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BackgroundTo sustain the global requirements for food and renewable resources, unraveling the molecular networks underlying plant growth is becoming pivotal. Although several approaches to identify genes and networks involved in final organ size have been proven successful, our understanding remains fragmentary.ResultsHere, we assessed variation in 103 lines of the Zea mays B73xH99 RIL population for a set of final leaf size and whole shoot traits at the seedling stage, complemented with measurements capturing growth dynamics, and cellular measurements. Most traits correlated well with the size of the division zone, implying that the molecular basis of final leaf size is already defined in dividing cells of growing leaves. Therefore, we searched for association between the transcriptional variation in dividing cells of the growing leaf and final leaf size and seedling biomass, allowing us to identify genes and processes correlated with the specific traits. A number of these genes have a known function in leaf development. Additionally, we illustrated that two independent mechanisms contribute to final leaf size, maximal growth rate and the duration of growth.ConclusionsUntangling complex traits such as leaf size by applying in-depth phenotyping allows us to define the relative contributions of the components and their mutual associations, facilitating dissection of the biological processes and regulatory networks underneath.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0735-9) contains supplementary material, which is available to authorized users.

Graphical User Interface; Executable JAVA archive file, source code, supplementary information and sample files can be downloaded from the website: http://www.kermit.ugent.be/taxongap

Leaves are vital organs for biomass and seed production because of their role in the generation of metabolic energy and organic compounds. A better understanding of the molecular networks underlying leaf development is crucial to sustain global requirements for food and renewable energy. Here, we combined transcriptome profiling of proliferative leaf tissue with indepth phenotyping of the fourth leaf at later stages of development in 197 recombinant inbred lines of two different maize (Zea mays) populations. Previously, correlation analysis in a classical biparental mapping population identified 1,740 genes correlated with at least one of 14 traits. Here, we extended these results with data from a multiparent advanced generation intercross population. As expected, the phenotypic variability was found to be larger in the latter population than in the biparental population, although general conclusions on the correlations among the traits are comparable. Data integration from the two diverse populations allowed us to identify a set of 226 genes that are robustly associated with diverse leaf traits. This set of genes is enriched for transcriptional regulators and genes involved in protein synthesis and cell wall metabolism. In order to investigate the molecular network context of the candidate gene set, we integrated our data with publicly available functional genomics data and identified a growth regulatory network of 185 genes. Our results illustrate the power of combining in-depth phenotyping with transcriptomics in mapping populations to dissect the genetic control of complex traits and present a set of candidate genes for use in biomass improvement

To increase both the yield potential and stability of crops, integrated breeding strategies are used that have mostly a direct genetic basis, but the utility of epigenetics to improve complex traits is unclear. A better understanding of the status of the epigenome and its contribution to agronomic performance would help in developing approaches to incorporate the epigenetic component of complex traits into breeding programs. Starting from isogenic canola (Brassica napus) lines, epilines were generated by selecting, repeatedly for three generations, for increased energy use efficiency and drought tolerance. These epilines had an enhanced energy use efficiency, drought tolerance, and nitrogen use efficiency. Transcriptome analysis of the epilines and a line selected for its energy use efficiency solely revealed common differentially expressed genes related to the onset of stress tolerance-regulating signaling events. Genes related to responses to salt, osmotic, abscisic acid, and drought treatments were specifically differentially expressed in the droughttolerant epilines. The status of the epigenome, scored as differential trimethylation of lysine-4 of histone 3, further supported the phenotype by targeting drought-responsive genes and facilitating the transcription of the differentially expressed genes. From these results, we conclude that the canola epigenome can be shaped by selection to increase energy use efficiency and stress tolerance. Hence, these findings warrant the further development of strategies to incorporate epigenetics into breeding.The need to improve crop yield in both quantity (yield potential) and stability (actual yield) to meet the increasing demand for food, feed, and plant-derived materials is a major challenge. Very different complementary technologies are used to optimize yield and to develop crops with increased resilience against adverse environmental growth conditions (Botella et al., 2008;Cattivelli et al., 2008; Jhaet al., 2014). Plant breeding programs are a basic component of this improvement process encompassing a wide range of technologies, such as exploration of the genetic potential by intraspecific and interspecific crosses, combination of genetic pools in hybrid breeding, mutational breeding, molecular breeding, and transgene technologies.Rather recently, epigenetics has been investigated as a potential breeding platform (Springer, 2013). Epigenetic variations or heritable changes in gene expression that are not linked to changes in the DNA sequence, but associated with differences in DNA methylation or histone modifications, provide an alternative source of phenotypic variability. A vast and growing number of studies implicate DNA methylation and histone modification in the modulation of gene expression in general, control of developmental transitions, and plant responses to biotic 1 This work was supported by the Agency for Innovation by Science and Technology (grant no. IWT100268), the Ghent University Special Research Fund (grant no. 01J11311), the European Training and...

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BackgroundMachine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.ResultsIn view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model.ConclusionsFAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.