Multilocus variable number of tandem repeat analysis reveals multiple introductions in Spain of Xanthomonas arboricola pv. Pruni, the causal agent of bacterial spot disease of stone fruits and almond

Xanthomonas arboricola pv. pruni is the causal agent of the bacterial spot disease of stone fruits, almond and some ornamental Prunus species. In Spain it was first detected in 2002 and since then, several outbreaks have occurred in different regions affecting mainly Japanese plum, peach and almond, both in commercial orchards and nurseries. As the origin of the introduction(s) was unknown, we have assessed the genetic diversity of 239 X. arboricola pv. pruni strains collected from 11 Spanish provinces from 2002 to 2013 and 25 reference strains from international collections. We have developed an optimized multilocus variable number of tandem repeat analysis (MLVA) scheme targeting 18 microsatellites and five minisatellites. A high discriminatory power was achieved since almost 50% of the Spanish strains were distinguishable, confirming the usefulness of this genotyping technique at small spatio-temporal scales. Spanish strains grouped in 18 genetic clusters (conservatively delineated so that each cluster contained haplotype networks linked by up to quadruple-locus variations). Furthermore, pairwise comparisons among populations from different provinces showed a strong genetic differentiation. Our results suggest multiple introductions of this pathogen in Spain and redistribution through contaminated nursery propagative plant material

Unsupervised gene network inference with decision trees and Random forests

In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametric, have good scalability, and have very few parameters. In particular, we describe in detail the GENIE3 algorithm, a state-of-the-art method for GRN inference