DIVIS: a semantic DIstance to improve the VISualisation of heterogeneous phenotypic datasets.

BACKGROUND: Thanks to the wider spread of high-throughput experimental techniques, biologists are accumulating large amounts of datasets which often mix quantitative and qualitative variables and are not always complete, in particular when they regard phenotypic traits. In order to get a first insight into these datasets and reduce the data matrices size scientists often rely on multivariate analysis techniques. However such approaches are not always easily practicable in particular when faced with mixed datasets. Moreover displaying large numbers of individuals leads to cluttered visualisations which are difficult to interpret. RESULTS: We introduced a new methodology to overcome these limits. Its main feature is a new semantic distance tailored for both quantitative and qualitative variables which allows for a realistic representation of the relationships between individuals (phenotypic descriptions in our case). This semantic distance is based on ontologies which are engineered to represent real-life knowledge regarding the underlying variables. For easier handling by biologists, we incorporated its use into a complete tool, from raw data file to visualisation. Following the distance calculation, the next steps performed by the tool consist in (i) grouping similar individuals, (ii) representing each group by emblematic individuals we call archetypes and (iii) building sparse visualisations based on these archetypes. Our approach was implemented as a Python pipeline and applied to a rosebush dataset including passport and phenotypic data. CONCLUSIONS: The introduction of our new semantic distance and of the archetype concept allowed us to build a comprehensive representation of an incomplete dataset characterised by a large proportion of qualitative data. The methodology described here could have wider use beyond information characterizing organisms or species and beyond plant science. Indeed we could apply the same approach to any mixed dataset.

Diversity and selection of the continuous-flowering gene, RoKSN, in rose.

Blooming seasonality is an important trait in ornamental plants and was selected by humans. Wild roses flower only in spring whereas most cultivated modern roses can flower continuously. This trait is explained by a mutation of a floral repressor gene, RoKSN, a TFL1 homologue. In this work, we studied the origin, the diversity and the selection of the RoKSN gene. We analyzed 270 accessions, including wild and old cultivated Asian and European roses as well as modern roses. By sequencing the RoKSN gene, we proposed that the allele responsible for continuous-flowering, RoKSNcopia, originated from Chinese wild roses (Indicae section), with a recent insertion of the copia element. Old cultivated Asian roses with the RoKSNcopia allele were introduced in Europe, and the RoKSNcopia allele was progressively selected during the 19th and 20th centuries, leading to continuous-flowering modern roses. Furthermore, we detected a new allele, RoKSNA181, leading to a weak reblooming. This allele encodes a functional floral repressor and is responsible for a moderate accumulation of RoKSN transcripts. A transient selection of this RoKSNA181 allele was observed during the 19th century. Our work highlights the selection of different alleles at the RoKSN locus for recurrent blooming in rose.

Nineteenth century French rose (Rosa sp.) germplasm shows a shift over time from a European to an Asian genetic background.

Hybridization with introduced genetic resources is commonly practiced in ornamental plant breeding to introgress desired traits. The 19th century was a golden age for rose breeding in France. The objective here was to study the evolution of rose genetic diversity over this period, which included the introduction of Asian genotypes into Europe. A large sample of 1228 garden roses encompassing the conserved diversity cultivated during the 18th and 19th centuries was genotyped with 32 microsatellite primer pairs. Its genetic diversity and structure were clarified. Wide diversity structured in 16 genetic groups was observed. Genetic differentiation was detected between ancient European and Asian accessions, and a temporal shift from a European to an Asian genetic background was observed in cultivated European hybrids during the 19th century. Frequent crosses with Asian roses throughout the 19th century and/or selection for Asiatic traits may have induced this shift. In addition, the consistency of the results with respect to a horticultural classification is discussed. Some horticultural groups, defined according to phenotype and/or knowledge of their pedigree, seem to be genetically more consistent than others, highlighting the difficulty of classifying cultivated plants. Therefore, the horticultural classification is probably more appropriate for commercial purposes rather than genetic relatedness, especially to define preservation and breeding strategies.

On the characterization of flowering curves using Gaussian mixture models.

In this paper, we develop a statistical methodology applied to the characterization of flowering curves using Gaussian mixture models. Our study relies on a set of rosebushes flowering data, and Gaussian mixture models are mainly used to quantify the reblooming properties of each one. In this regard, we also suggest our own selection criterion to take into account the lack of symmetry of most of the flowering curves. Three classes are created on the basis of a principal component analysis conducted on a set of reblooming indicators, and a subclassification is made using a longitudinal k-means algorithm which also highlights the role played by the precocity of the flowering. In this way, we obtain an overview of the correlations between the features we decided to retain on each curve. In particular, results suggest the lack of correlation between reblooming and flowering precocity. The pertinent indicators obtained in this study will be a first step towards the comprehension of the environmental and genetic control of these biological processes.