Functional genomics, challenges and perspectives for the future

With the advent of high throughput sequencing, proteomics and metabolomics tools, functional genomics and systems biology have become a central platform in plant sciences and beyond. The numerous plant genomes sequenced (and in progress of being sequenced) have generated a wealth of information on the composition, structure and organization of different plant genomes. This has led to the identification and annotation of 10 000s of plant genes. Nevertheless, determining the function of these genes, the networks they are involved in, and the genome as a whole remains a major challenge for modern plant research. The functional genomics/systems biology platform is an extremely complex and powerful approach to determine the function of individual genes, pathways, networks and ultimately entire genomes (Fig. 1). The principal premise behind this approach is to evaluate and study the entire cell or organism as a system and understand how different biological processes occur within this system, how they are controlled and how they are executed. To study the system as a whole it is perturbed by, for a example, a mutation or a change in growth conditions, and is then studied using different sub-platforms that determine the response of the entire genome, transcriptome, metabolome, fluxome, ionome and any additional technique that address the entire set of components in the cell (e.g. Hrmova and Fincher 2009, Baxter 2010, Davies et al. 2010, Edwards and Batley 2010, Pedreschi et al. 2010, Saito and Matsuda 2010, Araújo et al. 2012, Berkman et al. 2012). Each sub-platform generates a large data set that is curated and stored in a database and different bioinformatics tools are then used to integrate the data, mine for specific genes, pathways and networks, annotate and generate a visualization output that attempts to link all the different components involved (e.g. Mochida and Shinozaki 2010, Higashi and Saito 2013). Modeling is then applied in an attempt to explain the dynamic behavior of the entire system and generate new hypotheses, propose additional perturbations and assign a function to the different cellular components involved (e.g. Moreno-Risueno et al. 2010, Yin and Struik 2010). Of course assigning a function requires a phenotype and this is addressed by the phenomics platform (e.g. Kuromori et al. 2009, Kondou et al. 2010, Furbank and Tester 2011), as well as by the initiation of a new cycle of experiments using a slightly different perturbation of the system. The power of this approach lies in the fact that each individual perturbation results in a response that involves the entire network/system of the cell and encompasses 1000s of different genes, transcripts and metabolites, generating relational data sets that could be linked and produce multiple correlations that imply a function. The function is then tested via further perturbations and phenotypic analysis that could be a visually measured phenotype, such as altered growth or tolerance to a specific abiotic stress, or for example a metabolic phenotype that would be determined with one of the different platforms. The drawback of applying this approach to different biological systems is of course that it is extremely expensive and complex. Nevertheless, recent advances in technology had significantly reduced the cost of whole genome and transcriptome sequencing opening many new avenues of research. Perhaps the most obvious advancement in technology in recent years was the development of Next Generation (NextGen) DNA sequencing platforms. These have increased our ability to sequence plant genomes (more than 100 plant genomes have already been fully sequenced), and decreased the cost of whole genome and whole transcriptome sequencing to a level that is highly accessible for individual researchers (e.g. Appleby et al. 2009, Edwards et al. 2013). The implications of this advancement are immense and far reaching in many fields including medicine, agriculture, public health, defense and more. In addition to the sequence data that generates a physical map of the genome, NextGen