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Analysis of Fungal Genomes Reveals Commonalities of Intron Gain or Loss and Functions in Intron-Poor SpeciesOuangraoua, Aida (Ed.)Abstract Previous evolutionary reconstructions have concluded that early eukaryotic ancestors including both the last common ancestor of eukaryotes and of all fungi had intron-rich genomes. By contrast, some extant eukaryotes have few introns, underscoring the complex histories of intron–exon structures, and raising the question as to why these few introns are retained. Here, we have used recently available fungal genomes to address a variety of questions related to intron evolution. Evolutionary reconstruction of intron presence and absence using 263 diverse fungal species supports the idea that massive intron reduction through intron loss has occurred in multiple clades. The intron densities estimated in various fungal ancestors differ from zero to 7.6 introns per 1 kb of protein-coding sequence. Massive intron loss has occurred not only in microsporidian parasites and saccharomycetous yeasts, but also in diverse smuts and allies. To investigate the roles of the remaining introns in highly-reduced species, we have searched for their special characteristics in eight intron-poor fungi. Notably, the introns of ribosome-associated genes RPL7 and NOG2 have conserved positions; both intron-containing genes encoding snoRNAs. Furthermore, both the proteins and snoRNAs are involved in ribosome biogenesis, suggesting that the expression of the protein-coding genes and noncoding snoRNAs may be functionallymore »
Ouangraoua, Aida (Ed.)Abstract The core genome represents the set of genes shared by all, or nearly all, strains of a given population or species of prokaryotes. Inferring the core genome is integral to many genomic analyses, however, most methods rely on the comparison of all the pairs of genomes; a step that is becoming increasingly difficult given the massive accumulation of genomic data. Here, we present CoreCruncher; a program that robustly and rapidly constructs core genomes across hundreds or thousands of genomes. CoreCruncher does not compute all pairwise genome comparisons and uses a heuristic based on the distributions of identity scores to classify sequences as orthologs or paralogs/xenologs. Although it is much faster than current methods, our results indicate that our approach is more conservative than other tools and less sensitive to the presence of paralogs and xenologs. CoreCruncher is freely available from: https://github.com/lbobay/CoreCruncher. CoreCruncher is written in Python 3.7 and can also run on Python 2.7 without modification. It requires the python library Numpy and either Usearch or Blast. Certain options require the programs muscle or mafft.