Microbe-ID: an open source toolbox for microbial genotyping and species identification

A machine learning algorithm for the automatic classification of Phytophthora infestans genotypes into clonal lineages

Premise

The prompt categorization of Phytophthora infestans isolates into described clonal lineages is a key tool for the management of its associated disease, potato late blight. New isolates of this pathogen are currently classified by comparing their microsatellite genotypes with characterized clonal lineages, but an automated classification tool would greatly improve this process. Here, we developed a flexible machine learning-based classifier for P. infestans genotypes.

Methods

The performance of different machine learning algorithms in classifying P. infestans genotypes into its clonal lineages was preliminarily evaluated with decreasing amounts of training data. The four best algorithms were then evaluated using all collected genotypes.

Results

mlpML, cforest, nnet, and AdaBag performed best in the preliminary test, correctly classifying almost 100% of the genotypes. AdaBag performed significantly better than the others when tested using the complete data set (Tukey HSD P < 0.001). This algorithm was then implemented in a web application for the automated classification of P. infestans genotypes, which is freely available at https://github.com/cpatarroyo/genotypeclas.

Discussion

We developed a gradient boosting-based tool to automatically classify P. infestans genotypes into its clonal lineages. This could become a valuable resource for the prompt identification of clonal lineages spreading into new regions.

Insights into evolving global populations of Phytophthora infestans via new complementary mtDNA haplotype markers and nuclear SSRs

In many parts of the world the damaging potato late blight pathogen, Phytophthora infestans, is spread as a succession of clonal lineages. The discrimination of genetic diversity within such evolving populations provides insights into the processes generating novel lineages and the pathways and drivers of pathogen evolution and dissemination at local and global scales. This knowledge, in turn, helps optimise management practices. Here we combine two key methods for dissecting mitochondrial and nuclear diversity and resolve intra and inter-lineage diversity of over 100 P. infestans isolates representative of key clonal lineages found globally. A novel set of PCR primers that amplify five target regions are provided for mitochondrial DNA sequence analysis. These five loci increased the number of mtDNA haplotypes resolved from four with the PCR RFLP method to 37 (17, 6, 8 and 4 for Ia, Ib, IIa, and IIb haplotypes, respectively, plus 2 Herb-1 haplotypes). As with the PCR RFLP method, two main lineages, I and II were defined. Group I contained 25 mtDNA haplotypes that grouped broadly according to the Ia and Ib types and resolved several sub-clades amongst the global sample. Group II comprised two distinct clusters with four haplotypes corresponding to the RFLP type IIb and eight haplotypes resolved within type IIa. The 12-plex SSR assay revealed 90 multilocus genotypes providing accurate discrimination of dominant clonal lineages and other genetically diverse isolates. Some association of genetic diversity and geographic region of contemporary isolates was observed; US and Mexican isolates formed a loose grouping, distinct from isolates from Europe, South America and other regions. Diversity within clonal lineages was observed that varied according to the age of the clone. In combination, these fine-scale nuclear and maternally inherited mitochondrial markers enabled a greater level of discrimination among isolates than previously available and provided complementary perspectives on evolutionary questions relating to the diversity, phylogeography and the origins and spread of clonal lineages of P. infestans.

Gall-ID: tools for genotyping gall-causing phytopathogenic bacteria

Understanding the population structure and genetic diversity of plant pathogens, as well as the effect of agricultural practices on pathogen evolution, is important for disease management. Developments in molecular methods have contributed to increase the resolution for accurate pathogen identification, but those based on analysis of DNA sequences can be less straightforward to use. To address this, we developed Gall-ID, a web-based platform that uses DNA sequence information from 16S rDNA, multilocus sequence analysis and whole genome sequences to group disease-associated bacteria to their taxonomic units. Gall-ID was developed with a particular focus on gall-forming bacteria belonging to Agrobacterium, Pseudomonas savastanoi, Pantoea agglomerans, and Rhodococcus. Members of these groups of bacteria cause growth deformation of plants, and some are capable of infecting many species of field, orchard, and nursery crops. Gall-ID also enables the use of high-throughput sequencing reads to search for evidence for homologs of characterized virulence genes, and provides downloadable software pipelines for automating multilocus sequence analysis, analyzing genome sequences for average nucleotide identity, and constructing core genome phylogenies. Lastly, additional databases were included in Gall-ID to help determine the identity of other plant pathogenic bacteria that may be in microbial communities associated with galls or causative agents in other diseased tissues of plants. The URL for Gall-ID is http://gall-id.cgrb.oregonstate.edu/.