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UCL Institute of Cardiovascular Science

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InformationHome page for the GO consortium (GOC)
ÌýView the current annotation statistics for UniProt GO
ÌýGuide to GO evidence codes
ÌýTutorial describing how electronic and manual annotations are made
ÌýA searchable list of 4000+ cardiovascular associated human genes
ÌýA searchable list of 91 Alzheimer's associated human genes
ÌýA searchable list of 330 Parkinson's associated human genes
ÌýCan paste in any text for highlighting with potential GO terms
ÌýDimmer et al., 2008 (pdf)ÌýThis article provides an overview of the Gene Ontology, its uses and the software available for the analysis of high-throughput data. We recommend that you analyse your data with a variety of functional analysis tools, as there will be differences in the date the annotation dataset was uploaded into the tool and the statistical packages used. The tools listed below are easy to use and generally update their datasets regularly
ÌýTutorial (pdf)This powerpoint presentation introduces the basic features of four third-party GO analysis tools; , , , and .
BrowsersFind GO terms or terms associated with specific UniProt accessions
ÌýFind GO terms, filters for species, datasource or evidence codes available
ÌýProvides a database and analysis tools for molecular interaction data
ÌýProvides manually curated macromolecular complexes from a number of key model organisms.
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Provides computational access to molecular-interaction data including protein-protein interaction (PPI) resources for use with network analysis tools such as . Now includes access to GO annotation file derived non-IMEX datasets called EBI-GOA-nonIntAct (PPI data) andÌýEBI-GOA-miRNA. These files have been made available through a collaboration between UCL and EMBL-EBI. For more details, read our web page dedicated to PSCIQUIC.

ÌýFind gene record with wide range of annotations and links
ÌýFind protein record with wide range of annotations and links
Literature search enginesLiterature search and text mining tool. Can highlight abstracts to show potential GO terms
ÌýNCBI literature search tool with multiple filter options
ÌýLinks together protein information. Search using gene symbol or accession ID
ÌýLiterature search tool that categorises results by GO terms mentioned in abstract
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Functional analysis toolsGOC tool, provides a table of enriched terms, and associated sample frequency and genes. For human analysis either input your own background dataset or leave this section blank, the tool will then use the one available with the 'select the database filter' option 'UniProtKB'
ÌýProvides a range of functional analysis tools, e.g. FatiGO and 'gene set analysis'
ÌýDisplays a wide range of outputs and graphical views
Ìýperforms statistical enrichment analysis to find over-representation of information like Gene Ontology terms. Its output is tabular graphic where ontology tree relationships of enriched terms are shown. Other output formats are available and can be used as input for other software such as via software.
ÌýDisplays the enriched GO terms within the ontology structure or as a table, can be more difficult to get to work due to pop-ups
ÌýRequires local installation. Displays a table of results related to the term and a list of all genes annotated to the term in the study set, as well as an integrated graphical display
ÌýMGI tool which provides a table of enriched terms, and the associated genes, as well as a graph of the enriched terms. For human analysis choose 'Annotation Set: goa_human [date]' option from dropdown menu
ÌýÌýOverlays enriched GO terms on a protein 'network'. Online version can only analyse short gene lists (50 genes), for longer lists use this tool as a plugin with Cytoscape. Be selective in the networks tab to avoid error prone text mined data.
Disease specific databasesContains links to locus specific mutation databases
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ÌýContains more than 250 Coronary artery disease related genes
ÌýThe Parkinson's Disease map is a knowledge repository established to describe mechanisms of PD by means of molecular networks to grasp complex relationships between the genetic and environmental risk factors.