Author/Authors :
Charless C. Fowlkes، نويسنده , , Cris L. Luengo Hendriks، نويسنده , , Soile V.E. Ker?nen، نويسنده , , Gunther H. Weber، نويسنده , , Oliver Rübel، نويسنده , , Min-Yu Huang، نويسنده , , Sohail Chatoor، نويسنده , , Angela H. DePace، نويسنده , , Lisa Simirenko، نويسنده , , Clara Henriquez، نويسنده , , Amy Beaton، نويسنده , , Richard Weiszmann، نويسنده , , Susan Celniker، نويسنده , , Bernd Hamann، نويسنده , , David W. Knowles، نويسنده , , Mark D. Biggin، نويسنده , , Michael B. Eisen، نويسنده , , Jitendra Malik، نويسنده ,
Abstract :
To fully understand animal transcription networks, it is essential to accurately measure the spatial and temporal expression patterns of transcription factors and their targets. We describe a registration technique that takes image-based data from hundreds of Drosophila blastoderm embryos, each costained for a reference gene and one of a set of genes of interest, and builds a model VirtualEmbryo. This model captures in a common framework the average expression patterns for many genes in spite of significant variation in morphology and expression between individual embryos. We establish the methodʹs accuracy by showing that relationships between a pair of genesʹ expression inferred from the model are nearly identical to those measured in embryos costained for the pair. We present a VirtualEmbryo containing data for 95 genes at six time cohorts. We show that known gene-regulatory interactions can be automatically recovered from this data set and predict hundreds of new interactions.
