Towards revealing the functions of all genes in plants.
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.
Carnegie Institution for Science, Department of Plant Biology, 260 Panama St, Stanford, CA 94305, USA. Electronic address: srhee@carnegiescience.edu.
Seung Yon
SY
Rhee
Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany. Electronic address: mutwil@mpimp-golm.mpg.de.
Marek
M
Mutwil
D000465:Algorithms; D019295:Computational Biology; D053263:Gene Regulatory Networks; D010944:Plants / Q000235:genetics*
big data; co-expression; co-function; function prediction; networks; omics