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Interspecies hybridization is prevalent in various eukaryotic lineages and plays important roles in phenotypic diversification, adaptation, and speciation. To better understand the changes that occurred in the different subgenomes of a hybrid species and how they facilitate adaptation, we have completed chromosome-level de novo assemblies of all chromosomes for a recently formed hybrid yeast,Saccharomyces bayanusstrain CBS380, using Oxford Nanopore Technologies' MinION long-read sequencing. We characterize theS. bayanusgenome and compare it with its parent species,Saccharomyces uvarumandSaccharomyces eubayanus, and otherS. bayanusgenomes to better understand genome evolution after a relatively recent hybridization event. We observe multiple recombination events between the subgenomes in each chromosome, followed by loss of heterozygosity (LOH) in nine chromosome pairs. In addition to maintaining nearly all gene content and synteny from its parental genomes,S. bayanushas acquired many genes from other yeast species, primarily through the introgression ofSaccharomyces cerevisiae, such as those involved in the maltose metabolism. Finally, the patterns of recombination and LOH suggest an allotetraploid origin ofS. bayanus. The gene acquisition and rapid LOH in the hybrid genome probably facilitated its adaptation to maltose brewing environments and mitigated the maladaptive effect of hybridization. This paper describes the first in-depth study using long-read sequencing technology of anS. bayanushybrid genome, which may serve as an excellent reference for future studies of this important yeast and other yeast strains. 

Abstract The transcription initiation landscape of eukaryotic genes is complex and highly dynamic. In eukaryotes, genes can generate multiple transcript variants that differ in 5′ boundaries due to usages of alternative transcription start sites (TSSs), and the abundance of transcript isoforms are highly variable. Due to a large number and complexity of the TSSs, it is not feasible to depict details of transcript initiation landscape of all genes using text-format genome annotation files. Therefore, it is necessary to provide data visualization of TSSs to represent quantitative TSS maps and the core promoters (CPs). In addition, the selection and activity of TSSs are influenced by various factors, such as transcription factors, chromatin remodeling and histone modifications. Thus, integration and visualization of functional genomic data related to these features could provide a better understanding of the gene promoter architecture and regulatory mechanism of transcription initiation. Yeast species play important roles for the research and human society, yet no database provides visualization and integration of functional genomic data in yeast. Here, we generated quantitative TSS maps for 12 important yeast species, inferred their CPs and built a public database, YeasTSS (www.yeastss.org). YeasTSS was designed as a central portal for visualization and integration of the TSS maps, CPs and functional genomic data related to transcription initiation in yeast. YeasTSS is expected to benefit the research community and public education for improving genome annotation, studies of promoter structure, regulated control of transcription initiation and inferring gene regulatory network. 

Abstract Transcription initiation is regulated in a highly organized fashion to ensure proper cellular functions. Accurate identification of transcription start sites (TSSs) and quantitative characterization of transcription initiation activities are fundamental steps for studies of regulated transcriptions and core promoter structures. Several high-throughput techniques have been developed to sequence the very 5′end of RNA transcripts (TSS sequencing) on the genome scale. Bioinformatics tools are essential for processing, analysis, and visualization of TSS sequencing data. Here, we present TSSr, an R package that provides rich functions for mapping TSS and characterizations of structures and activities of core promoters based on all types of TSS sequencing data. Specifically, TSSr implements several newly developed algorithms for accurately identifying TSSs from mapped sequencing reads and inference of core promoters, which are a prerequisite for subsequent functional analyses of TSS data. Furthermore, TSSr also enables users to export various types of TSS data that can be visualized by genome browser for inspection of promoter activities in association with other genomic features, and to generate publication-ready TSS graphs. These user-friendly features could greatly facilitate studies of transcription initiation based on TSS sequencing data. The source code and detailed documentations of TSSr can be freely accessed at https://github.com/Linlab-slu/TSSr.