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Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster

Samsonova, A.A., Niranjan, M., Russell, S. and Brazma, A. (2007) Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster. PLoS Computational Biology, 3 (7). e144. ISSN 1553-734X

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Abstract

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed genespecific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.

Item Type: Article
Copyright, Publisher and Additional Information: � 2007 Samsonova et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Sheffield Import
Date Deposited: 27 Oct 2009 16:33
Last Modified: 25 Jul 2014 01:47
Published Version: http://dx.doi.org/10.1371/journal.pcbi.0030144
Status: Published
Publisher: Public Library of Science
Refereed: Yes
Identification Number: doi: 10.1371/journal.pcbi.0030144
URI: http://eprints.whiterose.ac.uk/id/eprint/10037

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