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High-throughput metabarcoding studies on fungi and other eukaryotic microorganisms are rapidly becoming more frequent and more complex, requiring researchers to handle ever increasing amounts of raw sequence data. Here, we provide a flexible pipeline for pruning and analyzing fungal barcode (ITS rDNA) data generated as paired-end reads on Illumina MiSeq sequencers. The pipeline presented includes specific steps fine-tuned for ITS, that are mostly missing from pipelines developed for prokaryotes. It (1) employs state of the art programs and follows best practices in fungal high-throughput metabarcoding; (2) consists of modules and scripts easily modifiable by the user to ensure maximum flexibility with regard to specific needs of a project or future methodological developments; and (3) is straightforward to use, also in classroom settings. We provide detailed descriptions and revision techniques for each step, thus giving the user maximum control over data treatment and avoiding a black-box approach. Employing this pipeline will improve and speed up the tedious and error-prone process of cleaning fungal Illumina metabarcoding data.
Smut fungi are well-suited to investigate the ecology and evolution of plant pathogens, as they are strictly biotrophic, yet cultivable on media. Here we report the genome sequence of Melanopsichium pennsylvanicum, closely related to Ustilago maydis and other Poaceae-infecting smuts, but parasitic to a dicot plant. To explore the evolutionary patterns resulting from host adaptation after this huge host jump, the genome of M. pennsylvanicum was sequenced and compared to the genomes of Ustilago maydis, Sporisorium reilianum, and Ustilago hordei. While all four genomes had a similar completeness in CEGMA analyses, gene absence was highest in M. pennsylvanicum, and most pronounced in putative secreted proteins, which are often considered as effector candidates. In contrast, the amount of private genes was similar among the species, highlighting that gene loss rather than gene gain is the hallmark of adaptation after the host jump to the dicot host. Our analyses revealed a trend of putative effectors to be next to another putative effector, but the majority of these are not in clusters and thus the focus on pathogenicity clusters might not be appropriate for all smut genomes. Positive selection studies revealed that M. pennsylvanicum has the highest number and proportion of genes under positive selection. In general, putative effectors showed a higher proportion of positively selected genes than non-effector candidates. The 248 putative secreted effectors found in all four smut genomes might constitute a core set needed for pathogenicity, while those 92 that are found in all grass-parasitic smuts, but have no ortholog in M. pennsylvanicum might constitute a set of effectors important for successful colonization of grass hosts.