Recent advances in gene function prediction using context-specific coexpression networks in plants

Abstract

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks—created by integrating multiple expression datasets—connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional “global” to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

Hormonal Regulation and Crosstalk of Auxin/Cytokinin Signaling Pathways in Potatoes In Vitro and in Relation to Vegetation or Tuberization Stages

“…The co-expression was long assumed to be a feature of paralog genes belonging to the same family, which means a uniform reaction to the defined stimulus [ 57 , 58 ]. We made an attempt to characterize at least qualitatively the extent of co-expression (functional clustering) of genes under study.…”

“…To conclude, the results presented in Figure 5 show that indeed, many redundant paralogous genes respond to hormonal stimulus with clusterized co-expression. However, the expectation of obligatory co-expression of the majority of paralogs belonging to the same gene clade [ 57 , 58 ] is not always justified. Sucrose exerts strong and variable effects on the responses of gene clusters and individual genes to hormone treatment.…”

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

“…Integration of such transcriptome-level datasets for inference of GRNs remains a feasible approach ( Razaghi-Moghadam and Nikoloski, 2020 ). Transcriptome-based network inference techniques have also shown great promise in accelerating in silico gene discovery for in planta gene validation in plants ( Li et al, 2015 ; Gupta and Pereira, 2019 ; Haque et al, 2019 ).…”

Optimising the use of gene expression data to predict plant metabolic pathway memberships

“…The utility of this strategy has been shown in some single‐gene studies (Hirai et al ., 2007; Righetti et al ., 2015), but its accuracy on a genome‐wide scale is unclear. The second strategy is unsupervised machine learning, in which genes are first grouped into co‐expression clusters and then genes of unknown function are assigned functions based on genes with known functions over‐represented within the same cluster (Mutwil et al ., 2011; Uygun et al ., 2016; Gupta & Pereira, 2019). The third strategy is supervised machine learning in which the function of a gene is predicted using models learned from the expression profiles of genes with known functions.…”