SuperFly: a comparative database for quantified spatio-temporal gene expression patterns in early dipteran embryos

We present SuperFly (http://superfly.crg.eu), a relational database for quantified spatio-temporal expression data of segmentation genes during early development in different species of dipteran insects (flies, midges and mosquitoes). SuperFly has a special focus on emerging non-drosophilid model sy...

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Bibliographic Details
Published in:Nucleic acids research 2015-01, Vol.43 (Database issue), p.D751-D755
Main Authors: Cicin-Sain, Damjan, Pulido, Antonio Hermoso, Crombach, Anton, Wotton, Karl R, Jiménez-Guri, Eva, Taly, Jean-François, Roma, Guglielmo, Jaeger, Johannes
Format: Article
Language:English
Subjects:
Animals
Bases de dades
Database Issue
Databases, Genetic
Diptera - embryology
Diptera - genetics
Diptera - metabolism
Drosophila melanogaster - genetics
Drosòfila
Embryo, Nonmammalian - metabolism
Gene Expression
Genètica
Insectes
Internet
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Summary:We present SuperFly (http://superfly.crg.eu), a relational database for quantified spatio-temporal expression data of segmentation genes during early development in different species of dipteran insects (flies, midges and mosquitoes). SuperFly has a special focus on emerging non-drosophilid model systems. The database currently includes data of high spatio-temporal resolution for three species: the vinegar fly Drosophila melanogaster, the scuttle fly Megaselia abdita and the moth midge Clogmia albipunctata. At this point, SuperFly covers up to 9 genes and 16 time points per species, with a total of 1823 individual embryos. It provides an intuitive web interface, enabling the user to query and access original embryo images, quantified expression profiles, extracted positions of expression boundaries and integrated datasets, plus metadata and intermediate processing steps. SuperFly is a valuable new resource for the quantitative comparative study of gene expression patterns across dipteran species. Moreover, it provides an interesting test set for systems biologists interested in fitting mathematical gene network models to data. Both of these aspects are essential ingredients for progress toward a more quantitative and mechanistic understanding of developmental evolution.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gku1142