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    Description

    In two of our recent systems biology studies of forest trees we reconstructed gene networks active in wood tissue development for an undomesticated tree genus, Populus. In the first study, we used time series data to determine gene expression dynamics underlying wood formation in response to gravitational stimulus. In the second study, we integrated data from newly generated and publicly available transcriptome profiling, transcription factor binding, DNA accessibility and genome-wide association mapping experiments, to identify relationships among genes expressed during wood formation. We demonstrated that these approaches can be used for dissecting complex developmental responses in trees, and can reveal gene clusters and mechanisms influencing poorly understood developmental processes. Combining orthogonal approaches can yield better resolved gene networks, but the resulting network modules may contain large numbers of genes. This limitation reflects the difficulty in creating a variety of experimental conditions that can reveal expression and functional differences among genes within a module, thus imposing limits on the resolving power of network models in practice. To resolve networks at a finer level we are now adding a complementary approach to our work: using cross-species gene network inference. In this approach, transcriptome assemblies of two or more species are considered together to identify expression responses common to all species and also responses that are species specific. To that end here we present a new tool, fastOC, for identifying gene co-expression networks across multiple species. We provide initial evidence that the tool works effectively in calculating co-expression modules with minimal computing requirements, thus making cross-species gene network comparison practical.

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    Citation

    Zinkgraf, M.; Groover, A.; Filkov, V. 2018. Reconstructing gene networks of forest trees from gene expression data: toward higher resolution approaches. In: Kalajdziski S., Ackovska N., eds. ICT Innovations 2018. Engineering and Life Sciences. Cham, Switzerland: Springer-Cham: 3-12.

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https://www.fs.usda.gov/treesearch/pubs/58561