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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.
In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. Methodologies used include sequence alignment, searches against biological databases, and others.
Critical Assessment of Structure Prediction (CASP), sometimes called Critical Assessment of Protein Structure Prediction, is a community-wide, worldwide experiment for protein structure prediction taking place every two years since 1994. CASP provides research groups with an opportunity to objectively test their structure prediction methods and delivers an independent assessment of the state of the art in protein structure modeling to the research community and software users. Even though the primary goal of CASP is to help advance the methods of identifying protein three-dimensional structure from its amino acid sequence many view the experiment more as a “world championship” in this field of science. More than 100 research groups from all over the world participate in CASP on a regular basis and it is not uncommon for entire groups to suspend their other research for months while they focus on getting their servers ready for the experiment and on performing the detailed predictions.
In computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode genes. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functional elements such as regulatory regions. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been sequenced.
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 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.
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.
In biochemistry, a hypothetical protein is a protein whose existence has been predicted, but for which there is a lack of experimental evidence that it is expressed in vivo. Sequencing of several genomes has resulted in numerous predicted open reading frames to which functions cannot be readily assigned. These proteins, either orphan or conserved hypothetical proteins, make up an estimated 20% to 40% of proteins encoded in each newly sequenced genome. The real evidences for the hypothetical protein functioning in the metabolism of the organism can be predicted by comparing its sequence or structure homology by considering the conserved domain analysis. Even when there is enough evidence that the product of the gene is expressed, by techniques such as microarray and mass spectrometry, it is difficult to assign a function to it given its lack of identity to protein sequences with annotated biochemical function. Nowadays, most protein sequences are inferred from computational analysis of genomic DNA sequence. Hypothetical proteins are created by gene prediction software during genome analysis. When the bioinformatic tool used for the gene identification finds a large open reading frame without a characterised homologue in the protein database, it returns "hypothetical protein" as an annotation remark.
The Fiocruz Genome Comparison Project is a collaborative effort involving Brazil's Oswaldo Cruz Institute and IBM's World Community Grid, designed to produce a database comparing the genes from many genomes with each other using SSEARCH. The program SSEARCH performs a rigorous Smith–Waterman alignment between a protein sequence and another protein sequence, a protein database, a DNA or a DNA library.
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.
GENCODE is a scientific project in genome research and part of the ENCODE scale-up project.
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.
COMBREX is a multifaceted project that includes a database of gene annotations, functional predictions and recommendations based on Active Learning principles associated with millions of genes in prokaryotic genomes.
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.
dcGO is a comprehensive ontology database for protein domains. As an ontology resource, dcGO integrates Open Biomedical Ontologies from a variety of contexts, ranging from functional information like Gene Ontology to others on enzymes and pathways, from phenotype information across major model organisms to information about human diseases and drugs. As a protein domain resource, dcGO includes annotations to both the individual domains and supra-domains.
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.
I-TASSER is a bioinformatics method for predicting three-dimensional structure model of protein molecules from amino acid sequences. It detects structure templates from the Protein Data Bank by a technique called fold recognition. The full-length structure models are constructed by reassembling structural fragments from threading templates using replica exchange Monte Carlo simulations. I-TASSER is one of the most successful protein structure prediction methods in the community-wide CASP experiments.
References
- ↑ Predrag, Radivojac; 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.
- ↑ Bernal, Axel; Uy Ear; Nikos Kyrpides (2001). "Genomes OnLine Database (GOLD): a monitor of genome projects world-wide". Nucleic Acids Research. 29 (1): 126–127. doi:10.1093/nar/29.1.126. PMC 29859 . PMID 11125068.
- ↑ Friedberg, Iddo; Martin Jambon; Adam Godzik (June 2006). "New avenues in protein function prediction". Protein Science. 15 (6): 1527–1529. doi:10.1110/ps.062158406. PMC 2242544 . PMID 16731984.
- ↑ Rodrigues, Ana; Barry Grant; Adam Godzik; Iddo Friedberg (2007). "The 2006 Automated Function Prediction Meeting". Bioinformatics. 8 (Suppl 4): S1–4. doi: 10.1186/1471-2105-8-s4-s1 . PMC 1892079 . PMID 17570143.
- ↑ Gillis, Jesse; Paul Pavlidis (April 2013). "Characterizing the state of the art in the computational assignment of gene function: lessons from the first critical assessment of functional annotation (CAFA)" (PDF). BMC Bioinformatics. 14 (Suppl 3): S15. doi: 10.1186/1471-2105-14-s3-s15 . PMC 3633048 . PMID 23630983.