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Adaptation is driven by the selection for beneficial mutations that provide a fitness advantage in the specific environment in which a population is evolving. However, environments are rarely constant or predictable. When an organism well adapted to one environment finds itself in another, pleiotropic effects of mutations that made it well adapted to its former environment will affect its success. To better understand such pleiotropic effects, we evolved both haploid and diploid barcoded budding yeast populations in multiple environments, isolated adaptive clones, and then determined the fitness effects of adaptive mutations in non-home environments in which they were not selected. We find that pleiotropy is common, with most adaptive evolved lineages showing fitness effects in non-home environments. Consistent with other studies, we find that these pleiotropic effects are unpredictable: they are beneficial in some environments and deleterious in others. However, we do find that lineages with adaptive mutations in the same genes tend to show similar pleiotropic effects. We also find that ploidy influences the observed adaptive mutational spectra in a condition-specific fashion. In some conditions, haploids and diploids are selected with adaptive mutations in identical genes, while in others they accumulate mutations in almost completely disjoint sets of genes.

Abstract Background Interspecific hybridization occurs in every eukaryotic kingdom. While hybrid progeny are frequently at a selective disadvantage, in some instances their increased genome size and complexity may result in greater stress resistance than their ancestors, which can be adaptively advantageous at the edges of their ancestors' ranges. While this phenomenon has been repeatedly documented in the field, the response of hybrid populations to long-term selection has not often been explored in the lab. To fill this knowledge gap we crossed the two most distantly related members of the Saccharomyces sensu stricto group, S. cerevisiae and S. uvarum, and established a mixed population of homoploid and aneuploid hybrids to study how different types of selection impact hybrid genome structure. Results As temperature was raised incrementally from 31°C to 46.5°C over 500 generations of continuous culture, selection favored loss of the S. uvarum genome, although the kinetics of genome loss differed among independent replicates. Temperature-selected isolates exhibited greater inherent and induced thermal tolerance than parental species and founding hybrids, and also exhibited ethanol resistance. In contrast, as exogenous ethanol was increased from 0% to 14% over 500 generations of continuous culture, selection favored euploid S. cerevisiae x S. uvarum hybrids. Ethanol-selected isolates were more ethanol tolerant than S. uvarum and one of the founding hybrids, but did not exhibit resistance to temperature stress. Relative to parental and founding hybrids, temperature-selected strains showed heritable differences in cell wall structure in the forms of increased resistance to zymolyase digestion and Micafungin, which targets cell wall biosynthesis. Conclusions This is the first study to show experimentally that the genomic fate of newly-formed interspecific hybrids depends on the type of selection they encounter during the course of evolution, underscoring the importance of the ecological theatre in determining the outcome of the evolutionary play.

Abstract Background Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof. Results We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. Our methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and p-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq. Conclusions Our within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.

Historical contingency and diminishing returns epistasis have been typically studied for relatively divergent genotypes and/or over long evolutionary timescales. Here, we use Saccharomyces cerevisiae to study the extent of diminishing returns and the changes in the adaptive mutational spectra following a single first adaptive mutational step. We further evolve three clones that arose under identical conditions from a common ancestor. We follow their evolutionary dynamics by lineage tracking and determine adaptive outcomes using fitness assays and whole genome sequencing. We find that diminishing returns manifests as smaller fitness gains during the 2nd step of adaptation compared to the 1st step, mainly due to a compressed distribution of fitness effects. We also find that the beneficial mutational spectra for the 2nd adaptive step are contingent on the 1st step, as we see both shared and diverging adaptive strategies. Finally, we find that adaptive loss-of-function mutations, such as nonsense and frameshift mutations, are less common in the second step of adaptation than in the first step.

Abstract Background Comprehensive annotation and quantification of transcriptomes are outstanding problems in functional genomics. While high throughput mRNA sequencing (RNA-Seq) has emerged as a powerful tool for addressing these problems, its success is dependent upon the availability and quality of reference genome sequences, thus limiting the organisms to which it can be applied. Results Here, we describe Rnnotator, an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome. We have applied the Rnnotator assembly pipeline to two yeast transcriptomes and compared the results to the reference gene catalogs of these organisms. The contigs produced by Rnnotator are highly accurate (95%) and reconstruct full-length genes for the majority of the existing gene models (54.3%). Furthermore, our analyses revealed many novel transcribed regions that are absent from well annotated genomes, suggesting Rnnotator serves as a complementary approach to analysis based on a reference genome for comprehensive transcriptomics. Conclusions These results demonstrate that the Rnnotator pipeline is able to reconstruct full-length transcripts in the absence of a complete reference genome.

Abstract Background MAGE-ML has been promoted as a standard format for describing microarray experiments and the data they produce. Two characteristics of the MAGE-ML format compromise its use as a universal standard: First, MAGE-ML files are exceptionally large – too large to be easily read by most people, and often too large to be read by most software programs. Second, the MAGE-ML standard permits many ways of representing the same information. As a result, different producers of MAGE-ML create different documents describing the same experiment and its data. Recognizing all the variants is an unwieldy software engineering task, resulting in software packages that can read and process MAGE-ML from some, but not all producers. This Tower of MAGE-ML Babel bars the unencumbered exchange of microarray experiment descriptions couched in MAGE-ML. Results We have developed XBabelPhish – an XQuery-based technology for translating one MAGE-ML variant into another. XBabelPhish's use is not restricted to translating MAGE-ML documents. It can transform XML files independent of their DTD, XML schema, or semantic content. Moreover, it is designed to work on very large (> 200 Mb.) files, which are common in the world of MAGE-ML. Conclusion XBabelPhish provides a way to inter-translate MAGE-ML variants for improved interchange of microarray experiment information. More generally, it can be used to transform most XML files, including very large ones that exceed the capacity of most XML tools.

Abstract Background Biomedical ontologies are being widely used to annotate biological data in a computer-accessible, consistent and well-defined manner. However, due to their size and complexity, annotating data with appropriate terms from an ontology is often challenging for experts and non-experts alike, because there exist few tools that allow one to quickly find relevant ontology terms to easily populate a web form. Results We have produced a tool, OntologyWidget, which allows users to rapidly search for and browse ontology terms. OntologyWidget can easily be embedded in other web-based applications. OntologyWidget is written using AJAX (Asynchronous JavaScript and XML) and has two related elements. The first is a dynamic auto-complete ontology search feature. As a user enters characters into the search box, the appropriate ontology is queried remotely for terms that match the typed-in text, and the query results populate a drop-down list with all potential matches. Upon selection of a term from the list, the user can locate this term within a generic and dynamic ontology browser, which comprises the second element of the tool. The ontology browser shows the paths from a selected term to the root as well as parent/child tree hierarchies. We have implemented web services at the Stanford Microarray Database (SMD), which provide the OntologyWidget with access to over 40 ontologies from the Open Biological Ontology (OBO) website 1. Each ontology is updated weekly. Adopters of the OntologyWidget can either use SMD's web services, or elect to rely on their own. Deploying the OntologyWidget can be accomplished in three simple steps: (1) install Apache Tomcat 2 on one's web server, (2) download and install the OntologyWidget servlet stub that provides access to the SMD ontology web services, and (3) create an html (HyperText Markup Language) file that refers to the OntologyWidget using a simple, well-defined format. Conclusion We have developed OntologyWidget, an easy-to-use ontology search and display tool that can be used on any web page by creating a simple html description. OntologyWidget provides a rapid auto-complete search function paired with an interactive tree display. We have developed a web service layer that communicates between the web page interface and a database of ontology terms. We currently store 40 of the ontologies from the OBO website 1, as well as a several others. These ontologies are automatically updated on a weekly basis. OntologyWidget can be used in any web-based application to take advantage of the ontologies we provide via web services or any other ontology that is provided elsewhere in the correct format. The full source code for the JavaScript and description of the OntologyWidget is available from http://smd.stanford.edu/ontologyWidget/.

Abstract Background Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support. Results We propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can be used for annotating and communicating microarray data in a MIAME compliant fashion. Conclusion MAGE-TAB will enable laboratories without bioinformatics experience or support to manage, exchange and submit well-annotated microarray data in a standard format using a spreadsheet. The MAGE-TAB format is self-contained, and does not require an understanding of MAGE-ML or XML.

Abstract Background Genetic differences between yeast strains used in wine-making may account for some of the variation seen in their fermentation properties and may also produce differing sensory characteristics in the final wine product itself. To investigate this, we have determined genomic differences among several Saccharomyces cerevisiae wine strains by using a "microarray karyotyping" (also known as "array-CGH" or "aCGH") technique. Results We have studied four commonly used commercial wine yeast strains, assaying three independent isolates from each strain. All four wine strains showed common differences with respect to the laboratory S. cerevisiae strain S288C, some of which may be specific to commercial wine yeasts. We observed very little intra-strain variation; i.e., the genomic karyotypes of different commercial isolates of the same strain looked very similar, although an exception to this was seen among the Montrachet isolates. A moderate amount of inter-strain genomic variation between the four wine strains was observed, mostly in the form of depletions or amplifications of single genes; these differences allowed unique identification of each strain. Many of the inter-strain differences appear to be in transporter genes, especially hexose transporters (HXT genes), metal ion sensors/transporters (CUP1, ZRT1, ENA genes), members of the major facilitator superfamily, and in genes involved in drug response (PDR3, SNQ1, QDR1, RDS1, AYT1, YAR068W). We therefore used halo assays to investigate the response of these strains to three different fungicidal drugs (cycloheximide, clotrimazole, sulfomethuron methyl). Strains with fewer copies of the CUP1 loci showed hypersensitivity to sulfomethuron methyl. Conclusion Microarray karyotyping is a useful tool for analyzing the genome structures of wine yeasts. Despite only small to moderate variations in gene copy numbers between different wine yeast strains and within different isolates of a given strain, there was enough variation to allow unique identification of strains; additionally, some of the variation correlated with drug sensitivity. The relatively small number of differences seen by microarray karyotyping between the strains suggests that the differences in fermentative and organoleptic properties ascribed to these different strains may arise from a small number of genetic changes, making it possible to test whether the observed differences do indeed confer different sensory properties in the finished wine.

Abstract Background When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data. Results We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields. Conclusions The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://search.cpan.org/dist/Microarray-GeneXplorer/.

Abstract Background Microarray-based comparative genome hybridization experiments generate data that can be mapped onto the genome. These data are interpreted more easily when represented graphically in a genomic context. Results We have developed Caryoscope, which is an open source Java application for visualizing microarray data from array comparative genome hybridization experiments in a genomic context. Caryoscope can read General Feature Format files (GFF files), as well as comma- and tab-delimited files, that define the genomic positions of the microarray reporters for which data are obtained. The microarray data can be browsed using an interactive, zoomable interface, which helps users identify regions of chromosomal deletion or amplification. The graphical representation of the data can be exported in a number of graphic formats, including publication-quality formats such as PostScript. Conclusion Caryoscope is a useful tool that can aid in the visualization, exploration and interpretation of microarray data in a genomic context.

Abstract Background The power of microarray analysis can be realized only if data is systematically archived and linked to biological annotations as well as analysis algorithms. Description The Longhorn Array Database (LAD) is a MIAME compliant microarray database that operates on PostgreSQL and Linux. It is a fully open source version of the Stanford Microarray Database (SMD), one of the largest microarray databases. LAD is available at http://www.longhornarraydatabase.org Conclusions Our development of LAD provides a simple, free, open, reliable and proven solution for storage and analysis of two-color microarray data.

BackgroundThe fungal genus Aspergillus is of critical importance to humankind. Species include those with industrial applications, important pathogens of humans, animals and crops, a source of potent carcinogenic contaminants of food, and an important genetic model. The genome sequences of eight aspergilli have already been explored to investigate aspects of fungal biology, raising questions about evolution and specialization within this genus.ResultsWe have generated genome sequences for ten novel, highly diverse Aspergillus species and compared these in detail to sister and more distant genera. Comparative studies of key aspects of fungal biology, including primary and secondary metabolism, stress response, biomass degradation, and signal transduction, revealed both conservation and diversity among the species. Observed genomic differences were validated with experimental studies. This revealed several highlights, such as the potential for sex in asexual species, organic acid production genes being a key feature of black aspergilli, alternative approaches for degrading plant biomass, and indications for the genetic basis of stress response. A genome-wide phylogenetic analysis demonstrated in detail the relationship of the newly genome sequenced species with other aspergilli.ConclusionsMany aspects of biological differences between fungal species cannot be explained by current knowledge obtained from genome sequences. The comparative genomics and experimental study, presented here, allows for the first time a genus-wide view of the biological diversity of the aspergilli and in many, but not all, cases linked genome differences to phenotype. Insights gained could be exploited for biotechnological and medical applications of fungi.

Evolution by natural selection is expected to be a slow and gradual process. In particular, the mutations that drive evolution are predicted to be small and modular, incrementally improving a small number of traits. However, adaptive mutations identified early in microbial evolution experiments, cancer, and other systems often provide substantial fitness gains and pleiotropically improve multiple traits at once. We asked whether such pleiotropically adaptive mutations are common throughout adaptation or are instead a rare feature of early steps in evolution that tend to target key signaling pathways. To do so, we conducted barcoded second-step evolution experiments initiated from 5 first-step mutations identified from a prior yeast evolution experiment. We then isolated hundreds of second-step mutations from these evolution experiments, measured their fitness and performance in several growth phases, and conducted whole genome sequencing of the second-step clones. Here, we found that while the vast majority of mutants isolated from the first-step of evolution in this condition show patterns of pleiotropic adaptation—improving both performance in fermentation and respiration growth phases—second-step mutations show a shift towards modular adaptation, mostly improving respiration performance and only rarely improving fermentation performance. We also identified a shift in the molecular basis of adaptation from genes in cellular signaling pathways towards genes involved in respiration and mitochondrial function. Our results suggest that the genes in cellular signaling pathways may be more likely to provide large, adaptively pleiotropic benefits to the organism due to their ability to coherently affect many phenotypes at once. As such, these genes may serve as the source of pleiotropic adaptation in the early stages of evolution, and once these become exhausted, organisms then adapt more gradually, acquiring smaller, more modular mutations. Evolution is expected to involve mutations that are small and modular in effect, but recent findings suggest that mutations early in an adaptive process can have strong and pleiotropic effects. This study uses experimental evolution in yeast to show that while first-step mutants appear pleiotropic in nature, second-step mutations show a shift towards modular adaptation. [ABSTRACT FROM AUTHOR]

Cryptococcus, a major cause of disseminated infections in immunocompromised patients, kills over 600,000 people per year worldwide. Genes involved in the virulence of the meningitis-causing fungus are being characterized at an increasing rate, and to date, at least 648 Cryptococcus gene names have been published. However, these data are scattered throughout the literature and are challenging to find. Furthermore, conflicts in locus identification exist, so that named genes have been subsequently published under new names or names associated with one locus have been used for another locus. To avoid these conflicts and to provide a central source of Cryptococcus gene information, we have collected all published Cryptococcus gene names from the scientific literature and associated them with standard Cryptococcus locus identifiers and have incorporated them into FungiDB (www.fungidb.org). FungiDB is a panfungal genome database that collects gene information and functional data and provides search tools for 61 species of fungi and oomycetes. We applied these published names to a manually curated ortholog set of all Cryptococcus species currently in FungiDB, including Cryptococcus neoformans var. neoformans strains JEC21 and B-3501A, C. neoformans var. grubii strain H99, and Cryptococcus gattii strains R265 and WM276, and have written brief descriptions of their functions. We also compiled a protocol for gene naming that summarizes guidelines proposed by members of the Cryptococcus research community. The centralization of genomic and literature-based information for Cryptococcus at FungiDB will help researchers communicate about genes of interest, such as those related to virulence, and will further facilitate research on the pathogen.

Abstract Background Candida albicans is a ubiquitous opportunistic fungal pathogen that afflicts immunocompromised human hosts. With rare and transient exceptions the yeast is diploid, yet despite its clinical relevance the respective sequences of its two homologous chromosomes have not been completely resolved. Results We construct a phased diploid genome assembly by deep sequencing a standard laboratory wild-type strain and a panel of strains homozygous for particular chromosomes. The assembly has 700-fold coverage on average, allowing extensive revision and expansion of the number of known SNPs and indels. This phased genome significantly enhances the sensitivity and specificity of allele-specific expression measurements by enabling pooling and cross-validation of signal across multiple polymorphic sites. Additionally, the diploid assembly reveals pervasive and unexpected patterns in allelic differences between homologous chromosomes. Firstly, we see striking clustering of indels, concentrated primarily in the repeat sequences in promoters. Secondly, both indels and their repeat-sequence substrate are enriched near replication origins. Finally, we reveal an intimate link between repeat sequences and indels, which argues that repeat length is under selective pressure for most eukaryotes. This connection is described by a concise one-parameter model that explains repeat-sequence abundance in C. albicans as a function of the indel rate, and provides a general framework to interpret repeat abundance in species ranging from bacteria to humans. Conclusions The phased genome assembly and insights into repeat plasticity will be valuable for better understanding allele-specific phenomena and genome evolution.

A complete description of the transcriptome of an organism is crucial for a comprehensive understanding of how it functions and how its transcriptional networks are controlled, and may provide insights into the organisms evolution. Despite the status of Saccharomyces cerevisiae as arguably the most well-studied model eukaryote, we still do not have a full catalog or understanding of all its genes. In order to interrogate the transcriptome of S. cerevisiae for low abundance or rapidly turned over transcripts, we deleted elements of the RNA degradation machinery with the goal of preferentially increasing the relative abundance of such transcripts. We then used high-resolution tiling microarrays and ultra high-throughput sequencing (UHTS) to identify, map, and validate unannotated transcripts that are more abundant in the RNA degradation mutants relative to wild-type cells. We identified 365 currently unannotated transcripts, the majority presumably representing low abundance or short-lived RNAs, of which 185 are previously unknown and unique to this study. It is likely that many of these are cryptic unstable transcripts (CUTs), which are rapidly degraded and whose function(s) within the cell are still unclear, while others may be novel functional transcripts. Of the 185 transcripts we identified as novel to our study, greater than 80 percent come from regions of the genome that have lower conservation scores amongst closely related yeast species than 85 percent of the verified ORFs in S. cerevisiae. Such regions of the genome have typically been less well-studied, and by definition transcripts from these regions will distinguish S. cerevisiae from these closely related species.

Recent sequencing and assembly of the genome for the fungal pathogen Candida albicans used simple automated procedures for the identification of putative genes. We have reviewed the entire assembly, both by hand and with additional bioinformatic resources, to accurately map and describe 6,354 genes and to identify 246 genes whose original database entries contained sequencing errors (or possibly mutations) that affect their reading frame. Comparison with other fungal genomes permitted the identification of numerous fungus-specific genes that might be targeted for antifungal therapy. We also observed that, compared to other fungi, the protein-coding sequences in the C. albicans genome are especially rich in short sequence repeats. Finally, our improved annotation permitted a detailed analysis of several multigene families, and comparative genomic studies showed that C. albicans has a far greater catabolic range, encoding respiratory Complex 1, several novel oxidoreductases and ketone body degrading enzymes, malonyl-CoA and enoyl-CoA carriers, several novel amino acid degrading enzymes, a variety of secreted catabolic lipases and proteases, and numerous transporters to assimilate the resulting nutrients. The results of these efforts will ensure that the Candida research community has uniform and comprehensive genomic information for medical research as well as for future diagnostic and therapeutic applications.