期刊
TRENDS IN PLANT SCIENCE
卷 19, 期 4, 页码 212-221出版社
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tplants.2013.10.006
关键词
function prediction; omics; big data; networks; co-expression; co-function
资金
- US National Science Foundation [IOS-1026003, DBI-0640769, MCB-1052348]
- US Department of Energy [BER-65472]
- Max Planck Institute for Molecular Plant Physiology
- Division Of Integrative Organismal Systems
- Direct For Biological Sciences [1026003] Funding Source: National Science Foundation
- Div Of Molecular and Cellular Bioscience
- Direct For Biological Sciences [1052348] Funding Source: National Science Foundation
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.
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