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Gallone, B.

Publications and source records attributed to Gallone, B..

2 recordsLinked to original sources

The genus Cortinarius should not (yet) be split

The genus Cortinarius (Pers.) Gray (Agaricales, Basidiomycota) is one of the most species-rich fungal genera with thousands of species reported. Cortinarius species are important ectomycorrhizal fungi and form associations with many vascular plants globally. Until recently Cortinarius was the single genus of the Cortinariaceae family, despite several attempts to provide a workable, lower-rank hierarchical structure based on subgenera and sections. The first phylogenomic study for this group elevated the old genus Cortinarius to family level and the family was split into ten genera, of which seven were described as new. Here, by careful re-examination of the recently published phylogenomic dataset, we detected extensive gene-tree/species-tree conflicts using both concatenation and multispecies coalescent (MSC) approaches. Our analyses demonstrate that the Cortinarius phylogeny remains unresolved and the resulting phylogenomic hypotheses suffer from very short and unsupported branches in the backbone. We can confirm monophyly of only four out of ten suggested new genera, leaving uncertain the relationships between each other and the general branching order. Thorough exploration of the tree space demonstrated that the topology on which Cortinarius revised classification relies on does not represent the best phylogenetic hypothesis and should not be used as constrained topology to include additional species. For this reason, we argue that based on available evidence the genus Cortinarius should not (yet) be split. Moreover, considering that phylogenetic uncertainty translates to taxonomic uncertainty, we advise for careful evaluation of phylogenomic datasets before proposing radical taxonomic and nomenclatural changes.

evolutionary biology↗

Harnessing the power of technical and natural variation in 116 yeast datasets to benchmark long read assembly pipelines

With increases in throughput and reductions in cost, long read sequencing has become the standard for most genome assembly projects and has opened up new avenues for large-scale genomic research. While more amenable to assembly than short-read sequence data, long-read datasets tend to have higher error rates. To address this problem numerous tools have been developed to correct reads before assembly and polish assembled contigs. Although, numerous studies have been conducted to assess or benchmark these tools, few capture the real variance in long read sequence data that might affect tool performance much less full pipeline performance. To address these shortcomings, we compiled a dataset containing long-read sequences of 116 different strains of brewers yeast, S. cerevisiae, gathered largely from public databases and evaluated different assembly-related tools as well as their interactions. We found that pre-assembly short-read error correction of long reads combined with post-assembly short-read polishing provided the best assemblies. We also found that correction/polishing steps with uncorrected long reads often lead to degradation of assembly quality. Finally, we show which tools and pipelines work best with different types of input data.

bioinformatics↗