Gene co‐expression analysis has emerged in the past 5 years as a powerful tool for gene function prediction. In essence, co‐expression analysis asks the question ‘what are the genes that are co‐expressed, that is, those that show similar expression profiles across many experiments, with my gene of interest?’. Genes that are highly co‐expressed may be involved in the biological process or processes of the query gene. This review describes the tools that are available for performing such analyses, how each of these perform, and also discusses statistical issues including how normalization of gene expression data can influence co‐expression results, calculation of co‐expression scores andPvalues, and the influence of data sets used for co‐expression analysis. Finally, examples from the literature will be presented, wherein co‐expression has been used to corroborate and discover various aspects of plant biology.

The model organism Arabidopsis thaliana is readily used in basic research due to resource availability and relative speed of data acquisition. A major goal is to transfer acquired knowledge from Arabidopsis to crop species. However, the identification of functional equivalents of well-characterized Arabidopsis genes in other plants is a nontrivial task. It is well documented that transcriptionally coordinated genes tend to be functionally related and that such relationships may be conserved across different species and even kingdoms. To exploit such relationships, we constructed whole-genome coexpression networks for Arabidopsis and six important plant crop species. The interactive networks, clustered using the HCCA algorithm, are provided under the banner PlaNet (http://aranet.mpimp-golm.mpg.de). We implemented a comparative network algorithm that estimates similarities between network structures. Thus, the platform can be used to swiftly infer similar coexpressed network vicinities within and across species and can predict the identity of functional homologs. We exemplify this using the PSA-D and chalcone synthase-related gene networks. Finally, we assessed how ontology terms are transcriptionally connected in the seven species and provide the corresponding MapMan term coexpression networks. The data support the contention that this platform will considerably improve transfer of knowledge generated in Arabidopsis to valuable crop species.

A vital quest in biology is comprehensible visualization and interpretation of correlation relationships on a genome scale. Such relationships may be represented in the form of networks, which usually require disassembly into smaller manageable units, or clusters, to facilitate interpretation. Several graph-clustering algorithms that may be used to visualize biological networks are available. However, only some of these support weighted edges, and none provides good control of cluster sizes, which is crucial for comprehensible visualization of large networks. We constructed an interactive coexpression network for the Arabidopsis (Arabidopsis thaliana) genome using a novel Heuristic Cluster Chiseling Algorithm (HCCA) that supports weighted edges and that may control average cluster sizes. Comparative clustering analyses demonstrated that the HCCA performed as well as, or better than, the commonly used Markov, MCODE, and k-means clustering algorithms. We mapped MapMan ontology terms onto coexpressed node vicinities of the network, which revealed transcriptional organization of previously unrelated cellular processes. We further explored the predictive power of this network through mutant analyses and identified six new genes that are essential to plant growth. We show that the HCCA-partitioned network constitutes an ideal “cartographic” platform for visualization of correlation networks. This approach rapidly provides network partitions with relative uniform cluster sizes on a genome-scale level and may thus be used for correlation network layouts also for other species.

The avocado, Persea americana , is a fruit crop of immense importance to Mexican agriculture with an increasing demand worldwide. Avocado lies in the anciently diverged magnoliid clade of angiosperms, which has a controversial phylogenetic position relative to eudicots and monocots. We sequenced the nuclear genomes of the Mexican avocado race, P. americana var. drymifolia , and the most commercially popular hybrid cultivar, Hass, and anchored the latter to chromosomes using a genetic map. Resequencing of Guatemalan and West Indian varieties revealed that ∼39% of the Hass genome represents Guatemalan source regions introgressed into a Mexican race background. Some introgressed blocks are extremely large, consistent with the recent origin of the cultivar. The avocado lineage experienced 2 lineage-specific polyploidy events during its evolutionary history. Although gene-tree/species-tree phylogenomic results are inconclusive, syntenic ortholog distances to other species place avocado as sister to the enormous monocot and eudicot lineages combined. Duplicate genes descending from polyploidy augmented the transcription factor diversity of avocado, while tandem duplicates enhanced the secondary metabolism of the species. Phenylpropanoid biosynthesis, known to be elicited by Colletotrichum (anthracnose) pathogen infection in avocado, is one enriched function among tandems. Furthermore, transcriptome data show that tandem duplicates are significantly up- and down-regulated in response to anthracnose infection, whereas polyploid duplicates are not, supporting the general view that collections of tandem duplicates contribute evolutionarily recent “tuning knobs” in the genome adaptive landscapes of given species.