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.
| Original language | English (US) |
|---|---|
| Article number | 372 |
| Journal | BMC Bioinformatics |
| Volume | 14 |
| DOIs | |
| State | Published - Dec 28 2013 |
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ASJC Scopus subject areas
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Applied Mathematics
- Structural Biology
Cite this
A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis. / Zhang, Wenlu; Feng, Daming; Li, Rongjian; Chernikov, Andrey; Chrisochoides, Nikos; Osgood, Christopher; Konikoff, Charlotte; Newfeld, Stuart; Kumar, Sudhir; Ji, Shuiwang.
In: BMC Bioinformatics, Vol. 14, 372, 28.12.2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis
AU - Zhang, Wenlu
AU - Feng, Daming
AU - Li, Rongjian
AU - Chernikov, Andrey
AU - Chrisochoides, Nikos
AU - Osgood, Christopher
AU - Konikoff, Charlotte
AU - Newfeld, Stuart
AU - Kumar, Sudhir
AU - Ji, Shuiwang
PY - 2013/12/28
Y1 - 2013/12/28
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84891013356&partnerID=8YFLogxK
U2 - 10.1186/1471-2105-14-372
DO - 10.1186/1471-2105-14-372
M3 - Article
C2 - 24373308
AN - SCOPUS:84891013356
VL - 14
JO - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
M1 - 372
ER -