Seung Yon Rhee1 and Marek Mutwil2
1 Carnegie Institution for Science, Department of Plant Biology, 260 Panama St, Stanford, CA 94305, USA
2 Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany
Corresponding authors: Seung Yon Rhee, Marek Mutwil
The great recent progress made in identifying the molecular parts lists of organisms revealed the paucity of our understanding of what most of the parts do. In this review, we introduce computational and statistical approaches and omics data used for inferring gene function in plants, with an emphasis on network-based inference. We also discuss caveats associated with network-based function predictions such as performance assessment, annotation propagation, the guilt-by-association concept, and the meaning of hubs. Finally, we note the current limitations and possible future directions such as the need for gold standard data from several species, unified access to data and tools, quantitative comparison of data and tool quality, and high-throughput experimental validation platforms for systematic gene function elucidation in plants.
Keywords : function prediction; omics; big data; networks; co-expression; co-function