The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern-matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generation sequencing data have been developed to improve the performances of pattern-matching. However, such tools have not yet been comprehensively evaluated in plants. Hence, we developed a new streamlined method aiming at predicting CREs and target genes of plant TFs in specific organs or conditions. Our approach implements a supervised machine learning strategy, which allows to learn decision rule models using TF ChIP-chip/seq experimental data. Different layers of genomic features were integrated in predictive models: the position on the gene, the DNA-sequence conservation, the chromatin state, and various cis-regulatory element footprints. Among the tested features, the chromatin features were crucial for improving the accuracy of the method. Furthermore, we evaluated the transferability of predictive models across TFs, organs and species. Finally, we validated our method by correctly inferring the target genes of key TF controlling metabolite biosynthesis at the organ-level in Arabidopsis. We developed a tool -Wimtrap- to reproduce our approach in plant species and conditions/organs for which ChIP-chip/seq data are available. Wimtrap is a user-friendly R package that supports a R-shiny web interface and is provided with pre-built models that can be used to quickly get predictions of CREs and TF gene targets in different organs or conditions in Arabidopsis thaliana, Solanum lycopersicum, Oryza sativa, and Zea mays.

Little is known about the relationship between nutrition and the circadian clock in plants. The first global transcriptomic study in plants of the response to magnesium deficiency (–Mg) revealed that the circadian clock was affected in the Arabidopsis thaliana model species. Interactions between the circadian clock and Mg status were here investigated in the light of recent knowledge. We highlight the wide disturbances caused by –Mg within the central oscillator and, reciprocally, the probable pervasive influence of the circadian clock on the response to –Mg. We provide evidence that light signalling pathways are likely to be involved in the input of Mg status to the circadian oscillator and that they interact with the circadian clock to coregulate an important part of the transcriptomic response. We further studied PIF3 LIKE 1 (PIL1) because it strongly and early responded, before the core genes of the circadian oscillator, and was a representative regulator of light signalling that interacts with the circadian oscillator. Furthermore, the far‐red light‐responsive genes, which are related to PIL1, were more enriched among the –Mg‐deregulated genes than those responding to red, blue and intense lights. Finally, pil1 mutants had an altered response to –Mg notably by losing the upregulation of PSEUDO‐RESPONSE REGULATOR 9, a core circadian oscillator. In short, we have further characterised the interactions between the Mg status and the circadian clock and identified the involvement of light signalling pathways in the response to Mg status. In particular, we have illustrated the role of a light‐signalling component in the regulation of the circadian oscillator and physiological processes during Mg starvation.