysis, and the elucidation of genomic and proteomic data.

2. PROSITE 

• The PROSITE database is a highly utilized and firmly established biological repository that furnishes significant insights into protein families, domains, and functional sites. The aforementioned is a compilation of protein sequence motifs, profiles, and patterns that facilitate the discernment and delineation of conserved regions and functional components within proteins.
• The inception of PROSITE dates back to the early 1980s when a team led by Amos Bairoch at the University of Geneva, Switzerland, developed the database. The original purpose of its development was to cater to the requirement of a database that could facilitate the identification of functional domains and sites in proteins by relying on their patterns of amino acid sequence. Over time, PROSITE has undergone development and broadening to encompass diverse categories of protein signatures.
• The central objective of PROSITE is to detect and annotate conserved regions or patterns present in protein sequences that serve as reliable indicators of particular protein families, domains, or functional sites. The aforementioned patterns are explicated utilizing a precise syntax that denotes the conserved residues as well as the variability at particular positions.
• PROSITE motifs refer to concise sequence patterns or regular expressions that denote distinct functional sites or characteristics within proteins. The motifs under consideration may comprise elementary amino acid patterns or intricate profiles that encompass sequence variability at particular positions.
• PROSITE profiles are sophisticated depictions that encompass the occurrence and dispersion of amino acids at particular locations within a protein family or domain. The construction of these profiles involves the utilization of multiple sequence alignments of proteins that exhibit a shared domain or function. These methods offer a more sophisticated and delicate approach to protein identification and classification, utilizing sequence similarity as the basis.
• PROSITE incorporates functional annotations and cross-references to other databases, in addition to motifs and profiles. This resource offers insights into the biological import, structural attributes, and evolutionary associations of protein families, domains, and functional sites.
• PROSITE is a frequently employed tool in the fields of bioinformatics and molecular biology for diverse objectives, including but not limited to protein annotation, protein family categorization, protein domain recognition, and prognostication of functional sites. The PROSITE database can be utilized by researchers to conduct searches using either protein sequences or motifs. This enables the identification of conserved regions and facilitates the acquisition of functional insights pertaining to their proteins of interest.
• The PROSITE database is readily available through online interfaces, enabling users to conduct searches for particular motifs, profiles, or proteins of interest. The resource undergoes frequent updates and maintenance to integrate novel discoveries, annotations, and enhancements in computational techniques.
• In brief, PROSITE is an invaluable tool for the detection and delineation of protein families, domains, and functional sites through the utilization of sequence patterns and profiles. The augmentation of comprehension regarding protein structure, function, and evolution is facilitated by the presence of annotations and cross-references. The PROSITE tool remains a crucial resource for molecular biologists and bioinformaticians in their endeavors to analyze protein sequences and perform functional annotation.

3. Pfam

The Pfam database is a highly utilized and all-encompassing tool for the categorization and labeling of protein families and domains. The analysis offers significant insights into the organization, operation, and phylogenetic connections of proteins through the identification of common domains and motifs.

The establishment of Pfam was attributed to a group headed by Robert Finn at the European Bioinformatics Institute (EBI) during the latter part of the 1990s. The central objective of Pfam is to categorize and provide descriptive labels for proteins by leveraging their conserved protein domains. These domains represent the fundamental and structural components of proteins that are frequently present across related protein sequences.

The identification and characterization of protein domains are accomplished by Pfam through the utilization of computational techniques and the expertise of curators. The utilization of Hidden Markov Models (HMMs) is employed to portray the sequence profiles of protein families and domains. This approach enables the precise and sensitive identification of homologous domains across a wide range of protein sequences.

The database is comprised of two primary constituents:

1. Pfam-A: Pfam-A is the segment of the database that is subject to manual curation, wherein specialists curate and annotate protein families and domains. The aforementioned families are thoroughly defined and furnish reliable data pertaining to protein structures, functionalities, and related biological mechanisms.
2. Pfam-B: Pfam-B is a constituent of the database that is generated automatically and comprises of sequences that are clustered automatically with relatively limited curation. This serves as a supplementary resource to Pfam-A, thereby enhancing the comprehensiveness of the protein domain landscape.

The Pfam database furnishes a diverse array of information pertaining to every protein family or domain entry. This information encompasses multiple sequence alignments, diagrams of domain architecture, functional annotations, references to literature, and cross-references to other databases. The provided information encompasses details regarding the preservation of particular residues within domains and the existence of distinct sequence motifs.

The utilization of Pfam has become a crucial instrument in the process of protein annotation, comparative genomics, and protein structure/function prediction. The utilization of shared domains among proteins provides valuable insights into their functional characteristics and evolutionary relationships, thereby facilitating the interpretation of genomic data.

The Pfam database is readily available to the scientific community via web interfaces, enabling users to conduct protein family searches, peruse domain annotations, and obtain comprehensive information on particular protein domains. Periodic updates are made to the database to integrate novel protein families, domain models, and advancements in domain annotation techniques.

To summarize, the Pfam database represents a significant asset for the purposes of protein domain annotation, classification, and analysis. The extensive assemblage of domain models and corresponding annotations available in this resource offers valuable insights to researchers regarding protein structure, function, and evolutionary connections. This, in turn, enhances comprehension of biological mechanisms and assists in the analysis of genomic data.

4. PRINTS

• The PRINTS database is a bioinformatics tool that furnishes data pertaining to protein families and their conserved motifs. The primary objective of this study is to concentrate on the detection and labeling of remarkably preserved sequence patterns, commonly referred to as fingerprints, in protein sequences.
• The organization known as PRINTS was founded during the early 1990s by a team led by Dr. Toby Gibson, operating within the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI). The principal objective of PRINTS is to ascertain and annotate protein families by virtue of the existence of particular sequence motifs that are distinctive of those families.
• The construction of fingerprints in PRINTS involves the utilization of multiple sequence alignments of proteins that are closely related. The process of aligning sequences allows for the identification of conserved regions that are present across various members of a protein family. This, in turn, facilitates the recognition of shared sequence motifs that play a role in the family’s function or structure. The consensus sequence patterns or profile representations of these motifs are represented by the fingerprints.
• The database offers annotations and functional information linked to every fingerprint. The annotations encompass depictions of the protein family, operational domains, and correlated biological mechanisms. Furthermore, PRINTS provides interconnections to alternative databases, citations to scholarly works, and details on the evolutionary connections among protein families.
• The PRINTS database is notable for its prioritization of annotations that have been manually curated, which is a key advantage. The database undergoes regular review and updating by proficient curators to ensure the dependability and precision of the information. The process of manual curation serves to improve the quality of annotations and facilitates the interpretation of functional significance of identified motifs by researchers.
• The PRINTS database is readily available through online interfaces, enabling users to conduct searches for protein families or motifs of interest. Additionally, it provides users with resources for conducting motif searching and sequence scanning in order to detect the presence of particular fingerprints within protein sequences.
• Although PRINTS has been a valuable resource in the field of bioinformatics, it is noteworthy that the database has not undergone active updates since 2009. Notwithstanding, the curated data and patterns featured in PRINTS can offer valuable perspectives on protein families and their persistent sequence motifs.
• To summarize, the PRINTS database serves as a tool for recognizing and annotating protein families through the utilization of conserved motifs or fingerprints. The platform provides meticulously curated annotations and furnishes significant insights into the structural, functional, and evolutionary interrelationships of proteins. Despite its discontinued maintenance, the meticulously selected motifs and their corresponding annotations in PRINTS remain highly beneficial for conducting protein sequence analysis and facilitating functional annotation.

5. BLOCKS

• The BLOCKS database is a bioinformatics tool that is dedicated to the identification and annotation of conserved protein sequence motifs, commonly referred to as blocks. The provision of significant insights into protein families and their conserved regions is instrumental in enhancing comprehension of protein structure, function, and evolutionary interrelationships.
• The BLOCKS database was established during the early 1990s by a team headed by Dr. Steven Henikoff at the Fred Hutchinson Cancer Research Center. The main aim of BLOCKS is to detect and document conserved sequence patterns within protein families, enabling scholars to recognize and scrutinize these patterns across diverse protein sequences.
• The database’s blocks are fabricated through the utilization of multiple sequence alignments of proteins that are interrelated. The aforementioned alignments serve to capture the regions that are conserved and shared among the various members of a given protein family. The consensus sequence patterns or profiles of the conserved motifs are represented by the blocks.
• Every individual block within the database is allocated a distinct identifier and is linked with annotations and functional data. The aforementioned annotations furnish explications regarding the protein family, functional domains, and any established biological processes linked with the conserved motifs. In addition to its primary function, the database provides cross-references to other databases, literature references, and data pertaining to evolutionary relationships among protein families.
• The BLOCKS database undergoes manual curation, wherein proficient curators conduct periodic reviews and updates to ensure its accuracy and reliability. The process of manual curation guarantees the precision and dependability of the annotations, thereby facilitating the comprehension of the functional implications of the detected blocks by researchers.
• The BLOCKS database can be accessed via web interfaces, enabling users to conduct targeted searches for protein families or blocks of interest. The database is equipped with tools that facilitate motif searching and sequence scanning, enabling the identification of specific blocks within protein sequences.
• It is noteworthy to mention that the BLOCKS database has not undergone active updates since 2007. Notwithstanding, the compiled data and preserved patterns in the database remain significant assets for scholars investigating protein lineages and their persistent sequence configurations.
• To summarize, the BLOCKS database is a bioinformatics tool that is dedicated to detecting and annotating conserved protein sequence motifs in protein families. The resource furnishes meticulously curated annotations and confers significant insights into protein structure, function, and evolutionary associations. Despite being in a state of disuse, the BLOCKS database’s meticulously selected motifs and their corresponding annotations remain valuable for protein sequence analysis and functional annotation purposes.

6. InterPro

• The InterPro database is a bioinformatics resource that offers a comprehensive integration of information from multiple protein databases, with the aim of providing functional analysis and classification of protein sequences. The objective is to detect conserved domains, motifs, and functional sites present in protein sequences, which can provide significant knowledge regarding their structure, function, and evolutionary connections.
• The inception of InterPro dates back to 1999, when a joint effort was initiated by the European Bioinformatics Institute (EMBL-EBI), the SIB Swiss Institute of Bioinformatics, and the Protein Information Resource (PIR). The task at hand pertains to the annotation and classification of protein sequences that are being generated at an accelerated pace due to the ongoing genome sequencing projects.
• The integration of data and analysis from various resources, such as Pfam, PROSITE, PRINTS, SMART, TIGRFAMs, and others, is facilitated by the database. The aforementioned resources provide domain profiles, motifs, and functional annotations that are integrated to generate InterPro entries.
• InterPro employs a range of computational techniques, including Hidden Markov Models (HMMs), sequence patterns, and profile-based searches, to detect conserved domains and motifs in protein sequences. The utilization of Gene Ontology (GO) terms is integrated into the system to furnish functional annotations and protein classification according to biological processes, molecular functions, and cellular components.
• The entries within the database are systematically categorized based on their protein families, domains, repeats, sites, and other pertinent functional attributes. Every record is allocated a distinct identifier and comprises of annotations, cross-references to external databases, literature citations, and structural data, if accessible. Additionally, it offers visual depictions of protein domain structures and sequence comparisons to facilitate the examination of protein sequences.
• InterPro provides a range of tools and resources that facilitate protein sequence analysis. Among these, the InterProScan tool enables users to submit protein sequences for automated annotation and identification of domains and motifs. The database undergoes periodic updates to integrate novel data, refine annotation techniques, and optimize the amalgamation of information from diverse sources.
• The InterPro database can be accessed without charge via web interfaces, which offer users various search and browsing functionalities, as well as the option to obtain data and analysis outcomes. The database is a significant asset for scholars investigating protein structure, function, and evolution. Additionally, it proves useful for the functional annotation and classification of recently sequenced proteins.
• To summarize, the InterPro database is a comprehensive tool that amalgamates data from various protein databases to furnish functional analysis and classification of protein sequences. The integration of computational techniques, domain profiles, motifs, and functional annotations is employed to facilitate comprehension of protein structure, function, and evolutionary connections. The InterPro tool is of paramount importance in the field of bioinformatics research as it enables the comprehension of genomic and proteomic data.

7. Gene Ontology

The Gene Ontology (GO) database is a bioinformatics tool that offers uniform functional annotations for genes and their products across various organisms. The provided framework presents a systematic lexicon and structure for characterizing the biological operations, cellular constituents, and molecular mechanisms linked to genes and their corresponding proteins.

The Gene Ontology was initiated in the latter part of the 1990s through a collaborative endeavor among several research institutions. The principal objective was to establish a regulated lexicon that could be employed to consistently annotate genes and their corresponding products, thereby streamlining data integration and comparative analysis across diverse organisms.

The Gene Ontology comprises three primary ontologies or categories:

1. Molecular Function: The ontology of Molecular Function pertains to the fundamental actions performed by gene products on a molecular scale. The repertoire of functions encompassed by this entity comprises catalytic activities, specific molecule binding, receptor activities, and additional functionalities.
2. Biological Process: The ontology under consideration pertains to biological phenomena, encompassing events, processes, or pathways that entail the participation of genes and their corresponding products. The aforementioned biological phenomena encompass a range of processes, including but not limited to cellular development, metabolic activity, intercellular signaling, progression through the cell cycle, and immune system response.
3. Cellular Component: The ontology under consideration pertains to the cellular components where gene products are either active or present, encompassing the cellular locations and structures. The cellular structure encompasses various constituents, namely organelles, membranes, cytoskeleton, nucleus, and extracellular regions.

The Gene Ontology comprises a hierarchical structure wherein each category is arranged in a manner that exhibits specificity, with broader terms situated above more specific ones. Nodes in a directed acyclic graph (DAG) are utilized to represent terms, thereby enabling the capture of interrelationships among them.

The Gene Ontology’s annotations are generated via a combination of manual curation by experts in the relevant field and automated techniques that utilize computational tools and algorithms. Annotations are correlated with particular genes or gene products, thereby connecting them to the relevant terminologies present in the ontology. The act of annotating genetic information offers valuable insights into the various functions, processes, and locations that are associated with genes and their corresponding products.

The Gene Ontology database is readily available through online interfaces, enabling users to conduct targeted searches for particular genes or gene products and obtain their functional annotations. The database offers a range of tools that facilitate the analysis of data, enrichment analysis, and visualization of Gene Ontology (GO) terms.

The Gene Ontology has gained significant traction as a widely utilized resource within the domains of bioinformatics and genomics. This technology facilitates the annotation and analysis of vast genomic and transcriptomic datasets, allowing for cross-species comparison of gene functions and providing valuable insights into the biological processes and functions linked to genes of interest.

To summarize, the Gene Ontology database offers a uniform terminology and structure for characterizing the functionalities, mechanisms, and localizations linked to genes and their resultant molecules. The utilization of this tool is of utmost importance in the domain of bioinformatics as it serves as a crucial component in functional annotation, data integration, and comparative analysis. Its implementation enables the interpretation of gene-related data and provides valuable insights into biological systems.

8. KEGG

The KEGG database is a bioinformatics resource that integrates biological information from different molecular-level datasets in a comprehensive manner. The analysis of genes and gene products in various organisms offers significant knowledge about their functions, interactions, and pathways.

The Kyoto Encyclopedia of Genes and Genomes (KEGG) was founded in the early 1990s by Minoru Kanehisa and his team at Kyoto University in Japan. The integration of genomic, chemical, and systemic information into a unified resource aims to bridge the gap between genomic information and biological knowledge.

The KEGG database is composed of three primary components:

• KEGG Pathway: The KEGG Pathway is a comprehensive resource that offers in-depth insights into the molecular interactions, reactions, and signaling pathways across various biological systems. The subject matter encompasses metabolic pathways, cellular processes, and a range of diseases. The graphical maps represent pathways, which enable users to visualize and explore the connections between genes, proteins, and other molecules that participate in particular biological processes.
• KEGG Orthology (KO): The KO component is a tool that offers functional annotations for genes and proteins by utilizing orthologous relationships. Orthologs are genes that have been inherited from a common ancestor and are found in different organisms. These genes usually have similar functions and play important roles in the biological processes of the organisms that possess them. The KO system is a tool that assigns unique identifiers (KO numbers) to genes and proteins. This helps in analyzing functional similarities and differences across different species.
• KEGG Genome: The KEGG Genome is a database that provides information on the complete or draft genome sequences of different organisms. The platform offers users the ability to access gene catalogs, functional annotations, and other genomic data. This allows users to investigate the functions of genes within specific organisms.

The KEGG database is a comprehensive resource that includes KEGG Brite, a hierarchical organization of biological entities, and KEGG Ligand, which provides information on chemical compounds, enzymes, and drugs.

The KEGG database is a regularly updated and expanded resource that integrates new data and discoveries. The accuracy and reliability of the information is ensured through the use of computational methods, data integration, and expert curation.

The KEGG database can be accessed through web interfaces, which enables users to conduct searches for particular genes, pathways, or organisms. The database is equipped with a range of tools that allow researchers to analyze, visualize, and interpret data. This enables them to investigate gene functions, metabolic pathways, disease mechanisms, and other biological processes.

The KEGG database is a resource that is widely used in research related to genomics, systems biology, and bioinformatics. The interpretation of genomic data, understanding of cellular processes, and identification of potential targets for drug development and disease research are facilitated by it.

The KEGG database is a resource that integrates various biological information, such as pathways, orthologous relationships, and genomic data, in a comprehensive manner. This tool enables the investigation of gene functions, molecular interactions, and metabolic pathways in various organisms. The KEGG database is an essential tool for analyzing and interpreting biological data, which helps us comprehend intricate biological systems.

9. Reactome

• The Reactome database is a bioinformatics resource that offers comprehensive information on molecular interactions, biological pathways, and processes. The field of molecular biology is concerned with the thorough examination and interpretation of the molecular mechanisms that underlie a range of biological phenomena, including metabolism, signal transduction, and cellular processes.
• The collaboration between the Ontario Institute for Cancer Research (OICR) and the European Bioinformatics Institute (EMBL-EBI) led to the initiation of Reactome in 2003. The main objective of the platform is to gather and organize accurate data on biological pathways and reactions. This allows scientists to explore and comprehend the intricate web of molecular interactions involved in various cellular processes.
• The Reactome database is a collection of carefully curated pathways that provide a detailed account of the chronological order of events that occur during various biological processes. The pathways mentioned by the user encompass various biological fields such as metabolism, immune system, signal transduction, cell cycle, and more. Reactome’s curated content is sourced from a combination of literature curation, expert knowledge, and computational predictions.
• The database provides comprehensive data on various molecules, including proteins, small molecules, and nucleic acids, along with their interactions and modifications. The tool offers information about the annotations related to the function, localization, and regulatory events of each molecule present in a pathway. Reactome is an information resource that includes cross-references to other databases such as gene annotations, protein structures, and disease associations.
• Various analysis and visualization tools are provided by Reactome to assist researchers in interpreting and exploring pathway data. The tools provided enable users to perform various tasks such as investigating specific pathways, analyzing expression data, conducting network-based analysis, and creating interactive pathway diagrams.
• Reactome is a platform that is particularly focused on human biology. The database has a broad coverage of species, but it focuses mainly on human pathways and processes. Reactome is a valuable resource for researchers who are interested in studying human biology, disease mechanisms, and drug discovery.
• The Reactome database can be accessed through web interfaces without any cost. Users can utilize the search feature to find particular pathways, molecules, or diseases. The platform offers downloadable data sets, APIs for programmatic access, and the ability to integrate with other bioinformatics tools and resources.
• The Reactome database is a resource that provides comprehensive curation, annotation, and analysis of biological pathways and molecular interactions. The provided information covers a wide range of cellular processes, which aids in the examination of intricate biological systems and offers understanding into disease mechanisms and potential therapeutic targets. Reactome is a tool that researchers in genomics, systems biology, and drug discovery can use to aid their work.

Applications of Secondary Databases 

Secondary databases are essential in different areas of bioinformatics and have a wide range of uses. Secondary databases are commonly used for various purposes such as data warehousing, business intelligence, data mining, and analytics. They are also utilized for backup and disaster recovery, as well as for testing and development purposes.

• Sequence Annotation: Secondary databases are sources of annotations and functional information for various biomolecules such as genes and proteins. These databases are used for sequence annotation purposes. Annotations are used by researchers to gain a better understanding of the biological functions, domains, motifs, and structures that are linked to particular sequences. The interpretation of experimental data and identification of potential functional elements within sequences can be facilitated by this information.
• Comparative Genomics: Secondary databases are used in comparative genomics to facilitate the comparison of sequences, genes, and genomes between various organisms. Alignment and comparison of sequences from different species is a useful technique for researchers to identify conserved regions, detect orthologous genes, study evolutionary relationships, and infer functional similarities and differences between species.
• Protein Structure and Function Prediction: Secondary databases provide valuable information on protein families, domains, motifs, and structural features, which can be used for predicting protein structure and function. The data has the potential to be used for predicting the structure and function of proteins by identifying similarities with proteins that are already known. Predictions play a crucial role in comprehending the molecular mechanisms that govern biological processes and are instrumental in the development of drugs and identification of targets.
• Pathway Analysis: Information on biological pathways, such as metabolic pathways, signaling cascades, and regulatory networks, can be obtained from secondary databases that offer curated data for pathway analysis. These databases are used by researchers to analyze and interpret high-throughput experimental data, such as gene expression or proteomic data, in the context of specific pathways. Pathway analysis is a useful tool that can reveal the connections and associations among genes and proteins. It can aid in comprehending cellular processes and the mechanisms of diseases.
• Functional Enrichment Analysis: Functional enrichment analysis is a method that utilizes secondary databases to identify overrepresented gene sets or functional categories within a given dataset. This analysis is useful in determining the biological significance of a set of genes and can provide insights into their potential functions. Researchers can determine the biological functions, pathways, or processes that are significantly associated with their data by comparing it to annotations and functional information in secondary databases. The purpose of this analysis is to gain a better understanding of the biological processes involved and to develop potential hypotheses for future research.
• Data Integration: Secondary databases are central repositories that store biological data from various sources, facilitating data integration. Integrating data from primary sources with annotations and cross-references available in secondary databases allows for comprehensive and integrated analysis by researchers. The integration enables researchers to perform data mining, knowledge discovery, and generate new hypotheses in diverse research fields.

Secondary databases play a crucial role in bioinformatics by offering curated information, functional annotations, and enabling data analysis across various biological domains. Computational tools play a crucial role in biological research by improving our comprehension of biological systems, facilitating the analysis of experimental data, and assisting in the identification of novel insights and hypotheses.

FAQ

References

1. Benson, D. A., et al. (2018). GenBank. Nucleic Acids Research, 46(D1), D41-D47.
2. UniProt Consortium. (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research, 49(D1), D480-D489.
3. Wu, C. H., et al. (2004). The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids Research, 32(suppl_1), D115-D119.
4. Hubbard, T., et al. (2002). The Ensembl genome database project. Nucleic Acids Research, 30(1), 38-41.
5. Bairoch, A. (2000). The ENZYME database in 2000. Nucleic Acids Research, 28(1), 304-305.
6. Croft, D., et al. (2014). The Reactome pathway knowledgebase. Nucleic Acids Research, 42(D1), D472-D477.
7. Kanehisa, M., et al. (2021). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 49(D1), D684-D690.
8. Gattiker, A., et al. (2002). The GO database: tools for the Gene Ontology. Nucleic Acids Research, 30(1), 262-266.
9. Apweiler, R., et al. (2004). InterPro: an integrated documentation resource for protein families, domains and functional sites. Briefings in Bioinformatics, 5(2), 46-54.
10. Attwood, T. K., et al. (2002). PRINTS and its automatic supplement, prePRINTS. Nucleic Acids Research, 30(1), 283-285.