Open Access Highly Accessed Research article

Integrated functional networks of process, tissue, and developmental stage specific interactions in Arabidopsis thaliana

Ana Pop12, Curtis Huttenhower3, Anjali Iyer-Pascuzzi4, Philip N Benfey4 and Olga G Troyanskaya12*

Author Affiliations

1 Computer Science Department, Princeton University, Princeton, NJ, USA

2 Lewis-Sigler Institute for Integrative Genomics, Princeton University, NJ, USA

3 Biostatistics Department, Harvard School of Public Health, Boston, MA, USA

4 Department of Biology and Center for Systems Biology, Duke University, Durham, NC, USA

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BMC Systems Biology 2010, 4:180  doi:10.1186/1752-0509-4-180

Published: 31 December 2010



Recent years have seen an explosion in plant genomics, as the difficulties inherent in sequencing and functionally analyzing these biologically and economically significant organisms have been overcome. Arabidopsis thaliana, a versatile model organism, represents an opportunity to evaluate the predictive power of biological network inference for plant functional genomics.


Here, we provide a compendium of functional relationship networks for Arabidopsis thaliana leveraging data integration based on over 60 microarray, physical and genetic interaction, and literature curation datasets. These include tissue, biological process, and development stage specific networks, each predicting relationships specific to an individual biological context. These biological networks enable the rapid investigation of uncharacterized genes in specific tissues and developmental stages of interest and summarize a very large collection of A. thaliana data for biological examination. We found validation in the literature for many of our predicted networks, including those involved in disease resistance, root hair patterning, and auxin homeostasis.


These context-specific networks demonstrate that highly specific biological hypotheses can be generated for a diversity of individual processes, developmental stages, and plant tissues in A. thaliana. All predicted functional networks are available online at webcite.