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Open Access Highly Accessed Methodology article

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

Wenlu Zhang1, Daming Feng1, Rongjian Li1, Andrey Chernikov1, Nikos Chrisochoides1, Christopher Osgood2, Charlotte Konikoff3, Stuart Newfeld4, Sudhir Kumar345 and Shuiwang Ji1*

Author Affiliations

1 Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA

2 Department of Biological Sciences, Old Dominion University, Norfolk, VA 23529, USA

3 Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA

4 School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA

5 Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia

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BMC Bioinformatics 2013, 14:372  doi:10.1186/1471-2105-14-372

Published: 28 December 2013

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/ webcite.

Conclusions

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