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Abstract
<p class="first" id="d7855590e84">Unraveling gene function is pivotal to understanding
the signaling cascades that control
plant development and stress responses. As experimental profiling is costly and labor
intensive, there is a clear need for high-confidence computational annotation. In
contrast to detailed gene-specific functional information, transcriptomics data are
widely available for both model and crop species. Here, we describe a novel automated
function prediction method, which leverages complementary information from multiple
expression datasets by analyzing study-specific gene co-expression networks. First,
we benchmarked the prediction performance on recently characterized Arabidopsis thaliana
genes, and showed that our method outperforms state-of-the-art expression-based approaches.
Next, we predicted biological process annotations for known (n = 15 790) and unknown
(n = 11 865) genes in A. thaliana and validated our predictions using experimental
protein-DNA and protein-protein interaction data (covering >220 000 interactions
in
total), obtaining a set of high-confidence functional annotations. Our method assigned
at least one validated annotation to 5054 (42.6%) unknown genes, and at least one
novel validated function to 3408 (53.0%) genes with computational annotations only.
These omics-supported functional annotations shed light on a variety of developmental
processes and molecular responses, such as flower and root development, defense responses
to fungi and bacteria, and phytohormone signaling, and help fill the information gap
on biological process annotations in Arabidopsis. An in-depth analysis of two context-specific
networks, modeling seed development and response to water deprivation, shows how previously
uncharacterized genes function within the respective networks. Moreover, our automated
function prediction approach can be applied in future studies to facilitate gene discovery
for crop improvement.
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