Biological database

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Home page of a biological database called characterises functional links between proteins String home page.png
Home page of a biological database called characterises functional links between proteins

Biological databases are libraries of biological sciences, collected from scientific experiments, published literature, high-throughput experiment technology, and computational analysis.[ citation needed ] They contain information from research areas including genomics, proteomics, metabolomics, microarray gene expression, and phylogenetics. [2] Information contained in biological databases includes gene function, structure, localization (both cellular and chromosomal), clinical effects of mutations as well as similarities of biological sequences and structures.

Contents

Biological databases can be classified by the kind of data they collect (see below). Broadly, there are molecular databases (for sequences, molecules, etc.), functional databases (for physiology, enzyme activities, phenotypes, ecology etc), taxonomic databases (for species and other taxonomic ranks), images and other media, or specimens (for museum collections etc.)

Databases are important tools in assisting scientists to analyze and explain a host of biological phenomena from the structure of biomolecules and their interaction, to the whole metabolism of organisms and to understanding the evolution of species. This knowledge helps facilitate the fight against diseases, assists in the development of medications, predicting certain genetic diseases and in discovering basic relationships among species in the history of life.

Technical basis and theoretical concepts

Relational database concepts of computer science and Information retrieval concepts of digital libraries are important for understanding biological databases. Biological database design, development, and long-term management is a core area of the discipline of bioinformatics. [3] Data contents include gene sequences, textual descriptions, attributes and ontology classifications, citations, and tabular data. These are often described as semi-structured data, and can be represented as tables, key delimited records, and XML structures.[ citation needed ]

Access

Most biological databases are available through web sites that organise data such that users can browse through the data online. In addition the underlying data is usually available for download in a variety of formats. Biological data comes in many formats. These formats include text, sequence data, protein structure and links. Each of these can be found from certain sources, for example:[ citation needed ]

Problems and challenges

Biological knowledge is distributed among countless databases. This sometimes makes it difficult to ensure the consistency of information, e.g. when different names are used for the same species or different data formats. As a consequence, inter-operability is a constant challenge for information exchange. For instance, if a DNA sequence database stores the DNA sequence along the name of a species, a name change of that species may break the links to other databases which may use a different name. Integrative bioinformatics is one field attempting to tackle this problem by providing unified access. One solution is how biological databases cross-reference to other databases with accession numbers to link their related knowledge together (e.g. so that the accession number stays the same even if a species name changes). Redundancy is another problem, as many databases must store the same information, e.g. protein structure databases also contain the sequence of the proteins they cover, their sequence, and their bibliographic information.

Model-organism databases

Species-specific databases are available for some species, mainly those that are often used in research (model organisms). For example, EcoCyc is an E. coli database. Other popular model organism databases include Mouse Genome Informatics for the laboratory mouse, Mus musculus, the Rat Genome Database for Rattus, ZFIN for Danio Rerio (zebrafish), PomBase [4] for the fission yeast Schizosaccharomyces pombe, FlyBase for Drosophila, WormBase for the nematodes Caenorhabditis elegans and Caenorhabditis briggsae , and Xenbase for Xenopus tropicalis and Xenopus laevis frogs.

Biodiversity and species databases

Animal groups and their number of species from the Catalogue of Life Animal kingdom chart from Catalogue of Life.png
Animal groups and their number of species from the Catalogue of Life

Numerous databases attempt to document the diversity of life on earth. A prominent example is the Catalogue of Life, first created in 2001 by Species 2000 and the Integrated Taxonomic Information System. [6] The Catalogue of Life is a collaborative project that aims to document taxonomic categorization of all currently accepted species in the world. [7] The Catalogue of Life provides a consolidated and consistent database for researchers and policymakers to reference. The Catalogue of Life curates up-to-date datasets from other sources such as Conifer Database, ICTV MSL (for viruses), and LepIndex (for butterflies and moths). In total, the Catalogue of Life draws from 165 databases as of May 2022. [8] Operational costs of the Catalogue of Life are paid for by the Global Biodiversity Information Facility, the Illinois Natural History Survey, the Naturalis Biodiversity Center, and the Smithsonian Institution. [9]

Some biological databases also document geographical distribution of different species. Shuang Dai et al. created a new multi-source database to document spatial/geographical distribution of 1,371 bird species in China, as existing databases had been severely lacking in spatial distribution data for many species. [10] Sources for this new database included books, literature, GPS tracking, and online webpage data. The new database displayed taxonomy, distribution, species info, and data sources for each species. After completion of the bird spatial distribution database, it was discovered that 61% of known species in China were found to be distributed in regions beyond where they were previously known. [11]

Medical databases

Foot wounds from WoundsDB Woundsdb.png
Foot wounds from WoundsDB

Medical databases are a special case of biomedical data resource and can range from bibliographies, such as PubMed, to image databases for the development of AI based diagnostic software. For instance, one such image database was developed with the goal of aiding in the development of wound monitoring algorithms. [13] Over 188 multi-modal image sets were curated from 79 patient visits, consisting of photographs, thermal images, and 3D mesh depth maps. Wound outlines were manually drawn and added to the photo datasets. [14] The database was made publicly available in the form of a program called WoundsDB, downloadable from the Chronic Wound Database website.

Nucleic Acids Research Database Issue

An important resource for finding biological databases is a special yearly issue of the journal Nucleic Acids Research (NAR). The Database Issue of NAR is freely available, and categorizes many of the public biological databases. A companion database to the issue called the Online Molecular Biology Database Collection lists 1,380 online databases. [15] Other collections of databases exist such as MetaBase and the Bioinformatics Links Collection. [16] [17]

See also

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The process of analyzing and interpreting data can sometimes be referred to as computational biology, however this distinction between the two terms is often disputed. To some, the term computational biology refers to building and using models of biological systems.

<span class="mw-page-title-main">UniProt</span> Database of protein sequences and functional information

UniProt is a freely accessible database of protein sequence and functional information, many entries being derived from genome sequencing projects. It contains a large amount of information about the biological function of proteins derived from the research literature. It is maintained by the UniProt consortium, which consists of several European bioinformatics organisations and a foundation from Washington, DC, USA.

The Protein Information Resource (PIR), located at Georgetown University Medical Center, is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies. It contains protein sequences databases

The European Bioinformatics Institute (EMBL-EBI) is an intergovernmental organization (IGO) which, as part of the European Molecular Biology Laboratory (EMBL) family, focuses on research and services in bioinformatics. It is located on the Wellcome Genome Campus in Hinxton near Cambridge, and employs over 600 full-time equivalent (FTE) staff.

The DrugBank database is a comprehensive, freely accessible, online database containing information on drugs and drug targets created and maintained by the University of Alberta and The Metabolomics Innovation Centre located in Alberta, Canada. As both a bioinformatics and a cheminformatics resource, DrugBank combines detailed drug data with comprehensive drug target information. DrugBank has used content from Wikipedia; Wikipedia also often links to Drugbank, posing potential circular reporting issues.

<span class="mw-page-title-main">Pfam</span> Database of protein families

Pfam is a database of protein families that includes their annotations and multiple sequence alignments generated using hidden Markov models. The latest version of Pfam, 37.0, was released in June 2024 and contains 21,979 families. It is currently provided through InterPro website.

<span class="mw-page-title-main">Amos Bairoch</span> Swiss bioinformatician

Amos Bairoch is a Swiss bioinformatician and Professor of Bioinformatics at the Department of Human Protein Sciences of the University of Geneva where he leads the CALIPHO group at the Swiss Institute of Bioinformatics (SIB) combining bioinformatics, curation, and experimental efforts to functionally characterize human proteins.

InterPro is a database of protein families, protein domains and functional sites in which identifiable features found in known proteins can be applied to new protein sequences in order to functionally characterise them.

<span class="mw-page-title-main">PROSITE</span> Database of protein domains, families and functional sites

PROSITE is a protein database. It consists of entries describing the protein families, domains and functional sites as well as amino acid patterns and profiles in them. These are manually curated by a team of the Swiss Institute of Bioinformatics and tightly integrated into Swiss-Prot protein annotation. PROSITE was created in 1988 by Amos Bairoch, who directed the group for more than 20 years. Since July 2018, the director of PROSITE and Swiss-Prot is Alan Bridge.

Expasy is an online bioinformatics resource operated by the SIB Swiss Institute of Bioinformatics. It is an extensible and integrative portal which provides access to over 160 databases and software tools and supports a range of life science and clinical research areas, from genomics, proteomics and structural biology, to evolution and phylogeny, systems biology and medical chemistry. The individual resources are hosted in a decentralized way by different groups of the SIB Swiss Institute of Bioinformatics and partner institutions.

The Database of Macromolecular Motions is a bioinformatics database and software-as-a-service tool that attempts to categorize macromolecular motions, sometimes also known as conformational change. It was originally developed by Mark B. Gerstein, Werner Krebs, and Nat Echols in the Molecular Biophysics & Biochemistry Department at Yale University.

<span class="mw-page-title-main">PHI-base</span> Biological database

The Pathogen-Host Interactions database (PHI-base) is a biological database that contains manually curated information on genes experimentally proven to affect the outcome of pathogen-host interactions. The database has been maintained by researchers at Rothamsted Research and external collaborators since 2005. PHI-base has been part of the UK node of ELIXIR, the European life-science infrastructure for biological information, since 2016.

Rfam is a database containing information about non-coding RNA (ncRNA) families and other structured RNA elements. It is an annotated, open access database originally developed at the Wellcome Trust Sanger Institute in collaboration with Janelia Farm, and currently hosted at the European Bioinformatics Institute. Rfam is designed to be similar to the Pfam database for annotating protein families.

<span class="mw-page-title-main">MicrobesOnline</span>

MicrobesOnline is a publicly and freely accessible website that hosts multiple comparative genomic tools for comparing microbial species at the genomic, transcriptomic and functional levels. MicrobesOnline was developed by the Virtual Institute for Microbial Stress and Survival, which is based at the Lawrence Berkeley National Laboratory in Berkeley, California. The site was launched in 2005, with regular updates until 2011.

SUPERFAMILY is a database and search platform of structural and functional annotation for all proteins and genomes. It classifies amino acid sequences into known structural domains, especially into SCOP superfamilies. Domains are functional, structural, and evolutionary units that form proteins. Domains of common Ancestry are grouped into superfamilies. The domains and domain superfamilies are defined and described in SCOP. Superfamilies are groups of proteins which have structural evidence to support a common evolutionary ancestor but may not have detectable sequence homology.

Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.

PDBsum is a database that provides an overview of the contents of each 3D macromolecular structure deposited in the Protein Data Bank (PDB).

In bioinformatics, a Gene Disease Database is a systematized collection of data, typically structured to model aspects of reality, in a way to comprehend the underlying mechanisms of complex diseases, by understanding multiple composite interactions between phenotype-genotype relationships and gene-disease mechanisms. Gene Disease Databases integrate human gene-disease associations from various expert curated databases and text mining derived associations including Mendelian, complex and environmental diseases.

References

  1. ↑ Szklarczyk D; Franceschini A; Kuhn M; et al. (January 2011). "The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored". Nucleic Acids Res. 39 (Database issue): D561–8. doi:10.1093/nar/gkq973. PMC   3013807 . PMID   21045058.
  2. ↑ Altman RB (March 2004). "Building successful biological databases". Brief. Bioinformatics. 5 (1): 4–5. doi: 10.1093/bib/5.1.4 . PMID   15153301.
  3. ↑ Bourne P (August 2005). "Will a biological database be different from a biological journal?". PLOS Comput. Biol. 1 (3): 179–81. Bibcode:2005PLSCB...1...34B. doi: 10.1371/journal.pcbi.0010034 . PMC   1193993 . PMID   16158097.
  4. ↑ Lock, A; Rutherford, K; Harris, MA; Hayles, J; Oliver, SG; Bähler, J; Wood, V (13 October 2018). "PomBase 2018: user-driven reimplementation of the fission yeast database provides rapid and intuitive access to diverse, interconnected information". Nucleic Acids Research. 47 (D1): D821–D827. doi:10.1093/nar/gky961. PMC   6324063 . PMID   30321395.
  5. ↑ Catalogue of Life (2001). "Homepage". Search. Species 2000. Archived from the original on 2022-05-05. Retrieved 2022-05-05.
  6. ↑ Jones, Andrew C. (2011). "Identifying and Relating Biological Concepts in the Catalogue of Life". Journal of Biomedical Semantics. 2 (1): 7. doi: 10.1186/2041-1480-2-7 . PMC   3245425 . PMID   22004596.
  7. ↑ Catalogue of Life (2001). "What is Catalogue of Life?". Our Mission. Species 2000. Archived from the original on 2022-05-05. Retrieved 2022-05-05.
  8. ↑ Catalogue of Life (2001). "Source Datasets". Species 2000. Archived from the original on 2022-05-14. Retrieved 2022-05-05.
  9. ↑ Catalogue of Life (2001). "Funding". Species 2000. Archived from the original on 2022-05-05. Retrieved 2022-05-05.
  10. ↑ Dai, Shuang (2019). "A Spatialized Digital Database for All Bird Species in China". Science China Life Sciences. 62 (5): 661–667. doi:10.1007/s11427-018-9419-2. PMID   30900164. S2CID   84845653 . Retrieved 2022-05-05.
  11. ↑ Dai, Shuang (2019). "A Spatialized Digital Database for All Bird Species in China". Science China Life Sciences. 62 (5): 661–667. doi:10.1007/s11427-018-9419-2. PMID   30900164. S2CID   84845653 . Retrieved 2022-05-05.
  12. ↑ "Chronic Wound Database". WoundsDB. Silesian University of Technology. 2020. Retrieved 2022-05-05.
  13. ↑ Kręcichwost, Michał (2021). "Chronic Wounds Multimodal Image Database". Computerized Medical Imaging and Graphics. 88: 101844. doi:10.1016/j.compmedimag.2020.101844. PMID   33477091. S2CID   231676950 . Retrieved 2022-05-05.
  14. ↑ "Chronic Wound Database". WoundsDB. Silesian University of Technology. 2020. Retrieved 2022-05-05.
  15. ↑ Galperin MY; Fernández-Suárez XM (January 2012). "The 2012 Nucleic Acids Research Database Issue and the online Molecular Biology Database Collection". Nucleic Acids Res. 40 (Database issue): D1–8. doi:10.1093/nar/gkr1196. PMC   3245068 . PMID   22144685.
  16. ↑ Bolser DM; Chibon PY; Palopoli N; et al. (January 2012). "MetaBase--the wiki-database of biological databases". Nucleic Acids Res. 40 (Database issue): D1250–4. doi:10.1093/nar/gkr1099. PMC   3245051 . PMID   22139927.
  17. ↑ Brazas MD; Yim DS; Yamada JT; Ouellette BF (July 2011). "The 2011 Bioinformatics Links Directory update: more resources, tools and databases and features to empower the bioinformatics community". Nucleic Acids Res. 39 (Web Server issue): W3–7. doi:10.1093/nar/gkr514. PMC   3125814 . PMID   21715385.
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