Cork oak (Quercus suber) is native to southwest Europe and northwest Africa where it plays a crucial environmental and economical role. To tackle the cork oak production and industrial challenges, advanced research is imperative but dependent on the availability of a sequenced genome. To address this, we produced the first draft version of the cork oak genome. We followed a de novo assembly strategy based on high-throughput sequence data, which generated a draft genome comprising 23,347 scaffolds and 953.3 Mb in size. A total of 79,752 genes and 83,814 transcripts were predicted, including 33,658 high-confidence genes. An InterPro signature assignment was detected for 69,218 transcripts, which represented 82.6% of the total. Validation studies demonstrated the genome assembly and annotation completeness and highlighted the usefulness of the draft genome for read mapping of high-throughput sequence data generated using different protocols. All data generated is available through the public databases where it was deposited, being therefore ready to use by the academic and industry communities working on cork oak and/or related species.

Gene activity is largely controlled by transcriptional regulation through the action of transcription factors and other regulators. QsMYB1 is a member of the R2R3-MYB transcription factor family related to secondary growth, and in particular, with the cork development process. In order to identify the putative gene targets of QsMYB1 across the cork oak genome we developed a ChIP-Seq strategy. Results provide direct evidence that QsMY1B targets genes encoding for enzymes involved in the lignin and suberin pathways as well as gene encoding for ABCG transporters and LTPs implicated in the transport of monomeric suberin units across the cellular membrane. These results highlight the role of QsMYB1 as a regulator of lignin and suberin biosynthesis, transport and assembly. To our knowledge, this work constitutes the first ChIP-Seq experiment performed in cork oak, a non-model plant species with a long-life cycle, and these results will contribute to deepen the knowledge about the molecular mechanisms of cork formation and differentiation.

Forests are essential resources on a global scale, not only for the ecological benefits, but also for economical and landscape purposes. However, in recent years, a large number of forest species have suffered a serious decline, with maritime pine being one of the most affected. In Portugal, the maritime pine forest has been devastated by the pine wood nematode (PWN), the causal agent of pine wilt disease. In this study, RNA-Seq data was used to characterize the maritime pine response to infection with PWN, by determining the differentially expressed genes and identifying the regulatory networks and pathways associated. The analyses showed clear differences between an early response that occurs immediately after inoculation and a late response that is observed seven days after inoculation. Moreover, differentially expressed genes related to secondary metabolism, oxidative stress and defense against pathogen infection were identified over different time points. These results provide new insights about the molecular mechanisms and metabolic pathways involved in the response of Pinus pinaster against PWN infection, which will be a useful resource in follow-up studies and for future breeding programs to select plants with lower susceptibility to this disease.

Background: The adoption of whole-genome sequencing in genetic screens has facilitated the detection of genetic variation in the intronic regions of genes, far from annotated splice sites. However, selecting an appropriate computational tool to discriminate functionally relevant genetic variants from those with no effect is challenging, particularly for deep intronic regions where independent benchmarks are scarce. Results: In this study, we have provided an overview of the computational methods available and the extent to which they can be used to analyze deep intronic variation. We leveraged diverse datasets to extensively evaluate tool performance across different intronic regions, distinguishing between variants that are expected to disrupt splicing through different molecular mechanisms. Notably, we compared the performance of SpliceAI, a widely used sequence-based deep learning model, with that of more recent methods that extend its original implementation. We observed considerable differences in tool performance depending on the region considered, with variants generating cryptic splice sites being better predicted than those that potentially affect splicing regulatory elements. Finally, we devised a novel quantitative assessment of tool interpretability and found that tools providing mechanistic explanations of their predictions are often correct with respect to the ground - information, but the use of these tools results in decreased predictive power when compared to black box methods. Conclusions: Our findings translate into practical recommendations for tool usage and provide a reference framework for applying prediction tools in deep intronic regions, enabling more informed decision-making by practitioners.