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BMC Bioinformatics. 2013 Dec 28;14:372. doi: 10.1186/1471-2105-14-372.

A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis.

Author information

1
Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. sji@cs.odu.edu.

Abstract

BACKGROUND:

Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.

RESULTS:

We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.

CONCLUSIONS:

Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.

PMID:
24373308
PMCID:
PMC3879658
DOI:
10.1186/1471-2105-14-372
[Indexed for MEDLINE]
Free PMC Article
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