High-resolution gene expression data from blastoderm embryos of the scuttle fly Megaselia abdita

Gap genes are involved in segment determination during early development in dipteran insects (flies, midges, and mosquitoes). We carried out a systematic quantitative comparative analysis of the gap gene network across different dipteran species. Our work provides mechanistic insights into the evolu...

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Detalles Bibliográficos
Autores: Wotton, Karl R., Jiménez Guri, Eva, Crombach, Anton, Cicin Sain, Damjan, Jaeger, Johannes, 1973-
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/25161
Acceso en línea:http://hdl.handle.net/10230/25161
http://dx.doi.org/10.1038/sdata.2015.5
Access Level:acceso abierto
Palabra clave:Insectes -- Genètica
Descripción
Sumario:Gap genes are involved in segment determination during early development in dipteran insects (flies, midges, and mosquitoes). We carried out a systematic quantitative comparative analysis of the gap gene network across different dipteran species. Our work provides mechanistic insights into the evolution of this pattern-forming network. As a central component of our project, we created a high-resolution quantitative spatio-temporal data set of gap and maternal co-ordinate gene expression in the blastoderm embryo of the non-drosophilid scuttle fly, Megaselia abdita. Our data include expression patterns in both wild-type and RNAi-treated embryos. The data-covering 10 genes, 10 time points, and over 1,000 individual embryos-consist of original embryo images, quantified expression profiles, extracted positions of expression boundaries, and integrated expression patterns, plus metadata and intermediate processing steps. These data provide a valuable resource for researchers interested in the comparative study of gene regulatory networks and pattern formation, an essential step towards a more quantitative and mechanistic understanding of developmental evolution.