Saccharomyces Genome Database provides tools to survey gene expression and functional analysis data

New data and collaborations at the Saccharomyces Genome Database: updated reference genome, alleles, and the Alliance of Genome Resources

Saccharomyces cerevisiae is used to provide fundamental understanding of eukaryotic genetics, gene product function, and cellular biological processes. Saccharomyces Genome Database (SGD) has been supporting the yeast research community since 1993, serving as its de facto hub. Over the years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation, and developed various tools and methods for analysis and curation of a variety of emerging data types. More recently, SGD and six other model organism focused knowledgebases have come together to create the Alliance of Genome Resources to develop sustainable genome information resources that promote and support the use of various model organisms to understand the genetic and genomic bases of human biology and disease. Here we describe recent activities at SGD, including the latest reference genome annotation update, the development of a curation system for mutant alleles, and new pages addressing homology across model organisms as well as the use of yeast to study human disease.

Yeast as a Model to Understand Actin-Mediated Cellular Functions in Mammals-Illustrated with Four Actin Cytoskeleton Proteins

The budding yeast Saccharomyces cerevisiae has an actin cytoskeleton that comprises a set of protein components analogous to those found in the actin cytoskeletons of higher eukaryotes. Furthermore, the actin cytoskeletons of S. cerevisiae and of higher eukaryotes have some similar physiological roles. The genetic tractability of budding yeast and the availability of a stable haploid cell type facilitates the application of molecular genetic approaches to assign functions to the various actin cytoskeleton components. This has provided information that is in general complementary to that provided by studies of the equivalent proteins of higher eukaryotes and hence has enabled a more complete view of the role of these proteins. Several human functional homologues of yeast actin effectors are implicated in diseases. A better understanding of the molecular mechanisms underpinning the functions of these proteins is critical to develop improved therapeutic strategies. In this article we chose as examples four evolutionarily conserved proteins that associate with the actin cytoskeleton: (1) yeast Hof1p/mammalian PSTPIP1, (2) yeast Rvs167p/mammalian BIN1, (3) yeast eEF1A/eEF1A1 and eEF1A2 and (4) yeast Yih1p/mammalian IMPACT. We compare the knowledge on the functions of these actin cytoskeleton-associated proteins that has arisen from studies of their homologues in yeast with information that has been obtained from in vivo studies using live animals or in vitro studies using cultured animal cell lines.

Cross-stress resistance in Saccharomyces cerevisiae yeast--new insight into an old phenomenon

Acquired stress resistance is the result of mild stress causing the acquisition of resistance to severe stress of the same or a different type. The mechanism of "same-stress" resistance (resistance to a second, strong stress after mild primary stress of the same type) probably depends on the activation of defense and repair mechanisms specific for a particular type of stress, while cross-stress resistance (i.e., resistance to a second, strong stress after a different type of mild primary stress) is the effect of activation of both a specific and general stress response program, which in Saccharomyces cerevisiae yeast is known as the environmental stress response (ESR). Advancements in research techniques have made it possible to study the mechanism of cross-stress resistance at various levels of cellular organization: stress signal transduction pathways, regulation of gene expression, and transcription or translation processes. As a result of this type of research, views on the cross-stress protection mechanism have been reconsidered. It was originally thought that cross-stress resistance, irrespective of the nature of the two stresses, was determined by universal mechanisms, i.e., the same mechanisms within the general stress response. They are now believed to be more specific and strictly dependent on the features of the first stress.

Copy-number variation of cancer-gene orthologs is sufficient to induce cancer-like symptoms in Saccharomyces cerevisiae

BACKGROUND

Copy-number variation (CNV), rather than complete loss of gene function, is increasingly implicated in human disease. Moreover, gene dosage is recognised as important in tumourigenesis, and there is an increasing realisation that CNVs may not be just symptomatic of the cancerous state but may, in fact, be causative. However, the identification of CNV-related phenotypes for mammalian genes is a slow process, due to the technical difficulty of constructing deletion mutants. Using the genome-wide deletion library for the model eukaryote, Saccharomyces cerevisiae, we have identified genes (termed haploproficient, HP) which, when one copy is deleted from a diploid cell, result in an increased rate of proliferation. Since haploproficiency under nutrient-sufficient conditions is a novel phenotype, we sought here to characterise a subset of the yeast haploproficient genes which seem particularly relevant to human cancers.

RESULTS

We show that, for a subset of HP genes, heterozygous deletion is sufficient to cause aberrant cell cycling and altered rates of apoptosis, phenotypes associated with cancer in mammalian cells. A majority of these yeast genes are the orthologs of mammalian cancer genes, and hence our studies suggest that CNV of these oncogenic orthologs may be sufficient to lead to tumourigenesis in human cells. Moreover, where not already implicated, this cluster of cancer-like phenotypes in this model eukaryote may be predictive of the involvement in cancer of the mammalian orthologs of these yeast HP genes. Using the yeast set as a model, we show that the response to a range of anti-cancer drugs is strongly dependent on gene dosage, such that intermediate concentrations of the drugs can actually increase a mutant's growth rate.

CONCLUSIONS

The exploitation of data on the phenotypic impact of heterozygosis in Saccharomyces cerevisiae has permitted the prediction of CNVs affecting tumourigenesis in humans. Our yeast data also suggest that the identification of CNVs in tumour cells may assist both the selection of anti-cancer drugs and the dosages at which they should be administered if they are to be a beneficial, rather than a deleterious, therapy.

Comparative gene expression between two yeast species

Background

Comparative genomics brings insight into sequence evolution, but even more may be learned by coupling sequence analyses with experimental tests of gene function and regulation. However, the reliability of such comparisons is often limited by biased sampling of expression conditions and incomplete knowledge of gene functions across species. To address these challenges, we previously systematically generated expression profiles in Saccharomyces bayanus to maximize functional coverage as compared to an existing Saccharomyces cerevisiae data repository.

Results

In this paper, we take advantage of these two data repositories to compare patterns of ortholog expression in a wide variety of conditions. First, we developed a scalable metric for expression divergence that enabled us to detect a significant correlation between sequence and expression conservation on the global level, which previous smaller-scale expression studies failed to detect. Despite this global conservation trend, between-species gene expression neighborhoods were less well-conserved than within-species comparisons across different environmental perturbations, and approximately 4% of orthologs exhibited a significant change in co-expression partners. Furthermore, our analysis of matched perturbations collected in both species (such as diauxic shift and cell cycle synchrony) demonstrated that approximately a quarter of orthologs exhibit condition-specific expression pattern differences.

Conclusions

Taken together, these analyses provide a global view of gene expression patterns between two species, both in terms of the conditions and timing of a gene's expression as well as co-expression partners. Our results provide testable hypotheses that will direct future experiments to determine how these changes may be specified in the genome.

The metabolic blueprint of Phaeodactylum tricornutum reveals a eukaryotic Entner-Doudoroff glycolytic pathway

Diatoms are one of the most successful groups of unicellular eukaryotic algae. Successive endosymbiotic events contributed to their flexible metabolism, making them competitive in variable aquatic habitats. Although the recently sequenced genomes of the model diatoms Phaeodactylum tricornutum and Thalassiosira pseudonana have provided the first insights into their metabolic organization, the current knowledge on diatom biochemistry remains fragmentary. By means of a genome-wide approach, we developed DiatomCyc, a detailed pathway/genome database of P. tricornutum. DiatomCyc contains 286 pathways with 1719 metabolic reactions and 1613 assigned enzymes, spanning both the central and parts of the secondary metabolism of P. tricornutum. Central metabolic pathways, such as those of carbohydrates, amino acids and fatty acids, were covered. Furthermore, our understanding of the carbohydrate model in P. tricornutum was extended. In particular we highlight the discovery of a functional Entner-Doudoroff pathway, an ancient alternative for the glycolytic Embden-Meyerhof-Parnas pathway, and a putative phosphoketolase pathway, both uncommon in eukaryotes. DiatomCyc is accessible online (http://www.diatomcyc.org), and offers a range of software tools for the visualization and analysis of metabolic networks and 'omics' data. We anticipate that DiatomCyc will be key to gaining further understanding of diatom metabolism and, ultimately, will feed metabolic engineering strategies for the industrial valorization of diatoms.

AN EFFECTIVE DATA MINING TECHNIQUE FOR RECONSTRUCTING GENE REGULATORY NETWORKS FROM TIME SERIES EXPRESSION DATA

Recent development in DNA microarray technologies has made the reconstruction of gene regulatory networks (GRNs) feasible. To infer the overall structure of a GRN, there is a need to find out how the expression of each gene can be affected by the others. Many existing approaches to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory experiments can be performed afterwards for verification. Since, they are not intended to be used to predict if a gene in an unseen sample has any interactions with other genes, statistical verification of the reliability of the discovered interactions can be difficult. Furthermore, since the temporal ordering of the data is not taken into consideration, the directionality of regulation cannot be established using these existing techniques. To tackle these problems, we propose a data mining technique here. This technique makes use of a probabilistic inference approach to uncover interesting dependency relationships in noisy, high-dimensional time series expression data. It is not only able to determine if a gene is dependent on another but also whether or not it is activated or inhibited. In addition, it can predict how a gene would be affected by other genes even in unseen samples. For performance evaluation, the proposed technique has been tested with real expression data. Experimental results show that it can be very effective. The discovered dependency relationships can reveal gene regulatory relationships that could be used to infer the structures of GRNs.

Navigating public microarray databases

With the ever-escalating amount of data being produced by genome-wide microarray studies, it is of increasing importance that these data are captured in public databases so that researchers can use this information to complement and enhance their own studies. Many groups have set up databases of expression data, ranging from large repositories, which are designed to comprehensively capture all published data, through to more specialized databases. The public repositories, such as ArrayExpress at the European Bioinformatics Institute contain complete datasets in raw format in addition to processed data, whilst the specialist databases tend to provide downstream analysis of normalized data from more focused studies and data sources. Here we provide a guide to the use of these public microarray resources.

Website review: how to get the best from fission yeast genome data

Researchers are increasingly depending on various centralized resources to access the vast amount of information reported in the literature and generated by systematic sequencing and functional genomics projects. Biological databases have become everyday working tools for many researchers. This dependency goes both ways in that the databases require continuous feedback from the research community to maintain accurate, reliable, and upto- date information. The fission yeast Schizosaccharomyces pombe has recently been sequenced, setting the stage for the post-genome era of this popular model organism. Here, we provide an overview of relevant databases available, or being developed, together with a compilation of Internet resources containing useful information and tools for fission yeast.

Systematic planning of genome-scale experiments in poorly studied species

Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could readily be adapted to the design of other types of large-scale experiments as well as other groups of organisms.

Microarray expression experiments allow fast functional profiling of an organism's entire genome and significant efforts are devoted to analyzing the resulting data. Available genome sequences are also increasing quickly. However, it is unexplored how to use available functional genomics data to direct large-scale experiments in newly sequenced but poorly studied species. In this paper, we propose a strategy to systematically plan experimental treatments in the poorly studied species based on their model organism relatives. We consider both the accuracy of the datasets in capturing different biological processes and the redundancy between datasets. Quantifying the above information allows us to recommend a list of experimental treatments. We demonstrate the efficacy of this approach by designing, performing and evaluating S. bayanus microarray experiments using an available S. cerevisiae data repository. We show that this systematic planning process could reduce the labor in doing microarray experiments by 10 fold and achieve similar functional coverage.

The filamentous fungal gene expression database (FFGED)

Filamentous fungal gene expression assays provide essential information for understanding systemic cellular regulation. To aid research on fungal gene expression, we constructed a novel, comprehensive, free database, the filamentous fungal gene expression database (FFGED), available at http://bioinfo.townsend.yale.edu. FFGED features user-friendly management of gene expression data, which are assorted into experimental metadata, experimental design, raw data, normalized details, and analysis results. Data may be submitted in the process of an experiment, and any user can submit multiple experiments, thus classifying the FFGED as an "active experiment" database. Most importantly, FFGED functions as a collective and collaborative platform, by connecting each experiment with similar related experiments made public by other users, maximizing data sharing among different users, and correlating diverse gene expression levels under multiple experimental designs within different experiments. A clear and efficient web interface is provided with enhancement by AJAX (Asynchronous JavaScript and XML) and through a collection of tools to effectively facilitate data submission, sharing, retrieval and visualization.

Validation of reference genes for quantitative expression analysis by real-time RT-PCR in Saccharomyces cerevisiae

Background

Real-time RT-PCR is the recommended method for quantitative gene expression analysis. A compulsory step is the selection of good reference genes for normalization. A few genes often referred to as HouseKeeping Genes (HSK), such as ACT1, RDN18 or PDA1 are among the most commonly used, as their expression is assumed to remain unchanged over a wide range of conditions. Since this assumption is very unlikely, a geometric averaging of multiple, carefully selected internal control genes is now strongly recommended for normalization to avoid this problem of expression variation of single reference genes. The aim of this work was to search for a set of reference genes for reliable gene expression analysis in Saccharomyces cerevisiae.

Results

From public microarray datasets, we selected potential reference genes whose expression remained apparently invariable during long-term growth on glucose. Using the algorithm geNorm, ALG9, TAF10, TFC1 and UBC6 turned out to be genes whose expression remained stable, independent of the growth conditions and the strain backgrounds tested in this study. We then showed that the geometric averaging of any subset of three genes among the six most stable genes resulted in very similar normalized data, which contrasted with inconsistent results among various biological samples when the normalization was performed with ACT1. Normalization with multiple selected genes was therefore applied to transcriptional analysis of genes involved in glycogen metabolism. We determined an induction ratio of 100-fold for GPH1 and 20-fold for GSY2 between the exponential phase and the diauxic shift on glucose. There was no induction of these two genes at this transition phase on galactose, although in both cases, the kinetics of glycogen accumulation was similar. In contrast, SGA1 expression was independent of the carbon source and increased by 3-fold in stationary phase.

Conclusion

In this work, we provided a set of genes that are suitable reference genes for quantitative gene expression analysis by real-time RT-PCR in yeast biological samples covering a large panel of physiological states. In contrast, we invalidated and discourage the use of ACT1 as well as other commonly used reference genes (PDA1, TDH3, RDN18, etc) as internal controls for quantitative gene expression analysis in yeast.

Comparison of the transcriptomic "stress response" evoked by antimycin A and oxygen deprivation in Saccharomyces cerevisiae

BACKGROUND

Acute changes in environmental parameters (e.g., O2, pH, UV, osmolarity, nutrients, etc.) evoke a common transcriptomic response in yeast referred to as the "environmental stress response" (ESR) or "common environmental response" (CER). Why such a diverse array of insults should elicit a common transcriptional response remains enigmatic. Previous functional analyses of the networks involved have found that, in addition to up-regulating those for mitigating the specific stressor, the majority appear to be involved in balancing energetic supply and demand and modulating progression through the cell cycle. Here we compared functional and regulatory aspects of the stress responses elicited by the acute inhibition of respiration with antimycin A and oxygen deprivation under catabolite non-repressed (galactose) conditions.

RESULTS

Gene network analyses of the transcriptomic responses revealed both treatments result in the transient (10 - 60 min) down-regulation of MBF- and SBF-regulated networks involved in the G1/S transition of the cell cycle as well as Fhl1 and PAC/RRPE-associated networks involved in energetically costly programs of ribosomal biogenesis and protein synthesis. Simultaneously, Msn2/4 networks involved in hexose import/dissimilation, reserve energy regulation, and autophagy were transiently up-regulated. Interestingly, when cells were treated with antimycin A well before experiencing anaerobiosis these networks subsequently failed to respond to oxygen deprivation. These results suggest the transient stress response is elicited by the acute inhibition of respiration and, we postulate, changes in cellular energetics and/or the instantaneous growth rate, not oxygen deprivation per se. After a considerable delay (> or = 1 generation) under anoxia, predictable changes in heme-regulated gene networks (e.g., Hap1, Hap2/3/4/5, Mot3, Rox1 and Upc2) were observed both in the presence and absence of antimycin A.

CONCLUSION

This study not only differentiates between the gene networks that respond to respiratory inhibition and those that respond to oxygen deprivation but suggests the function of the ESR or CER is to balance energetic supply/demand and coordinate growth with the cell cycle, whether in response to perturbations that disrupt catabolic pathways or those that require rapidly up-regulating energetically costly programs for combating specific stressors.

The determinants of gene order conservation in yeasts

BACKGROUND

Why do some groups of physically linked genes stay linked over long evolutionary periods? Although several factors are associated with the formation of gene clusters in eukaryotic genomes, the particular contribution of each feature to clustering maintenance remains unclear.

RESULTS

We quantify the strength of the proposed factors in a yeast lineage. First we identify the magnitude of each variable to determine linkage conservation by using several comparator species at different distances to Saccharomyces cerevisiae. For adjacent gene pairs, in line with null simulations, intergenic distance acts as the strongest covariate. Which of the other covariates appear important depends on the comparator, although high co-expression is related to synteny conservation commonly, especially in the more distant comparisons, these being expected to reveal strong but relatively rare selection. We also analyze those pairs that are immediate neighbors through all the lineages considered. Current intergene distance is again the best predictor, followed by the local density of essential genes and co-regulation, with co-expression and recombination rate being the weakest predictors. The genome duplication seen in yeast leaves some mark on linkage conservation, as adjacent pairs resolved as single copy in all post-whole genome duplication species are more often found as adjacent in pre-duplication species.

CONCLUSION

Current intergene distance is consistently the strongest predictor of synteny conservation as expected under a simple null model. Other variables are of lesser importance and their relevance depends both on the species comparison in question and the fate of the duplicates following genome duplication.

Classifying transcription factor targets and discovering relevant biological features

Background

An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties.

Principal Findings

(1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4).

(2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression.

(3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties.

(4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter.

Conclusion

Postprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite.

Reviewers

This article was reviewed by Igor Jouline, Todd Mockler(nominated by Valerian Dolja), and Sandor Pongor.

Current approaches to gene regulatory network modelling

Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

BiologicalNetworks: visualization and analysis tool for systems biology

Systems level investigation of genomic scale information requires the development of truly integrated databases dealing with heterogeneous data, which can be queried for simple properties of genes or other database objects as well as for complex network level properties, for the analysis and modelling of complex biological processes. Towards that goal, we recently constructed PathSys, a data integration platform for systems biology, which provides dynamic integration over a diverse set of databases [Baitaluk et al. (2006) BMC Bioinformatics7, 55]. Here we describe a server, BiologicalNetworks, which provides visualization, analysis services and an information management framework over PathSys. The server allows easy retrieval, construction and visualization of complex biological networks, including genome-scale integrated networks of protein–protein, protein–DNA and genetic interactions. Most importantly, BiologicalNetworks addresses the need for systematic presentation and analysis of high-throughput expression data by mapping and analysis of expression profiles of genes or proteins simultaneously on to regulatory, metabolic and cellular networks. BiologicalNetworks Server is available at .

Changing perspectives in yeast research nearly a decade after the genome sequence

Research with budding yeast (Saccharomyces cerevisiae) has been transformed by the publication, nearly a decade ago, of the entire genome DNA sequence. The introduction of this first eukaryotic genomic sequence changed the yeast research environment significantly, not just because of dramatic progress in technical means but also because the sequence made accessible a new class of scientific questions. A central goal of yeast research remains the determination of the biological role of every sequence feature in the yeast genome. The most remarkable change has been the shift in perspective from focus on individual genes and functionalities to a more global view of how the cellular networks and systems interact and function together to produce the highly evolved organism we see today.

Clustering and re-clustering for pattern discovery in gene expression data

The combined interpretation of gene expression data and gene sequences is important for the investigation of the intricate relationships of gene expression at the transcription level. The expression data produced by microarray hybridization experiments can lead to the identification of clusters of co-expressed genes that are likely co-regulated by the same regulatory mechanisms. By analyzing the promoter regions of co-expressed genes, the common regulatory patterns characterized by transcription factor binding sites can be revealed. Many clustering algorithms have been used to uncover inherent clusters in gene expression data. In this paper, based on experiments using simulated and real data, we show that the performance of these algorithms could be further improved. For the clustering of expression data typically characterized by a lot of noise, we propose to use a two-phase clustering algorithm consisting of an initial clustering phase and a second re-clustering phase. The proposed algorithm has several desirable features: (i) it utilizes both local and global information by computing both a "local" pairwise distance between two gene expression profiles in Phase 1 and a "global" probabilistic measure of interestingness of cluster patterns in Phase 2, (ii) it distinguishes between relevant and irrelevant expression values when performing re-clustering, and (iii) it makes explicit the patterns discovered in each cluster for possible interpretations. Experimental results show that the proposed algorithm can be an effective algorithm for discovering clusters in the presence of very noisy data. The patterns that are discovered in each cluster are found to be meaningful and statistically significant, and cannot otherwise be easily discovered. Based on these discovered patterns, genes co-expressed under the same experimental conditions and range of expression levels have been identified and evaluated. When identifying regulatory patterns at the promoter regions of the co-expressed genes, we also discovered well-known transcription factor binding sites in them. These binding sites can provide explanations for the co-expressed patterns.

The use of MPSS for whole-genome transcriptional analysis in Arabidopsis

We have generated 36,991,173 17-base sequence "signatures" representing transcripts from the model plant Arabidopsis. These data were derived by massively parallel signature sequencing (MPSS) from 14 libraries and comprised 268,132 distinct sequences. Comparable data were also obtained with 20-base signatures. We developed a method for handling these data and for comparing these signatures to the annotated Arabidopsis genome. As part of this procedure, 858,019 potential or "genomic" signatures were extracted from the Arabidopsis genome and classified based on the position and orientation of the signatures relative to annotated genes. A comparison of genomic and expressed signatures matched 67,735 signatures predicted to be derived from distinct transcripts and expressed at significant levels. Expressed signatures were derived from the sense strand of at least 19,088 of 29,084 annotated genes. A comparison of the genomic and expression signatures demonstrated that approximately 7.7% of genomic signatures were underrepresented in the expression data. These genomic signatures contained one of 20 four-base words that were consistently associated with reduced MPSS abundances. More than 89% of the sum of the expressed signature abundances matched the Arabidopsis genome, and many of the unmatched signatures found in high abundances were predicted to match to previously uncharacterized transcripts.

Integrating whole-genome expression results into metabolic networks with Pathway Processor

Genes never act alone in a biological system, but participate in a cascade of networks. As a result, analyzing microarray data from a pathway perspective leads to a new level of understanding the system. The authors' group has recently developed Pathway Processor (http://cgr.harvard.edu/cavalieri/pp.html), an automatic statistical method to determine which pathways are most affected by transcriptional changes and to map expression data from multiple whole-genome expression experiments on metabolic pathways. This unit presents applications of the Pathway Processor software.

Stm1p, a G4 quadruplex and purine motif triplex nucleic acid-binding protein, interacts with ribosomes and subtelomeric Y' DNA in Saccharomyces cerevisiae

The Saccharomyces cerevisiae protein Stm1 was originally identified as a G4 quadruplex and purine motif triplex nucleic acid-binding protein. However, more recent studies have suggested a role for Stm1p in processes ranging from antiapoptosis to telomere maintenance. To better understand the biological role of Stm1p and its potential for G(*)G multiplex binding, we used epitope-tagged protein and immunological methods to identify the subcellular localization and protein and nucleic acid partners of Stm1p in vivo. Indirect immunofluorescence microscopy indicated that Stm1p is primarily a cytoplasmic protein, although a small percentage is also present in the nucleus. Conventional immunoprecipitation found that Stm1p is associated with ribosomal proteins and rRNA. This association was verified by rate zonal separation through sucrose gradients, which showed that Stm1p binds exclusively to mature 80 S ribosomes and polysomes. Chromatin immunoprecipitation experiments found that Stm1p preferentially binds telomere-proximal Y' element DNA sequences. Taken together, our data suggest that Stm1p is primarily a ribosome-associated protein, but one that can also interact with DNA, especially subtelomeric sequences. We discuss the implications of our findings in relation to prior genetic, genomic, and proteomic studies that have identified STM1 and/or Stm1p as well as the possible biological role of Stm1p.

SCMD: Saccharomyces cerevisiae Morphological Database

To study the global regulation of cell morphology, a number of groups have recently reported genome-wide screening data for yeast mutants with abnormal morphology. Despite the relatively simple ellipsoidal shape of yeast cells, in the past, cell morphology researchers have processed information on cells manually. These time-consuming, entirely subjective tasks motivated us to develop image-processing software that automatically extracts yeast cells from micrographs and processes them to measure key morphological characteristics such as cell size, roundness, bud neck position angle, nuclear DNA localization and actin localization. To date, we have retrieved 960,609 cells from 52,988 micrographs of 2531 mutants using our software, and we have published the results in the Saccharomyces cerevisiae Morphological Database (SCMD), which facilitates the analysis of abnormal cells. Our system provides quantitative data for shapes of the daughter and mother cells, localization of the nuclear DNA and morphology of the actin patches. To search for mutants with similar morphological traits, the system outputs a list of mutants ranked by similarity of average morphological parameters. The SCMD is available at http://yeast. gi.k.u-tokyo.ac.jp/.

From gene networks to gene function

We propose a novel method to identify functionally related genes based on comparisons of neighborhoods in gene networks. This method does not rely on gene sequence or protein structure homologies, and it can be applied to any organism and a wide variety of experimental data sets. The character of the predicted gene relationships depends on the underlying networks;they concern biological processes rather than the molecular function. We used the method to analyze gene networks derived from genome-wide chromatin immunoprecipitation experiments, a large-scale gene deletion study, and from the genomic positions of consensus binding sites for transcription factors of the yeast Saccharomyces cerevisiae. We identified 816 functional relationships between 159 genes and show that these relationships correspond to protein-protein interactions, co-occurrence in the same protein complexes, and/or co-occurrence in abstracts of scientific articles. Our results suggest functions for seven previously uncharacterized yeast genes: KIN3 and YMR269W may be involved in biological processes related to cell growth and/or maintenance, whereas IES6, YEL008W, YEL033W, YHL029C, YMR010W, and YMR031W-A are likely to have metabolic functions.

Role of transcriptional regulation in controlling fluxes in central carbon metabolism of Saccharomyces cerevisiae. A chemostat culture study

In contrast to batch cultivation, chemostat cultivation allows the identification of carbon source responses without interference by carbon-catabolite repression, accumulation of toxic products, and differences in specific growth rate. This study focuses on the yeast Saccharomyces cerevisiae, grown in aerobic, carbon-limited chemostat cultures. Genome-wide transcript levels and in vivo fluxes were compared for growth on two sugars, glucose and maltose, and for two C2-compounds, ethanol and acetate. In contrast to previous reports on batch cultures, few genes (180 genes) responded to changes of the carbon source by a changed transcript level. Very few transcript levels were changed when glucose as the growth-limiting nutrient was compared with maltose (33 transcripts), or when acetate was compared with ethanol (16 transcripts). Although metabolic flux analysis using a stoichiometric model revealed major changes in the central carbon metabolism, only 117 genes exhibited a significantly different transcript level when sugars and C2-compounds were provided as the growth-limiting nutrient. Despite the extensive knowledge on carbon source regulation in yeast, many of the carbon source-responsive genes encoded proteins with unknown or incompletely characterized biological functions. In silico promoter analysis of carbon source-responsive genes confirmed the involvement of several known transcriptional regulators and suggested the involvement of additional regulators. Transcripts involved in the glyoxylate cycle and gluconeogenesis showed a good correlation with in vivo fluxes. This correlation was, however, not observed for other important pathways, including the pentose-phosphate pathway, tricarboxylic acid cycle, and, in particular, glycolysis. These results indicate that in vivo fluxes in the central carbon metabolism of S. cerevisiae grown in steadystate, carbon-limited chemostat cultures are controlled to a large extent via post-transcriptional mechanisms.

Evidence for co-evolution of gene order and recombination rate

There is increasing evidence in eukaryotic genomes that gene order is not random, even allowing for tandem duplication. Notably, in numerous genomes, genes of similar expression tend to be clustered. Are there other reasons for clustering of functionally similar genes? If genes are linked to enable genetic, rather than physical clustering, then we also expect that clusters of certain genes might be associated with blocks of reduced recombination rates. Here we show that, in yeast, essential genes are highly clustered and this clustering is independent of clustering of co-expressed genes and of tandem duplications. Adjacent pairs of essential genes are preferentially conserved through evolution. Notably, we also find that clusters of essential genes are in regions of low recombination and that larger clusters have lower recombination rates. These results suggest that selection acts to modify both the fine-scale intragenomic variation in the recombination rate and the distribution of genes and provide evidence for co-evolution of gene order and recombination rate.

Isolation and characterization of nine microsatellite loci from the Hawaiian grouper Epinephelus quernus (Serranidae) for population genetic analyses

The availability of variable genetic markers for groupers (Serranidae) has generally been limited to mitochondrial DNA. For studies of population genetic structure, more loci are usually required; particularly useful are those that are nuclear in origin such as microsatellites. Here, we isolated and characterized 9 microsatellite loci from the endemic Hawaiian grouper Epinephelus quernus using a biotin-labeled oligonucleotide-streptavidin-coated magnetic bead approach. Of the 20 repeat-containing fragments isolated, 15 had sufficient flanking region in which to design primers. Among these, 9 produced consistent polymerase chain reaction product, and 6 were highly variable. These 6 loci were all composed of dinucleotide repeats, with the number of alleles ranging from 6 to 18, and heterozygosities from 33.3% to 91.7%. The high levels of variability observed should make these markers useful for population genetic studies of E. quernus, and potentially other epinephelines.

Genetically modified industrial yeast ready for application

Tremendous progress in the genetic engineering of yeast had been achieved at the end of 20th century, including the complete genome sequence, genome-wide gene expression profiling, and whole gene disruption strains. Nevertheless, genetically modified (GM) baking, brewing, wine, and sake yeasts have not, as yet, been used commercially, although numerous industrial recombinant yeasts have been constructed. The recent progress of genetic engineering for the construction of GM yeast is reviewed and possible requirements for their application are discussed. 'Self-cloning' yeast will be the most likely candidate for the first commercial application of GM microorganisms in food and beverage industries.

Cancer therapeutics in yeast

The budding yeast Saccharomyces cerevisiae is a genetically tractable model system with which to establish the cellular target of a given agent and investigate mechanisms of drug action.

Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks

We have developed a new tool to visualize expression data on metabolic pathways and to evaluate which metabolic pathways are most affected by transcriptional changes in whole-genome expression experiments. Using the Fisher Exact Test, the method scores biochemical pathways according to the probability that as many or more genes in a pathway would be significantly altered in a given experiment by chance alone. This method has been validated on diauxic shift experiments and reproduces well known effects of carbon source on yeast metabolism. The analysis is implemented with Pathway Analyzer, one of the tools of Pathway Processor, a new statistical package for the analysis of whole-genome expression data. Results from multiple experiments can be compared, reducing the analysis from the full set of individual genes to a limited number of pathways of interest. The pathways are visualized with OpenDX, an open-source visualization software package, and the relationship between genes in the pathways can be examined in detail using Expression Mapper, the second program of the package. This program features a graphical output displaying differences in expression on metabolic charts of the biochemical pathways to which the open reading frames are assigned.

Analysis of cell-cycle gene expression in Saccharomyces cerevisiae using microarrays and multiple synchronization methods

Microarray analysis of gene expression during the yeast division cycle has led to the proposal that a significant number of genes in Saccharomyces cerevisiae are expressed in a cell-cycle-specific manner. Four different methods of synchronization were used for cell-cycle analysis. Randomized data exhibit periodic patterns of lesser strength than the experimental data. Thus the cyclicities in the expression measurements in the four experiments presented do not arise from chance fluctuations or noise in the data. However, when the degree of cyclicity for genes in different experiments are compared, a large degree of non-reproducibility is found. Re-examining the phase timing of peak expression, we find that three of the experiments (those using alpha-factor, CDC28 and CDC15 synchronization) show consistent patterns of phasing, but the elutriation synchrony results demonstrate a different pattern from the other arrest-release synchronization methods. Specific genes can show a wide range of cyclical behavior between different experiments; a gene with high cyclicity in one experiment can show essentially no cyclicity in another experiment. The elutriation experiment, possibly being the least perturbing of the four synchronization methods, may give the most accurate characterization of the state of gene expression during the normal, unperturbed cell cycle. Under this alternative explanation, the observed cyclicities in the other three experiments are a stress response to synchronization, and may not reproduce in unperturbed cells.

The TRIPLES database: a community resource for yeast molecular biology

TRIPLES is a web-accessible database of TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces cerevisiae-a relational database housing nearly half a million data points generated from an ongoing study using large-scale transposon mutagenesis to characterize gene function in yeast. At present, TRIPLES contains three principal data sets (i.e. phenotypic data, protein localization data and expression data) for over 3500 annotated yeast genes as well as several hundred non-annotated open reading frames. In addition, the TRIPLES web site provides online order forms linked to each data set so that users may request any strain or reagent generated from this project free of charge. In response to user requests, the TRIPLES web site has undergone several recent modifications. Our localization data have been supplemented with approximately 500 fluorescent micrographs depicting actual staining patterns observed upon indirect immunofluorescence analysis of indicated epitope-tagged proteins. These localization data, as well as all other data sets within TRIPLES, are now available in full as tab-delimited text. To accommodate increased reagent requests, all orders are now cataloged in a separate database, and users are notified immediately of order receipt and shipment. Also, TRIPLES is one of five sites incorporated into the new functional analysis tool Function Junction provided by the Saccharomyces Genome Database. TRIPLES may be accessed from the Yale Genome Analysis Center (YGAC) homepage at http://ygac.med.yale.edu.

Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO)

The Saccharomyces Genome Database (SGD) resources, ranging from genetic and physical maps to genome-wide analysis tools, reflect the scientific progress in identifying genes and their functions over the last decade. As emphasis shifts from identification of the genes to identification of the role of their gene products in the cell, SGD seeks to provide its users with annotations that will allow relationships to be made between gene products, both within Saccharomyces cerevisiae and across species. To this end, SGD is annotating genes to the Gene Ontology (GO), a structured representation of biological knowledge that can be shared across species. The GO consists of three separate ontologies describing molecular function, biological process and cellular component. The goal is to use published information to associate each characterized S.cerevisiae gene product with one or more GO terms from each of the three ontologies. To be useful, this must be done in a manner that allows accurate associations based on experimental evidence, modifications to GO when necessary, and careful documentation of the annotations through evidence codes for given citations. Reaching this goal is an ongoing process at SGD. For information on the current progress of GO annotations at SGD and other participating databases, as well as a description of each of the three ontologies, please visit the GO Consortium page at http://www.geneontology.org. SGD gene associations to GO can be found by visiting our site at http://genome-www.stanford.edu/Saccharomyces/.

yMGV: helping biologists with yeast microarray data mining

yMGV (yeast Microarray Global Viewer) was designed to provide biologists with meaningful information from genome-wide yeast expression data. The database includes most of the available expression data published on yeast microarrays over the last 4 years. It provides customizable tools for the rapid visualization of expression profiles associated with a set of genes from all published experiments. It also allows users to compare the results from different publications so that they can identify genes with common expression profiles. We used yMGV to perform global analyses to find a gene expression profile specific for given biological conditions and to locate functional gene clusters on chromosomes. Other organisms will be added to this database. yMGV is accessible on the web at http://transcriptome.ens.fr/ymgv.

An integrated approach for finding overlooked genes in yeast

We report here the discovery of 137 previously unappreciated genes in yeast through a widely applicable and highly scalable approach integrating methods of gene-trapping, microarray-based expression analysis, and genome-wide homology searching. Our approach is a multistep process in which expressed sequences are first trapped using a modified transposon that produces protein fusions to beta-galactosidase (beta-gal); non-annotated open reading frames (ORFs) translated as beta-gal chimeras are selected as a candidate pool of potential genes. To verify expression of these sequences, labeled RNA is hybridized against a microarray of oligonucleotides designed to detect gene transcripts in a strand-specific manner. In complement to this experimental method, novel genes are also identified in silico by homology to previously annotated proteins. As these methods are capable of identifying both short ORFs and antisense ORFs, our approach provides an effective supplement to current gene-finding schemes. In total, the genes discovered using this approach constitute 2% of the yeast genome and represent a wealth of overlooked biology.

yMGV: a database for visualization and data mining of published genome-wide yeast expression data

The yeast Microarray Global Viewer (yMGV) is an on-line database providing a synthetic view of the transcriptional expression profiles of Saccharomyces cerevisiae genes in most of the published expression datasets. yMGV displays a one-screen graphical representation of gene expression variations for each published genome-wide experiment, allowing quick retrieval of experimental conditions affecting expression of this gene. yMGV also provides tools to isolate groups of genes sharing similar transcription profiles in a defined subset of experiments. Additionally, yMGV furnishes a set of statistical tools for critical assessment of published data. We therefore believe that yMGV is an efficient tool that affords a quick and comprehensive overview of microarray data and generates new gene classifications. As of 20 March 2001 the yMGV database contains 6 000 000 measurements, representing genome-wide expression comparisons of 932 experiments from 39 microarray publications. The yMGV interface is available at http://transcriptome.ens.fr/ymgv/.