Baker's yeast, Saccharomyces cerevisiae, is one of the best studied model organisms, and has been associated with human activity for thousands of years. Two papers published in the 19 March 2009 issue of Nature provide a picture of its population structure and its relationship with other yeasts. Liti et al. compare genome variation in S. cerevisiae isolates with its closest wild cousin, S. paradoxus, which has never been associated with human activity. They find that variation in S. paradoxus closely follows geographic borders; S. cerevisiae shows less differentiation, consistent with opportunities for cross-breeding, rather than a few distinct domestication events, as the main human influence. Schacherer et al. compare 63 S. cerevisiae isolates from different ecological niches and geographic locations. They find evidence for genetic differentiation of three distinct subgroups based on where the strains were isolated: from vineyards, sake and related fermentations and lab strains. Their data support the hypothesis that these three groups represent separate domestication events, and that S. cerevisiae as a whole is not domesticated. By sequencing over seventy isolates of the domesticated baker's yeast Saccharomyces cerevisiae and its closest relative, S. paradoxus, this study describes variation in gene content, SNPs, indels, copy numbers and transposable elements, providing insights into the evolution of different lineages, phenotypic variation, domestication and population structure of Saccharomyces. Since the completion of the genome sequence of Saccharomyces cerevisiae in 1996 (refs 1, 2), there has been a large increase in complete genome sequences, accompanied by great advances in our understanding of genome evolution. Although little is known about the natural and life histories of yeasts in the wild, there are an increasing number of studies looking at ecological and geographic distributions3,4, population structure5,6,7,8 and sexual versus asexual reproduction9,10. Less well understood at the whole genome level are the evolutionary processes acting within populations and species that lead to adaptation to different environments, phenotypic differences and reproductive isolation. Here we present one- to fourfold or more coverage of the genome sequences of over seventy isolates of the baker’s yeast S. cerevisiae and its closest relative, Saccharomyces paradoxus. We examine variation in gene content, single nucleotide polymorphisms, nucleotide insertions and deletions, copy numbers and transposable elements. We find that phenotypic variation broadly correlates with global genome-wide phylogenetic relationships. S. paradoxus populations are well delineated along geographic boundaries, whereas the variation among worldwide S. cerevisiae isolates shows less differentiation and is comparable to a single S. paradoxus population. Rather than one or two domestication events leading to the extant baker’s yeasts, the population structure of S. cerevisiae consists of a few well-defined, geographically isolated lineages and many different mosaics of these lineages, supporting the idea that human influence provided the opportunity for cross-breeding and production of new combinations of pre-existing variations.

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

The MuTHER Consortium reports an analysis of the genetics of gene expression in three tissues from approximately 850 mono- and dizygotic twins. They systematically dissect cis and trans genetic effects and estimate non-genetic effects on gene expression. Sequence-based variation in gene expression is a key driver of disease risk. Common variants regulating expression in cis have been mapped in many expression quantitative trait locus (eQTL) studies, typically in single tissues from unrelated individuals. Here, we present a comprehensive analysis of gene expression across multiple tissues conducted in a large set of mono- and dizygotic twins that allows systematic dissection of genetic (cis and trans) and non-genetic effects on gene expression. Using identity-by-descent estimates, we show that at least 40% of the total heritable cis effect on expression cannot be accounted for by common cis variants, a finding that reveals the contribution of low-frequency and rare regulatory variants with respect to both transcriptional regulation and complex trait susceptibility. We show that a substantial proportion of gene expression heritability is trans to the structural gene, and we identify several replicating trans variants that act predominantly in a tissue-restricted manner and may regulate the transcription of many genes.

Very high-throughput sequencing technologies need to be matched by high-throughput functional studies if we are to make full use of the current explosion in genome sequences. We have generated a very large bacterial mutant pool, consisting of an estimated 1.1 million transposon mutants and we have used genomic DNA from this mutant pool, and Illumina nucleotide sequencing to prime from the transposon and sequence into the adjacent target DNA. With this method, which we have called TraDIS (transposon directed insertion-site sequencing), we have been able to map 370,000 unique transposon insertion sites to the Salmonella enterica serovar Typhi chromosome. The unprecedented density and resolution of mapped insertion sites, an average of one every 13 base pairs, has allowed us to assay simultaneously every gene in the genome for essentiality and generate a genome-wide list of candidate essential genes. In addition, the semiquantitative nature of the assay allowed us to identify genes that are advantageous and those that are disadvantageous for growth under standard laboratory conditions. Comparison of the mutant pool following growth in the presence or absence of ox bile enabled every gene to be assayed for its contribution toward bile tolerance, a trait required of any enteric bacterium and for carriage of S. Typhi in the gall bladder. This screen validated our hypothesis that we can simultaneously assay every gene in the genome to identify niche-specific essential genes.

The genomes of malaria parasites contain many genes of unknown function. To assist drug development through the identification of essential genes and pathways, we have measured competitive growth rates in mice of 2,578 barcoded Plasmodium berghei knockout mutants, representing >50% of the genome, and created a phenotype database. At a single stage of its complex life cycle, P. berghei requires two-thirds of genes for optimal growth, the highest proportion reported from any organism and a probable consequence of functional optimization necessitated by genomic reductions during the evolution of parasitism. In contrast, extreme functional redundancy has evolved among expanded gene families operating at the parasite-host interface. The level of genetic redundancy in a single-celled organism may thus reflect the degree of environmental variation it experiences. In the case of Plasmodium parasites, this helps rationalize both the relative successes of drugs and the greater difficulty of making an effective vaccine.