Tracing 100 million years of grass genome evolutionary plasticity.

Grasses derive from a family of monocotyledonous plants that includes crops of major economic importance such as wheat, rice, sorghum and barley, sharing a common ancestor some 100 million years ago. The genomic attributes of plant adaptation remain obscure and the consequences of recurrent whole genome duplications (WGD) or polyploidization events, a major force in plant evolution, remain largely speculative. We conducted a comparative analysis of omics data from ten grass species to unveil structural (inversions, fusions, fissions, duplications, substitutions) and regulatory (expression and methylation) basis of genome plasticity, as possible attributes of plant long lasting evolution and adaptation. The present study demonstrates that diverged polyploid lineages sharing a common WGD event often present the same patterns of structural changes and evolutionary dynamics, but these patterns are difficult to generalize across independent WGD events as a result of non-WGD factors such as selection and domestication of crops. Polyploidy is unequivocally linked to the evolutionary success of grasses during the past 100 million years, although it remains difficult to attribute this success to particular genomic consequences of polyploidization, suggesting that polyploids harness the potential of genome duplication, at least partially, in lineage-specific ways. Overall, the present study clearly demonstrates that post-polyploidization reprogramming is more complex than traditionally reported in investigating single species and calls for a critical and comprehensive comparison across independently polyploidized lineages.

Genomic data integration tutorial, a plant case study.

BACKGROUND: The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts. RESULTS: To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration. CONCLUSION: The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.