Computational aspects underlying genome to phenome analysis in plants.

Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features

The Plant Journal

SUMMARY Wild relatives of tomato are a valuable source of natural variation in tomato breeding, as many can be hybridized to the cultivated species (Solanum lycopersicum). Several, including S. lycopersicoides, have been crossed to S. lycopersicum for the development of ordered introgression lines (ILs), facilitating breeding for desirable traits. Despite the utility of these wild relatives and their associated ILs, few finished genome sequences have been produced to aid genetic and genomic studies. Here we report a chromosome-scale genome assembly for S. lycopersicoides LA2951, which contains 37,938 predicted protein-coding genes. With the aid of this genome assembly, we have precisely delimited the boundaries of the S. lycopersicoides introgressions in a set of S. lycopersicum cv. VF36 x LA2951 ILs. We demonstrate the usefulness of the LA2951 genome by identifying several quantitative trait loci (QTLs) for phenolics and carotenoids, including underlying candidate genes, and by investigating the genome organization and immunity-associated function of the clustered Pto gene family. In addition, syntenic analysis of R2R3MYB genes sheds light on the identity of the Aubergine locus underlying anthocyanin production. The genome sequence and IL map provide valuable resources for studying fruit nutrient/quality traits, pathogen resistance, and environmental stress tolerance