Transcription factor binding site identification in yeast: a comparison of high-density oligonucleotide and PCR-based microarray platforms

Conserved signaling modules regulate filamentous growth in fungi: a model for eukaryotic cell differentiation

Eukaryotic organisms are composed of different cell types with defined shapes and functions. Specific cell types are produced by the process of cell differentiation, which is regulated by signal transduction pathways. Signaling pathways regulate cell differentiation by sensing cues and controlling the expression of target genes whose products generate cell types with specific attributes. In studying how cells differentiate, fungi have proved valuable models because of their ease of genetic manipulation and striking cell morphologies. Many fungal species undergo filamentous growth-a specialized growth pattern where cells produce elongated tube-like projections. Filamentous growth promotes expansion into new environments, including invasion into plant and animal hosts by fungal pathogens. The same signaling pathways that regulate filamentous growth in fungi also control cell differentiation throughout eukaryotes and include highly conserved mitogen-activated protein kinase (MAPK) pathways, which is the focus of this review. In many fungal species, mucin-type sensors regulate MAPK pathways to control filamentous growth in response to diverse stimuli. Once activated, MAPK pathways reorganize cell polarity, induce changes in cell adhesion, and promote the secretion of degradative enzymes that mediate access to new environments. However, MAPK pathway regulation is complicated because related pathways can share components with each other yet induce unique responses (i.e. signal specificity). In addition, MAPK pathways function in highly integrated networks with other regulatory pathways (i.e. signal integration). Here, we discuss signal specificity and integration in several yeast models (mainly Saccharomyces cerevisiae and Candida albicans) by focusing on the filamentation MAPK pathway. Because of the strong evolutionary ties between species, a deeper understanding of the regulation of filamentous growth in established models and increasingly diverse fungal species can reveal fundamentally new mechanisms underlying eukaryotic cell differentiation.

Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity

Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways' roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.

New Aspects of Invasive Growth Regulation Identified by Functional Profiling of MAPK Pathway Targets in Saccharomyces cerevisiae

MAPK pathways are drivers of morphogenesis and stress responses in eukaryotes. A major function of MAPK pathways is the transcriptional induction of target genes, which produce proteins that collectively generate a cellular response. One approach to comprehensively understand how MAPK pathways regulate cellular responses is to characterize the individual functions of their transcriptional targets. Here, by examining uncharacterized targets of the MAPK pathway that positively regulates filamentous growth in Saccharomyces cerevisiae (fMAPK pathway), we identified a new role for the pathway in negatively regulating invasive growth. Specifically, four targets were identified that had an inhibitory role in invasive growth: RPI1, RGD2, TIP1, and NFG1/YLR042cNFG1 was a highly induced unknown open reading frame that negatively regulated the filamentous growth MAPK pathway. We also identified SFG1, which encodes a transcription factor, as a target of the fMAPK pathway. Sfg1p promoted cell adhesion independently from the fMAPK pathway target and major cell adhesion flocculin Flo11p, by repressing genes encoding presumptive cell-wall-degrading enzymes. Sfg1p also contributed to FLO11 expression. Sfg1p and Flo11p regulated different aspects of cell adhesion, and their roles varied based on the environment. Sfg1p also induced an elongated cell morphology, presumably through a cell-cycle delay. Thus, the fMAPK pathway coordinates positive and negative regulatory proteins to fine-tune filamentous growth resulting in a nuanced response. Functional analysis of other pathways' targets may lead to a more comprehensive understanding of how signaling cascades generate biological responses.

The Saccharomyces cerevisiae Cdk8 Mediator Represses AQY1 Transcription by Inhibiting Set1p-Dependent Histone Methylation

In the budding yeast Saccharomyces cerevisiae, nutrient depletion induces massive transcriptional reprogramming that relies upon communication between transcription factors, post-translational histone modifications, and the RNA polymerase II holoenzyme complex. Histone H3Lys4 methylation (H3Lys4 me), regulated by the Set1p-containing COMPASS methyltransferase complex and Jhd2p demethylase, is one of the most well-studied histone modifications. We previously demonstrated that the RNA polymerase II mediator components cyclin C-Cdk8p inhibit locus-specific H3Lys4 3me independently of Jhd2p. Here, we identify loci subject to cyclin C- and Jhd2p-dependent histone H3Lys4 3me inhibition using chromatin immunoprecipitation (ChIP)-seq. We further characterized the independent and combined roles of cyclin C and Jhd2p in controlling H3Lys4 3me and transcription in response to fermentable and nonfermentable carbon at multiple loci. These experiments suggest that H3Lys4 3me alone is insufficient to induce transcription. Interestingly, we identified an unexpected role for cyclin C-Cdk8p in repressing AQY1 transcription, an aquaporin whose expression is normally induced during nutrient deprivation. These experiments, combined with previous work in other labs, support a two-step model in which cyclin C-Cdk8p mediate AQY1 transcriptional repression by stimulating transcription factor proteolysis and preventing Set1p recruitment to the AQY1 locus.

Fine-tuning of histone H3 Lys4 methylation during pseudohyphal differentiation by the CDK submodule of RNA polymerase II

Transcriptional regulation is dependent upon the interactions between the RNA pol II holoenzyme complex and chromatin. RNA pol II is part of a highly conserved multiprotein complex that includes the core mediator and CDK8 subcomplex. In Saccharomyces cerevisiae, the CDK8 subcomplex, composed of Ssn2p, Ssn3p, Ssn8p, and Srb8p, is thought to play important roles in mediating transcriptional control of stress-responsive genes. Also central to transcriptional control are histone post-translational modifications. Lysine methylation, dynamically balanced by lysine methyltransferases and demethylases, has been intensively studied, uncovering significant functions in transcriptional control. A key question remains in understanding how these enzymes are targeted during stress response. To determine the relationship between lysine methylation, the CDK8 complex, and transcriptional control, we performed phenotype analyses of yeast lacking known lysine methyltransferases or demethylases in isolation or in tandem with SSN8 deletions. We show that the RNA pol II CDK8 submodule components SSN8/SSN3 and the histone demethylase JHD2 are required to inhibit pseudohyphal growth-a differentiation pathway induced during nutrient limitation-under rich conditions. Yeast lacking both SSN8 and JHD2 constitutively express FLO11, a major regulator of pseudohyphal growth. Interestingly, deleting known FLO11 activators including FLO8, MSS11, MFG1, TEC1, SNF1, KSS1, and GCN4 results in a range of phenotypic suppression. Using chromatin immunoprecipitation, we found that SSN8 inhibits H3 Lys4 trimethylation independently of JHD2 at the FLO11 locus, suggesting that H3 Lys4 hypermethylation is locking FLO11 into a transcriptionally active state. These studies implicate the CDK8 subcomplex in fine-tuning H3 Lys4 methylation levels during pseudohyphal differentiation.

Relating gene expression evolution with CpG content changes

Background

Previous studies have shown that CpG dinucleotides are enriched in a subset of promoters and the CpG content of promoters is positively correlated with gene expression levels. But the relationship between divergence of CpG content and gene expression evolution has not been investigated. Here we calculate the normalized CpG (nCpG) content in DNA regions around transcription start site (TSS) and transcription terminal site (TTS) of genes in nine organisms, and relate them with expression levels measured by RNA-seq.

Results

The nCpG content of TSS shows a bimodal distribution in all organisms except platypus, whereas the nCpG content of TTS only has a single peak. When the nCpG contents are compared between different organisms, we observe a different evolution pattern between TSS and TTS: compared with TTS, TSS exhibits a faster divergence rate between closely related species but are more conserved between distant species. More importantly, we demonstrate the link between gene expression evolution and nCpG content changes: up-/down- regulation of genes in an organism is accompanied by the nCpG content increase/decrease in their TSS and TTS proximal regions.

Conclusions

Our results suggest that gene expression changes between different organisms are correlated with the alterations in normalized CpG contents of promoters. Our analyses provide evidences for the impact of nCpG content on gene expression evolution.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-693) contains supplementary material, which is available to authorized users.

Structure of the transcriptional network controlling white-opaque switching in Candida albicans

The human fungal pathogen Candida albicans can switch between two phenotypic cell types, termed 'white' and 'opaque'. Both cell types are heritable for many generations, and the switch between the two types occurs epigenetically, that is, without a change in the primary DNA sequence of the genome. Previous work identified six key transcriptional regulators important for white-opaque switching: Wor1, Wor2, Wor3, Czf1, Efg1, and Ahr1. In this work, we describe the structure of the transcriptional network that specifies the white and opaque cell types and governs the ability to switch between them. In particular, we use a combination of genome-wide chromatin immunoprecipitation, gene expression profiling, and microfluidics-based DNA binding experiments to determine the direct and indirect regulatory interactions that form the switch network. The six regulators are arranged together in a complex, interlocking network with many seemingly redundant and overlapping connections. We propose that the structure (or topology) of this network is responsible for the epigenetic maintenance of the white and opaque states, the switching between them, and the specialized properties of each state.

Flavour-active wine yeasts

The flavour of fermented beverages such as beer, cider, saké and wine owe much to the primary fermentation yeast used in their production, Saccharomyces cerevisiae. Where once the role of yeast in fermented beverage flavour was thought to be limited to a small number of volatile esters and higher alcohols, the discovery that wine yeast release highly potent sulfur compounds from non-volatile precursors found in grapes has driven researchers to look more closely at how choice of yeast can influence wine style. This review explores recent progress towards understanding the range of ‘flavour phenotypes’ that wine yeast exhibit, and how this knowledge has been used to develop novel flavour-active yeasts. In addition, emerging opportunities to augment these phenotypes by engineering yeast to produce so-called grape varietal compounds, such as monoterpenoids, will be discussed.

A conserved transcriptional regulator governs fungal morphology in widely diverged species

Fungi exhibit a large variety of morphological forms. Here, we examine the functions of a deeply conserved regulator of morphology in three fungal species: Saccharomyces cerevisiae, Candida albicans, and Histoplasma capsulatum. We show that, despite an estimated 600 million years since those species diverged from a common ancestor, Wor1 in C. albicans, Ryp1 in H. capsulatum, and Mit1 in S. cerevisiae are transcriptional regulators that recognize the same DNA sequence. Previous work established that Wor1 regulates white-opaque switching in C. albicans and that its ortholog Ryp1 regulates the yeast to mycelial transition in H. capsulatum. Here we show that the ortholog Mit1 in S. cerevisiae is also a master regulator of a morphological transition, in this case pseudohyphal growth. Full-genome chromatin immunoprecipitation experiments show that Mit1 binds to the control regions of the previously known regulators of pseudohyphal growth as well as those of many additional genes. Through a comparison of binding sites for Mit1 in S. cerevisiae, Wor1 in C. albicans, and Wor1 ectopically expressed in S. cerevisiae, we conclude that the genes controlled by the orthologous regulators overlap only slightly between these two species despite the fact that the DNA binding specificity of the regulators has remained largely unchanged. We suggest that the ancestral Wor1/Mit1/Ryp1 protein controlled aspects of cell morphology and that movement of genes in and out of the Wor1/Mit1/Ryp1 regulon is responsible, in part, for the differences of morphological forms among these species.

Bayesian analysis of high-throughput quantitative measurement of protein-DNA interactions

Transcriptional regulation depends upon the binding of transcription factor (TF) proteins to DNA in a sequence-dependent manner. Although many experimental methods address the interaction between DNA and proteins, they generally do not comprehensively and accurately assess the full binding repertoire (the complete set of sequences that might be bound with at least moderate strength). Here, we develop and evaluate through simulation an experimental approach that allows simultaneous high-throughput quantitative analysis of TF binding affinity to thousands of potential DNA ligands. Tens of thousands of putative binding targets can be mixed with a TF, and both the pre-bound and bound target pools sequenced. A hierarchical Bayesian Markov chain Monte Carlo approach determines posterior estimates for the dissociation constants, sequence-specific binding energies, and free TF concentrations. A unique feature of our approach is that dissociation constants are jointly estimated from their inferred degree of binding and from a model of binding energetics, depending on how many sequence reads are available and the explanatory power of the energy model. Careful experimental design is necessary to obtain accurate results over a wide range of dissociation constants. This approach, which we call Simultaneous Ultra high-throughput Ligand Dissociation EXperiment (SULDEX), is theoretically capable of rapid and accurate elucidation of an entire TF-binding repertoire.

Transcriptional regulation and the diversification of metabolism in wine yeast strains

Transcription factors and their binding sites have been proposed as primary targets of evolutionary adaptation because changes to single transcription factors can lead to far-reaching changes in gene expression patterns. Nevertheless, there is very little concrete evidence for such evolutionary changes. Industrial wine yeast strains, of the species Saccharomyces cerevisiae, are a geno- and phenotypically diverse group of organisms that have adapted to the ecological niches of industrial winemaking environments and have been selected to produce specific styles of wine. Variation in transcriptional regulation among wine yeast strains may be responsible for many of the observed differences and specific adaptations to different fermentative conditions in the context of commercial winemaking. We analyzed gene expression profiles of wine yeast strains to assess the impact of transcription factor expression on metabolic networks. The data provide new insights into the molecular basis of variations in gene expression in industrial strains and their consequent effects on metabolic networks important to wine fermentation. We show that the metabolic phenotype of a strain can be shifted in a relatively predictable manner by changing expression levels of individual transcription factors, opening opportunities to modify transcription networks to achieve desirable outcomes.

Global screening of potential Candida albicans biofilm-related transcription factors via network comparison

BACKGROUND

Candida albicans is a commonly encountered fungal pathogen in humans. The formation of biofilm is a major virulence factor in C. albicans pathogenesis and is related to antidrug resistance of this organism. Although many factors affecting biofilm have been analyzed, molecular mechanisms that regulate biofilm formation still await to be elucidated.

RESULTS

In this study, from the gene regulatory network perspective, we developed an efficient computational framework, which integrates different kinds of data from genome-scale analysis, for global screening of potential transcription factors (TFs) controlling C. albicans biofilm formation. S. cerevisiae information and ortholog data were used to infer the possible TF-gene regulatory associations in C. albicans. Based on TF-gene regulatory associations and gene expression profiles, a stochastic dynamic model was employed to reconstruct the gene regulatory networks of C. albicans biofilm and planktonic cells. The two networks were then compared and a score of relevance value (RV) was proposed to determine and assign the quantity of correlation of each potential TF with biofilm formation. A total of twenty-three TFs are identified to be related to the biofilm formation; ten of them are previously reported by literature evidences.

CONCLUSIONS

The results indicate that the proposed screening method can successfully identify most known biofilm-related TFs and also identify many others that have not been previously reported. Together, this method can be employed as a pre-experiment screening approach that reveals new target genes for further characterization to understand the regulatory mechanisms in biofilm formation, which can serve as the starting point for therapeutic intervention of C. albicans infections.

The origin recognition complex interacts with a subset of metabolic genes tightly linked to origins of replication

The origin recognition complex (ORC) marks chromosomal sites as replication origins and is essential for replication initiation. In yeast, ORC also binds to DNA elements called silencers, where its primary function is to recruit silent information regulator (SIR) proteins to establish transcriptional silencing. Indeed, silencers function poorly as chromosomal origins. Several genetic, molecular, and biochemical studies of HMR-E have led to a model proposing that when ORC becomes limiting in the cell (such as in the orc2-1 mutant) only sites that bind ORC tightly (such as HMR-E) remain fully occupied by ORC, while lower affinity sites, including many origins, lose ORC occupancy. Since HMR-E possessed a unique non-replication function, we reasoned that other tight sites might reveal novel functions for ORC on chromosomes. Therefore, we comprehensively determined ORC “affinity” genome-wide by performing an ORC ChIP–on–chip in ORC2 and orc2-1 strains. Here we describe a novel group of orc2-1–resistant ORC–interacting chromosomal sites (ORF–ORC sites) that did not function as replication origins or silencers. Instead, ORF–ORC sites were comprised of protein-coding regions of highly transcribed metabolic genes. In contrast to the ORC–silencer paradigm, transcriptional activation promoted ORC association with these genes. Remarkably, ORF–ORC genes were enriched in proximity to origins of replication and, in several instances, were transcriptionally regulated by these origins. Taken together, these results suggest a surprising connection among ORC, replication origins, and cellular metabolism.

Chromosomes must be replicated prior to cell division. The process of duplication of each eukaryotic chromosome starts at discrete sites called origins of replication. An evolutionarily conserved Origin Recognition Complex (ORC) binds origins and helps make them replication-competent. ORC also binds another class of chromosomal sites that primarily function not as origins but as “silencers.” Silencers serve as starting points for the formation of silent chromatin, a special structure that represses local gene transcription in a promoter-independent fashion. One yeast silencer studied in great detail was found to bind ORC in vitro and in vivo with high affinity (“tightly”). On the other hand, several replication origins were found to bind ORC with lower affinity (“loosely”). We performed a genome-wide comparison of ORC affinity and found a novel class of high-affinity ORC–binding sites. Surprisingly, this class consisted neither of origins nor of silencers but of highly expressed genes involved in various metabolic processes. Transcriptional activation helped target ORC to these sites. These genes were frequently found near origins of replication, and in several instances their transcription was affected by deletion of the nearby origin. These results may shed light on a new molecular mechanism connecting nutrient status and cell division.

Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data

Transcriptional cooperativity among several transcription factors (TFs) is believed to be the main mechanism of complexity and precision in transcriptional regulatory programs. Here, we present a Bayesian network framework to reconstruct a high-confidence whole-genome map of transcriptional cooperativity in Saccharomyces cerevisiae by integrating a comprehensive list of 15 genomic features. We design a Bayesian network structure to capture the dominant correlations among features and TF cooperativity, and introduce a supervised learning framework with a well-constructed gold-standard dataset. This framework allows us to assess the predictive power of each genomic feature, validate the superior performance of our Bayesian network compared to alternative methods, and integrate genomic features for optimal TF cooperativity prediction. Data integration reveals 159 high-confidence predicted cooperative relationships among 105 TFs, most of which are subsequently validated by literature search. The existing and predicted transcriptional cooperativities can be grouped into three categories based on the combination patterns of the genomic features, providing further biological insights into the different types of TF cooperativity. Our methodology is the first supervised learning approach for predicting transcriptional cooperativity, compares favorably to alternative unsupervised methodologies, and can be applied to other genomic data integration tasks where high-quality gold-standard positive data are scarce.

Dynamic and complex transcription factor binding during an inducible response in yeast

Complex biological processes are often regulated, at least in part, by the binding of transcription factors to their targets. Recently, considerable effort has been made to analyze the binding of relevant factors to the suite of targets they regulate, thereby generating a regulatory circuit map. However, for most studies the dynamics of binding have not been analyzed, and thus the temporal order of events and mechanisms by which this occurs are poorly understood. We globally analyzed in detail the temporal order of binding of several key factors involved in the salt response of yeast to their target genes. Analysis of Yap4 and Sko1 binding to their target genes revealed multiple temporal classes of binding patterns: (1) constant binding, (2) rapid induction, (3) slow induction, and (4) transient induction. These results demonstrate that individual transcription factors can have multiple binding patterns and help define the different types of temporal binding patterns used in eukaryotic gene regulation. To investigate these binding patterns further, we also analyzed the binding of seven other key transcription factors implicated in osmotic regulation, including Hot1, Msn1, Msn2, Msn4, Skn7, and Yap6, and found significant coassociation among the different factors at their gene targets. Moreover, the binding of several key factors was correlated with distinct classes of Yap4- and Sko1-binding patterns and with distinct types of genes. Gene expression studies revealed association of Yap4, Sko1, and other transcription factor-binding patterns with different gene expression patterns. The integration and analysis of binding and expression information reveals a complex dynamic and hierarchical circuit in which specific combinations of transcription factors target distinct sets of genes at discrete times to coordinate a rapid and important biological response.

High-resolution DNA-binding specificity analysis of yeast transcription factors

Transcription factors (TFs) regulate the expression of genes through sequence-specific interactions with DNA-binding sites. However, despite recent progress in identifying in vivo TF binding sites by microarray readout of chromatin immunoprecipitation (ChIP-chip), nearly half of all known yeast TFs are of unknown DNA-binding specificities, and many additional predicted TFs remain uncharacterized. To address these gaps in our knowledge of yeast TFs and their cis regulatory sequences, we have determined high-resolution binding profiles for 89 known and predicted yeast TFs, over more than 2.3 million gapped and ungapped 8-bp sequences ("k-mers"). We report 50 new or significantly different direct DNA-binding site motifs for yeast DNA-binding proteins and motifs for eight proteins for which only a consensus sequence was previously known; in total, this corresponds to over a 50% increase in the number of yeast DNA-binding proteins with experimentally determined DNA-binding specificities. Among other novel regulators, we discovered proteins that bind the PAC (Polymerase A and C) motif (GATGAG) and regulate ribosomal RNA (rRNA) transcription and processing, core cellular processes that are constituent to ribosome biogenesis. In contrast to earlier data types, these comprehensive k-mer binding data permit us to consider the regulatory potential of genomic sequence at the individual word level. These k-mer data allowed us to reannotate in vivo TF binding targets as direct or indirect and to examine TFs' potential effects on gene expression in approximately 1,700 environmental and cellular conditions. These approaches could be adapted to identify TFs and cis regulatory elements in higher eukaryotes.

Fus3-triggered Tec1 degradation modulates mating transcriptional output during the pheromone response

The yeast transcription factor Ste12 controls both mating and filamentation pathways. Upon pheromone induction, the mitogen-activated protein kinases, Fus3 and Kss1, activate Ste12 by relieving the repression of two functionally redundant Ste12 inhibitors, Dig1 and Dig2. Mating genes are controlled by the Ste12/Dig1/Dig2 complex through Ste12-binding sites, whereas filamentation genes are regulated by the Tec1/Ste12/Dig1 complex through Tec1-binding sites. The two Ste12 complexes are mutually exclusive. During pheromone response, Tec1 is degraded upon phosphorylation by Fus3, preventing cross-activation of the filamentation pathway. Here, we show that a stable Tec1 also impairs the induction of mating genes. A mathematical model is developed to capture the dynamic formation of the two Ste12 complexes and their interactions with pathway-specific promoters. By model simulations and experimentation, we show that excess Tec1 can impair the mating transcriptional output because of its ability to sequester Ste12, and because of a novel function of Dig2 for the transcription of mating genes. We suggest that Fus3-triggered Tec1 degradation is an important part of the transcriptional induction of mating genes during the pheromone response.

YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae

The Yeast search for transcriptional regulators and consensus tracking (YEASTRACT) information system (www.yeastract.com) was developed to support the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in September 2007, this database contains over 30 990 regulatory associations between Transcription Factors (TFs) and target genes and includes 284 specific DNA binding sites for 108 characterized TFs. Computational tools are also provided to facilitate the exploitation of the gathered data when solving a number of biological questions, in particular the ones that involve the analysis of global gene expression results. In this new release, YEASTRACT includes DISCOVERER, a set of computational tools that can be used to identify complex motifs over-represented in the promoter regions of co-regulated genes. The motifs identified are then clustered in families, represented by a position weight matrix and are automatically compared with the known transcription factor binding sites described in YEASTRACT. Additionally, in this new release, it is possible to generate graphic depictions of transcriptional regulatory networks for documented or potential regulatory associations between TFs and target genes. The visual display of these networks of interactions is instrumental in functional studies. Tutorials are available on the system to exemplify the use of all the available tools.