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Abstract Studying the signatures of evolution can help to understand genetic processes. Here, we demonstrate how the existence of balancing selection can be used to identify the breeding systems of fungi from genomic data. The breeding systems of fungi are controlled by self-incompatibility loci that determine mating types between potential mating partners, resulting in strong balancing selection at the loci. Within the fungal phylum Basidiomycota, two such self-incompatibility loci, namely HD MAT locus and P/R MAT locus, control mating types of gametes. Loss of function at one or both MAT loci results in different breeding systems and relaxes the MAT locus from balancing selection. By investigating the signatures of balancing selection at MAT loci, one can infer a species’ breeding system without culture-based studies. Nevertheless, the extreme sequence divergence among MAT alleles imposes challenges for retrieving full variants from both alleles when using the conventional read-mapping method. Therefore, we employed a combination of read-mapping and local de novo assembly to construct haplotypes of HD MAT alleles from genomes in suilloid fungi (genera Suillus and Rhizopogon). Genealogy and pairwise divergence of HD MAT alleles showed that the origins of mating types predate the split between these two closely related genera. High sequence divergence, trans-specific polymorphism, and the deeply diverging genealogy confirm the long-term functionality and multiallelic status of HD MAT locus in suilloid fungi. This work highlights a genomics approach to studying breeding systems regardless of the culturability of organisms based on the interplay between evolution and genetics. 

Abstract Bioinformatic analysis—such as genome assembly quality assessment, alignment summary statistics, relative synonymous codon usage, file format conversion, and processing and analysis—is integrated into diverse disciplines in the biological sciences. Several command-line pieces of software have been developed to conduct some of these individual analyses, but unified toolkits that conduct all these analyses are lacking. To address this gap, we introduce BioKIT, a versatile command line toolkit that has, upon publication, 42 functions, several of which were community-sourced, that conduct routine and novel processing and analysis of genome assemblies, multiple sequence alignments, coding sequences, sequencing data, and more. To demonstrate the utility of BioKIT, we conducted a comprehensive examination of relative synonymous codon usage across 171 fungal genomes that use alternative genetic codes, showed that the novel metric of gene-wise relative synonymous codon usage can accurately estimate gene-wise codon optimization, evaluated the quality and characteristics of 901 eukaryotic genome assemblies, and calculated alignment summary statistics for 10 phylogenomic data matrices. BioKIT will be helpful in facilitating and streamlining sequence analysis workflows. BioKIT is freely available under the MIT license from GitHub (https://github.com/JLSteenwyk/BioKIT), PyPi (https://pypi.org/project/jlsteenwyk-biokit/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/jlsteenwyk-biokit). Documentation, user tutorials, and instructions for requesting new features are available online (https://jlsteenwyk.com/BioKIT).