{'fraction_non_alphanumeric': 0.020930232558139535, 'fraction_numerical': 0.003398926654740608, 'mean_word_length': 5.624407582938389, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
New day rising: state of the art in yeast functional genomics.

Nineteen years have elapsed since the genome of the budding yeast Saccharomyces cerevisiae was sequenced, seeding a proliferation of technology development and applications toward understanding more fully the intricately networked biology within this single-celled eukaryote. This proliferation in yeast genomics, applicable to both S. cerevisiae and Candida albicans, has resulted in an expanded knowledge base and toolkit of resources and approaches. At present, this toolkit holds strong— potentially unique—promise for the generation of new data sets and fundamental discovery. In this special issue, the present state of functional genomics in yeast is reviewed, covering resources and approaches for genomics, a sampling of current genomic studies and an overview of approaches highlighting the applicability of yeast-based research toward human biology and health. An annotated sequence for the S. cerevisiae genome was published in 1996 [1, 2], providing an important template for largescale and high-throughput analyses. Early functional genomic studies used this sequence principally as a means to demarcate genes, with the genes being profiled globally for expression levels or systematically targeted for the generation of deletion mutants [3–5]. An international consortium of laboratories constructed a genome-wide collection of S. cerevisiae deletion mutants in 2002, with each strain containing a precise start codon-to-stop codon replacement of a single gene with a cassette encoding resistance to the drug G418 [5]. Systematic phenotypic screens using this deletion collection have been a staple of yeast functional genomics in subsequent years. While these studies have been among the most widely recognizable in the discipline of functional genomics, a wider diversity of mutant alleles are now available for informative large-scale analysis of yeast gene function. We discuss these mutant allele collections, enabling functional genomics in both S. cerevisiae and C. albicans, in the first review of this issue. The opportunistic human fungal pathogen C. albicans is an important organism of study, as immunocompromised individuals remain likely to encounter candidiasis at some point in their lifetimes [6]. As a substrate for functional genomics, however, C. albicans poses many obstacles. Principally, C. albicans cannot be stably propagated as a haploid, and transforming DNA does not integrate by homologous recombination with the same efficiency as observed in S. cerevisiae. The Candida research community has, nonetheless, generated an impressive collection of resources for functional genomics; these allelic collections and exemplary studies using these resources are reviewed in this issue by Anderson and Bennett. The first two reviews in this issue highlight a toolkit of biological resources for yeast functional genomics, but the resulting large-scale data sets are inherently difficult to interpret without effective bioinformatics approaches. Gene Ontology annotations have provided a consistent nomenclature to determine statistical enrichment of genes/proteins associated with common functions, biological processes and subcellular components in high-throughput data sets [7]. The utility in these analyses of enrichment, though, is limited by the completeness with which genes are annotated. Further, these annotations are not ideally suited to identify pathway enrichment or pathway organization from large-scale data sets. With the obvious and ongoing need to better place genes within metabolic, regulatory or signaling pathways, computational approaches can be effective in leveraging large-scale data sets. In this issue, Cheng and Zhu review current methodologies to model biological pathways from large-scale and high-throughput data sets. The available resources and approaches mentioned above have enabled an expanding and varied body of functional genomic studies in yeast. To highlight this work, we review in this issue three timely areas of research using functional genomics and high-throughput methods. Downey and Baetz present an overview of current genomic and proteomic studies for the identification of substrates of lysine acetyltransferases and deacetylases. Measday and Stirling review recent screening studies in S. cerevisiae to identify genes and pathways that contribute to the maintenance of genome stability. In addition to these analyses of cell processes and signaling pathways, high-throughput studies are refining our understanding of widely used data sets. For example, it is intuitive that protein interactions are dynamic; however, high-throughput mapping studies of yeast have classically represented protein–protein interactions as static networks. In this issue, Filteau and colleagues in Christian Landry’s laboratory review studies that indicate the dynamic nature of protein interactions, considering interaction changes that are responsive to genetic perturbations and environmental variables. The availability of whole genome sequence data for classic laboratory strains of yeast has been supplemented over the years through efforts to sequence additional commonly used strains and clinical isolates [8]. These fundamental resources, among others, are facilitating studies to uncover the genetic basis of phenotypic variation. Skelly and Magwene review in this issue the topics of population genomics and phenotypic variation with respect to S. cerevisiae. Studies profiling interspecies changes in transcript levels, proteins and metabolites