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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 subsequent process of analyzing and interpreting data is referred to as computational biology.
The Gene Ontology (GO) is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species. More specifically, the project aims to: 1) maintain and develop its controlled vocabulary of gene and gene product attributes; 2) annotate genes and gene products, and assimilate and disseminate annotation data; and 3) provide tools for easy access to all aspects of the data provided by the project, and to enable functional interpretation of experimental data using the GO, for example via enrichment analysis. GO is part of a larger classification effort, the Open Biomedical Ontologies, being one of the Initial Candidate Members of the OBO Foundry.
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, United States.
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 Rat Genome Database (RGD) is a database of rat genomics, genetics, physiology and functional data, as well as data for comparative genomics between rat, human and mouse. RGD is responsible for attaching biological information to the rat genome via structured vocabulary, or ontology, annotations assigned to genes and quantitative trait loci (QTL), and for consolidating rat strain data and making it available to the research community. They are also developing a suite of tools for mining and analyzing genomic, physiologic and functional data for the rat, and comparative data for rat, mouse, human, and five other species.
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.
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.
In molecular biology and genetics, DNA annotation or genome annotation is the process of describing the structure and function of the components of a genome, by analyzing and interpreting them in order to extract their biological significance and understand the biological processes in which they participate. Among other things, it identifies the locations of genes and all the coding regions in a genome and determines what those genes do.
The Critical Assessment of Functional Annotation (CAFA) is an experiment designed to provide a large-scale assessment of computational methods dedicated to predicting protein function. Different algorithms are evaluated by their ability to predict the Gene Ontology (GO) terms in the categories of Molecular Function, Biological Process, and Cellular Component.
Blast2GO, first published in 2005, is a bioinformatics software tool for the automatic, high-throughput functional annotation of novel sequence data. It makes use of the BLAST algorithm to identify similar sequences to then transfers existing functional annotation from yet characterised sequences to the novel one. The functional information is represented via the Gene Ontology (GO), a controlled vocabulary of functional attributes. The Gene Ontology, or GO, is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species.
In bioinformatics, the PANTHER classification system is a large curated biological database of gene/protein families and their functionally related subfamilies that can be used to classify and identify the function of gene products. PANTHER is part of the Gene Ontology Reference Genome Project designed to classify proteins and their genes for high-throughput analysis.
Gene set enrichment analysis (GSEA) (also called functional enrichment analysis or pathway enrichment analysis) is a method to identify classes of genes or proteins that are over-represented in a large set of genes or proteins, and may have an association with different phenotypes (e.g. different organism growth patterns or diseases). The method uses statistical approaches to identify significantly enriched or depleted groups of genes. Transcriptomics technologies and proteomics results often identify thousands of genes which are used for the analysis.
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.
Single nucleotide polymorphism annotation is the process of predicting the effect or function of an individual SNP using SNP annotation tools. In SNP annotation the biological information is extracted, collected and displayed in a clear form amenable to query. SNP functional annotation is typically performed based on the available information on nucleic acid and protein sequences.
Julian John Thurstan Gough is a Group Leader in the Laboratory of Molecular Biology (LMB) of the Medical Research Council (MRC). He was previously a professor of bioinformatics at the University of Bristol.
PomBase is a model organism database that provides online access to the fission yeast Schizosaccharomyces pombe genome sequence and annotated features, together with a wide range of manually curated functional gene-specific data. The PomBase website was redeveloped in 2016 to provide users with a more fully integrated, better-performing service.
Model organism databases (MODs) are biological databases, or knowledgebases, dedicated to the provision of in-depth biological data for intensively studied model organisms. MODs allow researchers to easily find background information on large sets of genes, plan experiments efficiently, combine their data with existing knowledge, and construct novel hypotheses. They allow users to analyse results and interpret datasets, and the data they generate are increasingly used to describe less well studied species. Where possible, MODs share common approaches to collect and represent biological information. For example, all MODs use the Gene Ontology (GO) to describe functions, processes and cellular locations of specific gene products. Projects also exist to enable software sharing for curation, visualization and querying between different MODs. Organismal diversity and varying user requirements however mean that MODs are often required to customize capture, display, and provision of data.
References
- ↑ Fang, H.; Gough, J. (2012). "DcGO: Database of domain-centric ontologies on functions, phenotypes, diseases and more". Nucleic Acids Research. 41 (Database issue): D536–D544. doi:10.1093/nar/gks1080. PMC 3531119 . PMID 23161684.
- ↑ De Lima Morais, D. A.; Fang, H.; Rackham, O. J. L.; Wilson, D.; Pethica, R.; Chothia, C.; Gough, J. (2010). "SUPERFAMILY 1.75 including a domain-centric gene ontology method". Nucleic Acids Research. 39 (Database issue): D427–D434. doi:10.1093/nar/gkq1130. PMC 3013712 . PMID 21062816.
- ↑ Fang, H.; Gough, J. (2013). "A domain-centric solution to functional genomics via dcGO Predictor". BMC Bioinformatics. 14 (Suppl 3): S9. doi: 10.1186/1471-2105-14-S3-S9 . PMC 3584936 . PMID 23514627.
- ↑ Radivojac, P.; Clark, W. T.; Oron, T. R.; Schnoes, A. M.; Wittkop, T.; Sokolov, A.; Graim, K.; Funk, C.; Verspoor, K.; Ben-Hur, A.; Pandey, G.; Yunes, J. M.; Talwalkar, A. S.; Repo, S.; Souza, M. L.; Piovesan, D.; Casadio, R.; Wang, Z.; Cheng, J.; Fang, H.; Gough, J.; Koskinen, P.; Törönen, P.; Nokso-Koivisto, J.; Holm, L.; Cozzetto, D.; Buchan, D. W. A.; Bryson, K.; Jones, D. T.; et al. (2013). "A large-scale evaluation of computational protein function prediction". Nature Methods. 10 (3): 221–227. doi:10.1038/nmeth.2340. PMC 3584181 . PMID 23353650.
- ↑ Fang, H; Gough, J (2013). "A disease-drug-phenotype matrix inferred by walking on a functional domain network". Molecular BioSystems. 9 (7): 1686–96. doi:10.1039/c3mb25495j. PMID 23462907.
- ↑ Fang, H.; Oates, M. E.; Pethica, R. B.; Greenwood, J. M.; Sardar, A. J.; Rackham, O. J. L.; Donoghue, P. C. J.; Stamatakis, A.; De Lima Morais, D. A.; Gough, J. (2013). "A daily-updated tree of (sequenced) life as a reference for genome research". Scientific Reports. 3: 2015. Bibcode:2013NatSR...3E2015F. doi:10.1038/srep02015. PMC 6504836 . PMID 23778980.
- ↑ Fang, H (2014). "DcGOR: An R package for analysing ontologies and protein domain annotations". PLOS Computational Biology. 10 (10): e1003929. Bibcode:2014PLSCB..10E3929F. doi: 10.1371/journal.pcbi.1003929 . PMC 4214615 . PMID 25356683.