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dcGO: database of domain-centric ontologies on functions, phenotypes, diseases and more
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What is dcGO (Background)
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As a biomedical ontology resource, dcGO integrates knowledge from a variety of contexts, ranging from functional information like Gene Ontology (GO) to others on enzymes and pathways, from phenotype information across major model organisms to information about human diseases and drugs. In dcGO, all Biomedical Ontologies that are not GO are collectively referred to as BO.
As a protein domain resource, dcGO includes annotations to both the individual domains and supra-domains (i.e., combinations of two or more successive domains). By default, the domain classifications are taken from the Structural Classification Of Proteins (SCOP) at both the superfamily and family levels.
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How dcGO is built (Algorithm)
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As a general method, dcGO has an automated procedure for statistically inferring associations between ontological terms and domains or combinations of domains. An automatic pipeline regularly updates dcGO on a fortnightly basis.
Similar to the concept of GO slim, dcGO has a partition procedure for deriving a reduced, more manageable version of the ontology. In dcGO, each ontology slim contains terms at four levels of increasing granularity (i.e., highly general, general, specific, and highly specific).
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How to access dcGO (Download)
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Both flat files and MySQL tables are available for download along with detailed documentation.
Domain classifications and ontologies are organized in hierarchies, and dcGO includes the facility to browse the hierarchies: SCOP Hierarchy for browsing domains, GO Hierarchy for browsing GO terms, and BO Hierarchy for browsing other terms (mostly phenotypes).
In addition to SCOP domains, GO annotations to Pfam families are also provided (see PFAM Hierarchy and PFAM2GO Download).
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Phenotype similarity network (PSnet)
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As a domain-oriented tool for understanding genotype-phenotype relationships, given one phenotype, PSnet can be used to search for similar phenotype (and other) ontological terms; these are matched on the basis of their shared domain annotations (both at the superfamily and family levels).
OBO-formated phenotype and anatomy ontologies:
- Disease Ontology, e.g., DO:cell type cancer
- Human Phenotype, e.g., PA:Leukemia
- Mouse Phenotype, e.g., MP:embryonic lethality
- Worm Phenotype, e.g., WP:early embryonic lethal
- Yeast Phenotype, e.g., YP:chromosome/plasmid maintenance
- Fly Phenotype, e.g., FP:mitotic cell cycle defective
- Fly Anatomy, e.g., FA:developing embryonic structure
- Zebrafish Anatomy, e.g., ZA:nervous system
- Xenopus Anatomy, e.g., XAN:embryo
- Arabidopsis Plant, e.g., PAN:embryo plant structure
Other ontologies with fixed-length or a much simpler hierarchy (or adapted from vocabulary):
- Enzyme Commission, e.g., EC:Histone-lysine N-methyltransferase
- DrugBank ATC_code, e.g., DB:endocrine therapy
- UniProtKB KeyWords, e.g., KW:AIDS
- UniProtKB UniPathway, e.g., UP:oxidative phosphorylation
- CTD Diseases, e.g., CD:Leukemia, Myeloid, Acute
- CTD Chemicals, e.g., CC:Aspirin or CC:Fenretinide
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Species Tree Of Life (sTOL)
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As an evolutionary-oriented tool, dcGO can be combined with sTOL to provide a phylogenentic context to function and phenotype.
The distribution, across the tree of life for all completely seqeunced genomes, can be explored of both: SCOP domains or by GO/BO term.
When browsing the sTOL tree hierarchy, enriched GO/BO terms for extant and ancestral genomes (by domainome-based enrichment analysis) will be displayed.
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dcGO Predictor
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As a knowledge predictor, dcGO uses domain-centric annotations to predict functions and other higher-order knowledge (phenotypes, diseases and more) for sequences in your query.
The dcGO Predictor relies on the assignment to a sequence in your query of a domain with associated ontology. It will only report predictions for seqeunces with significant domain assignments using the SUPERFAMILY hidden Markov model library.
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dcGO Enrichment
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For a list of input domains (e.g., a list of unusual domains found in a genome as compared to another genome) and the target ontologies (e.g., GO), the dcGO Enrichment identifies the ontology terms enriched within the input domains.
The dcGO Enrichment takes all annotatable domains (with respects to ontology) as the test background. It is able to process a list of domains containing single group or multiple groups.
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dcGOnet
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The dcGOnet is a domain network from a functional perspective, with nodes consisting of protein domains (at the superfamily/evolutionary level) and edges weighted by the semantic similarity according to dcGO annotation profile.
It also features a general methodology (and the software freely available). The predicted drug-disease-phenotype matrix provides rich targets for investigation.
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dcGO Pevo
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Plasticity potential (PP) is defined as the tendency for a list of related domains (sharing certain common characteristics; annotated by a dcGO term) to occur in different architectures (or architecture diversity), as measured by the median number of architectures per domain.
The dcGO Pevo calculates PP-scores for a list of input dcGO terms, and displays their dynamic changes during the eukaryotic genome evolution.
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( show help)
Common words (e.g., "and" and "the"), and commonly occurring but specialized words (e.g., "domain", "function") will be removed from the search.
For multi-word searches, an entire phrase (AND) search is run first. If not found, any word (OR) is used instead.
Faceted research results will be organized and linked to the relevant pages, including one or more (if found) of:
- SCOP Hierarchy for SCOP domains and their annotations;
- GO Hierarchy for GO terms and their annotated domains/supra-domains;
- BO Hierarchy for terms from Biomedical Ontologies that are not GO, and domains/supra-domains being annotated;
- PSnet for cross-linking similar phenotypes based on domain annotations shared;
- sTOL for the distribution of individual domain and GO/BO-annotated domains over the tree, and enriched GO/BO terms for extant and ancestral genomes;
- dcGO Predictor for function, phenotype and disease predictions of >80 million sequences in >2,000 genomes, UniProt and hundreds of meta-genomes.
- dcGO Enrichment for the identification of the GO/BO terms enriched within a list of input domains.
- dcGO Pevo for exploring architecture plasticity potentials of dcGO terms during eukaryotic evolution.
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