YEASTRACT Database Research Articles

Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast.

Causal gene networks model the flow of information within a cell. Reconstructing causal networks from omics data is challenging because correlation does not imply causation. When genomics and transcriptomics data from a segregating population are combined, genomic variants can be used to orient the direction of causality between gene expression traits. Instrumental variable methods use a local expression quantitative trait locus (eQTL) as a randomized instrument for a gene's expression level, and assign target genes based on distal eQTL associations. Mediation-based methods additionally require that distal eQTL associations are mediated by the source gene. A detailed comparison between these methods has not yet been conducted, due to the lack of a standardized implementation of different methods, the limited sample size of most multi-omics datasets, and the absence of ground-truth networks for most organisms. Here we used Findr, a software package providing uniform implementations of instrumental variable, mediation, and coexpression-based methods, a recent dataset of 1012 segregants from a cross between two budding yeast strains, and the Yeastract database of known transcriptional interactions to compare causal gene network inference methods. We found that causal inference methods result in a significant overlap with the ground-truth, whereas coexpression did not perform better than random. A subsampling analysis revealed that the performance of mediation saturates at large sample sizes, due to a loss of sensitivity when residual correlations become significant. Instrumental variable methods on the other hand contain false positive predictions, due to genomic linkage between eQTL instruments. Instrumental variable and mediation-based methods also have complementary roles for identifying causal genes underlying transcriptional hotspots. Instrumental variable methods correctly predicted STB5 targets for a hotspot centred on the transcription factor STB5, whereas mediation failed due to Stb5p auto-regulating its own expression. Mediation suggests a new candidate gene, DNM1, for a hotspot on Chr XII, whereas instrumental variable methods could not distinguish between multiple genes located within the hotspot. In conclusion, causal inference from genomics and transcriptomics data is a powerful approach for reconstructing causal gene networks, which could be further improved by the development of methods to control for residual correlations in mediation analyses, and for genomic linkage and pleiotropic effects from transcriptional hotspots in instrumental variable analyses.

Assessing regulatory features of the current transcriptional network of Saccharomyces cerevisiae

The capacity of living cells to adapt to different environmental, sometimes adverse, conditions is achieved through differential gene expression, which in turn is controlled by a highly complex transcriptional network. We recovered the full network of transcriptional regulatory associations currently known for Saccharomyces cerevisiae, as gathered in the latest release of the YEASTRACT database. We assessed topological features of this network filtered by the kind of supporting evidence and of previously published networks. It appears that in-degree distribution, as well as motif enrichment evolve as the yeast transcriptional network is being completed. Overall, our analyses challenged some results previously published and confirmed others. These analyses further pointed towards the paucity of experimental evidence to support theories and, more generally, towards the partial knowledge of the complete network.

Modeling the Dynamics of a Family of Small Gene Regulatory Networks in which Single Edges Have Been Deleted Indicates Regulation by Hmo1, Msn2, and Cin5 Are Important for Controlling the Cold Shock Response in Saccharomyces cerevisiae

A gene regulatory network (GRN) is a set of transcription factors which regulate the level of expression of genes encoding other transcription factors. The dynamics of a GRN show how gene expression in the network changes over time. Microarray data were obtained from the Saccharomyces cerevisiae wild type strain and five transcription factor deletion strains (Δcin5, Δgln3, Δhap4, Δhmo1, Δzap1) before cold shock at 13°C and 15, 30, and 60 minutes after cold shock (NCBI GEO Series GSE83656). Genes that showed a significant change in expression were submitted to the YEASTRACT database to determine which transcription factors regulated them and to generate a candidate GRN of 15 nodes (transcription factors) and 28 edges (regulatory relationships). The edges of this intact network were then systematically deleted one‐at‐a‐time to create a family of 28 additional networks to determine the importance of each edge in the network. We used the open source software, GRNmap ( http://kdahlquist.github.io/GRNmap/), to model the dynamics of these GRNs. GRNmap models the change in expression for each gene as the production of mRNA minus its degradation using differential equations with a sigmoidal production function. Given published mRNA degradation rates (Neymotin et al. 2014; doi: 10.1261/rna.045104.114) and cold shock microarray data, GRNmap then estimated the production rates and expression thresholds for each gene, and regulatory weights for each edge, which denote the direction (activation or repression) and strength of the regulatory relationships. Edge weights were then visualized with the open source software GRNsight (Dahlquist et al. 2014; doi: 10.7717/peerj‐cs.85; https://dondi.github.io/GRNsight/). Figure 1 shows the intact GRN. Red edges indicate activation; blue edges indicate repression. Node stripes left to right indicate gene expression at 15, 30, and 60 minutes of cold shock, red is increased expression; blue is decreased. To evaluate the goodness of fit of the model, GRNmap reports the least squares error (LSE) between the model and the microarray data. The LSE was compared to the minimum theoretical least squares error (minLSE) achievable due to variation in the data, to determine the model performance between networks. In the 28 edge‐deletion networks, LSE:minLSE ratios indicated that five networks performed better than the intact network, ten networks performed about the same, and thirteen performed worse. The edge‐deletions involving the Hmo1, Msn2, and Cin5 transcription factors resulted in a poor fit of the model to data, indicating that those edges represent important regulatory relationships in the cold shock response. K‐means clustering was performed on the edge weight values from the intact network and edge‐deletion networks. An examination of the clusters showed that weight values deviated from those of the intact network for 6 of 9 edge‐deletions involving Msn2, 4 of 5 edge‐deletions involving Hmo1, and 3 of 6 edge‐deletions involving Cin5, further reinforcing their importance in the network controlling the cold shock response in yeast.Support or Funding InformationLoyola Marymount University Rains Research Assistant Program (A.K.F.)Figure 1

HSF1Base: A Comprehensive Database of HSF1 (Heat Shock Factor 1) Target Genes.

HSF1 (heat shock factor 1) is an evolutionarily conserved master transcriptional regulator of the heat shock response (HSR) in eukaryotic cells. In response to high temperatures, HSF1 upregulates genes encoding molecular chaperones, also called heat shock proteins, which assist the refolding or degradation of damaged intracellular proteins. Accumulating evidence reveals however that HSF1 participates in several other physiological and pathological processes such as differentiation, immune response, and multidrug resistance, as well as in ageing, neurodegenerative demise, and cancer. To address how HSF1 controls these processes one should systematically analyze its target genes. Here we present a novel database called HSF1Base (hsf1base.org) that contains a nearly comprehensive list of HSF1 target genes identified so far. The list was obtained by manually curating publications on individual HSF1 targets and analyzing relevant high throughput transcriptomic and chromatin immunoprecipitation data derived from the literature and the Yeastract database. To support the biological relevance of HSF1 targets identified by high throughput methods, we performed an enrichment analysis of (potential) HSF1 targets across different tissues/cell types and organisms. We found that general HSF1 functions (targets are expressed in all tissues/cell types) are mostly related to cellular proteostasis. Furthermore, HSF1 targets that are conserved across various animal taxa operate mostly in cellular stress pathways (e.g., autophagy), chromatin remodeling, ribosome biogenesis, and ageing. Together, these data highlight diverse roles for HSF1, expanding far beyond the HSR.

YEASTRACT+: a portal for cross-species comparative genomics of transcription regulation in yeasts.

The YEASTRACT+ information system (http://YEASTRACT-PLUS.org/) is a wide-scope tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in yeasts of biotechnological or human health relevance. YEASTRACT+ is a new portal that integrates the previously existing YEASTRACT (http://www.yeastract.com/) and PathoYeastract (http://pathoyeastract.org/) databases and introduces the NCYeastract (Non-Conventional Yeastract) database (http://ncyeastract.org/), focused on the so-called non-conventional yeasts. The information in the YEASTRACT database, focused on Saccharomyces cerevisiae, was updated. PathoYeastract was extended to include two additional pathogenic yeast species: Candida parapsilosis and Candida tropicalis. Furthermore, the NCYeastract database was created, including five biotechnologically relevant yeast species: Zygosaccharomyces baillii, Kluyveromyces lactis, Kluyveromyces marxianus, Yarrowia lipolytica and Komagataella phaffii. The YEASTRACT+ portal gathers 289 706 unique documented regulatory associations between transcription factors (TF) and target genes and 420 DNA binding sites, considering 247 TFs from 10 yeast species. YEASTRACT+ continues to make available tools for the prediction of the TFs involved in the regulation of gene/genomic expression. In this release, these tools were upgraded to enable predictions based on orthologous regulatory associations described for other yeast species, including two new tools for cross-species transcription regulation comparison, based on multi-species promoter and TF regulatory network analyses.

YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in Saccharomyces cerevisiae.

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT—www.yeastract.com) information system has been, for 11 years, a key tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in Saccharomyces cerevisiae. Since its last update in June 2017, YEASTRACT includes approximately 163000 regulatory associations between transcription factors (TF) and target genes in S. cerevisiae, based on more than 1600 bibliographic references; it also includes 247 specific DNA binding consensus recognized by 113 TFs. This release of the YEASTRACT database provides new visualization tools to visualize each regulatory network in an interactive fashion, enabling the user to select and observe subsets of the network such as: (i) considering only DNA binding evidence or both DNA binding and expression evidence; (ii) considering only either positive or negative regulatory associations; or (iii) considering only one set of related environmental conditions. A further tool to observe TF regulons is also offered, enabling a clear-cut understanding of the exact meaning of the available data. We believe that with this new version, YEASTRACT will improve its role as an open web resource instrumental for Yeast Biologists and Systems Biology researchers.

The PathoYeastract database: an information system for the analysis of gene and genomic transcription regulation in pathogenic yeasts.

We present the PATHOgenic YEAst Search for Transcriptional Regulators And Consensus Tracking (PathoYeastract - http://pathoyeastract.org) database, a tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in the pathogenic yeasts Candida albicans and C. glabrata. Upon data retrieval from hundreds of publications, followed by curation, the database currently includes 28 000 unique documented regulatory associations between transcription factors (TF) and target genes and 107 DNA binding sites, considering 134 TFs in both species. Following the structure used for the YEASTRACT database, PathoYeastract makes available bioinformatics tools that enable the user to exploit the existing information to predict the TFs involved in the regulation of a gene or genome-wide transcriptional response, while ranking those TFs in order of their relative importance. Each search can be filtered based on the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. Promoter analysis tools and interactive visualization tools for the representation of TF regulatory networks are also provided. The PathoYeastract database further provides simple tools for the prediction of gene and genomic regulation based on orthologous regulatory associations described for other yeast species, a comparative genomics setup for the study of cross-species evolution of regulatory networks.

YCRD: Yeast Combinatorial Regulation Database.

In eukaryotes, the precise transcriptional control of gene expression is typically achieved through combinatorial regulation using cooperative transcription factors (TFs). Therefore, a database which provides regulatory associations between cooperative TFs and their target genes is helpful for biologists to study the molecular mechanisms of transcriptional regulation of gene expression. Because there is no such kind of databases in the public domain, this prompts us to construct a database, called Yeast Combinatorial Regulation Database (YCRD), which deposits 434,197 regulatory associations between 2535 cooperative TF pairs and 6243 genes. The comprehensive collection of more than 2500 cooperative TF pairs was retrieved from 17 existing algorithms in the literature. The target genes of a cooperative TF pair (e.g. TF1-TF2) are defined as the common target genes of TF1 and TF2, where a TF’s experimentally validated target genes were downloaded from YEASTRACT database. In YCRD, users can (i) search the target genes of a cooperative TF pair of interest, (ii) search the cooperative TF pairs which regulate a gene of interest and (iii) identify important cooperative TF pairs which regulate a given set of genes. We believe that YCRD will be a valuable resource for yeast biologists to study combinatorial regulation of gene expression. YCRD is available at http://cosbi.ee.ncku.edu.tw/YCRD/ or http://cosbi2.ee.ncku.edu.tw/YCRD/.

Modeling the dynamics of a 21‐gene, 50‐edge gene regulatory network controlling the transcriptional response to cold shock in Saccharomyces cerevisiae using GRNmap

Gene expression is regulated by proteins called transcription factors which can either repress or activate a gene's transcriptional output. A gene regulatory network (GRN) consists of a set of transcription factors that regulate the level of expression of genes encoding other transcription factors. The dynamics of a GRN show how gene expression in the network changes over time. Previously in the lab, a MATLAB software package called GRNmap was developed that uses ordinary differential equations to model the dynamics of medium‐scale GRNs from budding yeast, Saccharomyces cerevisiae. The program estimates production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on DNA microarray data, and then performs a forward simulation of the dynamics of the network. DNA microarray data for 6189 yeast genes was obtained from the Dahlquist lab where they subjected yeast to cold shock at 13°C and measured gene expression at three time points (after 15, 30, and 60 minutes of cold shock). We performed LOESS normalization using the limma package in R and used a modified ANOVA to determine which genes had a log2 fold change significantly different than zero at any of the timepoints studied. Using GRNmap, we estimated the parameters of a GRN derived from the YEASTRACT database, consisting of 21 nodes (transcription factors) and 50 edges (regulatory relationships). To answer our fundamental question, whether the network accurately models what actually occurs during cold shock in the yeast cell, we analyzed the results of the modeling as follows. For each gene we evaluated how well the simulated data generated by the model fit the expression profile measured by the microarrays. Factors contributing to the goodness of fit included whether the transcripton factor itself exhibited significant dynamics (in the ANOVA test) and whether the regulators of that transcription actor also showed significant dynamics. This analysis showed that, while a few genes were modeled well, others were not, potentially because they are regulated by other transcription factors that were not present in the network modeled. To investigate this further, we compared the results of the modeling of the YEASTRACT‐derived network to 10 random networks that had the same nodes and and the same number of edges, but had randomized connections between nodes. We found that the random networks had larger values for the least squares error in the estimation and a different structure and degree‐distribution than the YEASTRACT‐derived network, suggesting that the YEASTRACT‐derived network modeled the transcriptional dynamics better. To improve the performance of the model and account for missing regulators, we are now evaluating a family of YEASTRACT‐derived networks by paring down an initial network of 35 nodes systematically down to 15 nodes by removing the next least significant transcription factor one by one from the network. From this analysis we expect to gain insight into the gene regulatory network that controls the cold shock response in yeast. Our working code is available on the GRNmap page (http://kdahlquist.github.io/GRNmap/), and visualization of the network is available on GRNsight (http://dondi.github.io/GRNsight/).Support or Funding InformationThis work was partially supported by NSF award 0921038 (B.G.F., K.D.D.), a Kadner‐Pitts Research Grant (K.D.D., K.G.J., N.E.W.), and a Loyola Marymount University Honors Summer Research Fellowship (K.G.J., N.E.W.).

Mathematical modeling shows that Gln3 and Zap1 affect the dynamics of the gene regulatory network controlling the cold shock response in Saccharomyces cerevisiae

A gene regulatory network (GRN) consists of a set of transcription factors that regulate the level of expression of genes encoding other transcription factors. The dynamics of a GRN show how gene expression in the network changes over time. While the transcriptional regulation of the response to the environmental stress of heat shock in budding yeast, Saccharomyces cerevisiae, is well‐understood, which transcription factors regulate the early response to cold shock is not fully understood. Thus, the focus of this study was to determine the GRN that controls the cold shock response in yeast and to model its dynamics. Microarray experiments were performed in the Dahlquist lab at various timepoints after cold shock (t=15, 30, and 60 minutes) to examine the gene expression patterns of both the wild type and mutant strains of yeast that had been deleted for the transcription factors Gln3 and Zap1. The microarray data were normalized using the limma package in R and a modified ANOVA was used to determine which genes had a log2 fold change significantly different than zero at any of the timepoints studied. The genes that met the significance criterion of an adjusted Benjamini and Hochberg p < 0.05 were submitted to the YEASTRACT database to determine which transcription factors potentially regulated those genes. Two GRNs, one derived from the Gln3 deletion strain data and one from the Zap1 deletion strain data were created. The GRNs and the corresponding microarray data were then input into GRNmap, a MATLAB software package that uses ordinary differential equations to model the dynamics of medium‐scale GRNs. The program estimated the production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on the microarray data, and then performed a forward simulation of the dynamics of the network. The results of the modeling for both networks were similar. The best fit of model to data was for genes directly connected to the deleted transcription factor (either Gln3 or Zap1). However, the deletion of either transcription factor from their respective networks had a large effect on the dynamics of several genes in the network that were not accounted for by the network connections. This suggests that the network structure did not fully model the actual cellular conditions of cold shock. This may be because the regulatory networks in the YEASTRACT database are based on measurements taken during other types of growth conditions, not cold shock. Our future directions include expanding the number of GRNs that we model to determine which transcription factors are missing from the current model. Our working code is available on the GRNmap page (http://kdahlquist.github.io/GRNmap/), and visualization of the network is available on GRNsight (http://dondi.github.io/GRNsight/).Support or Funding InformationThis work was partially supported by NSF award 0921038 (B.G.F., K.D.D.), a Kadner‐Pitts Research Grant (K.D.D.) and the Loyola Marymount University Rains Research Assistant Program (T.A.M.).

TRX-LOGOS - a graphical tool to demonstrate DNA information content dependent upon backbone dynamics in addition to base sequence.

BackgroundIt is now widely-accepted that DNA sequences defining DNA-protein interactions functionally depend upon local biophysical features of DNA backbone that are important in defining sites of binding interaction in the genome (e.g. DNA shape, charge and intrinsic dynamics). However, these physical features of DNA polymer are not directly apparent when analyzing and viewing Shannon information content calculated at single nucleobases in a traditional sequence logo plot. Thus, sequence logos plots are severely limited in that they convey no explicit information regarding the structural dynamics of DNA backbone, a feature often critical to binding specificity.Software and implementationWe present TRX-LOGOS, an R software package and Perl wrapper code that interfaces the JASPAR database for computational regulatory genomics. TRX-LOGOS extends the traditional sequence logo plot to include Shannon information content calculated with regard to the dinucleotide-based BI-BII conformation shifts in phosphate linkages on the DNA backbone, thereby adding a visual measure of intrinsic DNA flexibility that can be critical for many DNA-protein interactions. TRX-LOGOS is available as an R graphics module offered at both SourceForge and as a download supplement at this journal.ResultsTo demonstrate the general utility of TRX logo plots, we first calculated the information content for 416 Saccharomyces cerevisiae transcription factor binding sites functionally confirmed in the Yeastract database and matched to previously published yeast genomic alignments. We discovered that flanking regions contain significantly elevated information content at phosphate linkages than can be observed at nucleobases. We also examined broader transcription factor classifications defined by the JASPAR database, and discovered that many general signatures of transcription factor binding are locally more information rich at the level of DNA backbone dynamics than nucleobase sequence. We used TRX-logos in combination with MEGA 6.0 software for molecular evolutionary genetics analysis to visually compare the human Forkhead box/FOX protein evolution to its binding site evolution. We also compared the DNA binding signatures of human TP53 tumor suppressor determined by two different laboratory methods (SELEX and ChIP-seq). Further analysis of the entire yeast genome, center aligned at the start codon, also revealed a distinct sequence-independent 3 bp periodic pattern in information content, present only in coding region, and perhaps indicative of the non-random organization of the genetic code.ConclusionTRX-LOGOS is useful in any situation in which important information content in DNA can be better visualized at the positions of phosphate linkages (i.e. dinucleotides) where the dynamic properties of the DNA backbone functions to facilitate DNA-protein interaction.Electronic supplementary materialThe online version of this article (doi:10.1186/s13029-015-0040-8) contains supplementary material, which is available to authorized users.

Saccharomycesgenome database provides new regulation data

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is the community resource for genomic, gene and protein information about the budding yeast Saccharomyces cerevisiae, containing a variety of functional information about each yeast gene and gene product. We have recently added regulatory information to SGD and present it on a new tabbed section of the Locus Summary entitled ‘Regulation’. We are compiling transcriptional regulator–target gene relationships, which are curated from the literature at SGD or imported, with permission, from the YEASTRACT database. For nearly every S. cerevisiae gene, the Regulation page displays a table of annotations showing the regulators of that gene, and a graphical visualization of its regulatory network. For genes whose products act as transcription factors, the Regulation page also shows a table of their target genes, accompanied by a Gene Ontology enrichment analysis of the biological processes in which those genes participate. We additionally synthesize information from the literature for each transcription factor in a free-text Regulation Summary, and provide other information relevant to its regulatory function, such as DNA binding site motifs and protein domains. All of the regulation data are available for querying, analysis and download via YeastMine, the InterMine-based data warehouse system in use at SGD.

The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation inSaccharomyces cerevisiae

The YEASTRACT (http://www.yeastract.com) information system is a tool for the analysis and prediction of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2013, this database contains over 200 000 regulatory associations between transcription factors (TFs) and target genes, including 326 DNA binding sites for 113 TFs. All regulatory associations stored in YEASTRACT were revisited and new information was added on the experimental conditions in which those associations take place and on whether the TF is acting on its target genes as activator or repressor. Based on this information, new queries were developed allowing the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. This release further offers tools to rank the TFs controlling a gene or genome-wide response by their relative importance, based on (i) the percentage of target genes in the data set; (ii) the enrichment of the TF regulon in the data set when compared with the genome; or (iii) the score computed using the TFRank system, which selects and prioritizes the relevant TFs by walking through the yeast regulatory network. We expect that with the new data and services made available, the system will continue to be instrumental for yeast biologists and systems biology researchers.

Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription factor knockout microarray and the ChIP-chip data

BackgroundTranscription factor knockout microarrays (TFKMs) provide useful information about gene regulation. By using statistical methods for detecting differentially expressed genes between the gene expression microarray data of the mutant and wild type strains, the TF knockout targets of the knocked-out TF can be identified. However, the identified TF knockout targets may contain a certain amount of false positives due to the experimental noises inherent in the high-throughput microarray technology. Even if the identified TF knockout targets are true, the molecular mechanisms of how a TF regulates its TF knockout targets remain unknown by this kind of statistical approaches.ResultsTo solve these two problems, we developed a method to filter out the false positives in the original TF knockout targets (identified by statistical approaches) so that the biologically interpretable TF knockout targets can be extracted. Our method can further generate experimentally testable hypotheses of the molecular mechanisms of how a TF regulates its biologically interpretable TF knockout targets. The details of our method are as follows. First, a TF binding network was constructed using the ChIP-chip data deposited in the YEASTRACT database. Then for each original TF knockout target, it is said to be biologically interpretable if a path (in the TF binding network) from the knocked-out TF to this target could be identified by our path search algorithm. The identified path explains how the TF may regulate this target either directly by binding to its promoter or indirectly through intermediate TFs. After checking all the original TF knockout targets, the biologically interpretable ones could be extracted and the false positives could be filtered out. We validated the biological significance of our refined (i.e., biologically interpretable) TF knockout targets by assessing their functional enrichment, expression coherence, and the prevalence of protein-protein interactions. Our refined TF knockout targets outperform the original TF knockout targets across all measures.ConclusionsBy jointly analyzing the TFKM and ChIP-chip data, our method can extract the biologically interpretable TF knockout targets by identifying paths (in the TF binding network) from the knocked-out TF to these targets. The identified paths form experimentally testable hypotheses regarding the molecular mechanisms of how a TF may regulate its knockout targets. About seven hundred hypotheses generated by our methods have been experimentally validated in the literature. Our work demonstrates that integrating different data sources is a powerful approach to study complex biological systems.

532 NG2 expression identifies a tumour competent population in glioblastoma with distinct molecular signature

In order to identify genes involved in complex diseases, it is crucial to study the genetic interactions at the systems biology level. By utilizing modern high throughput microarray technology, it has become feasible to obtain gene expressions data and turn it into knowledge that explains the regulatory behavior of genes. In this study, an unsupervised nonlinear model was proposed to infer gene regulatory networks on a genome-wide scale. The proposed model consists of two components, a robust correlation estimator and a nonlinear recurrent model. The robust correlation estimator was used to initialize the parameters of the nonlinear recurrent curve-fitting model. Then the initialized model was used to fit the microarray data. The model was used to simulate the underlying nonlinear regulatory mechanisms in biological organisms. The proposed algorithm was applied to infer the regulatory mechanisms of the general network in Saccharomyces cerevisiae and the pulmonary disease pathways in Homo sapiens. The proposed algorithm requires no prior biological knowledge to predict linkages between genes. The prediction results were checked against true positive links obtained from the YEASTRACT database, the TRANSFAC database, and the KEGG database. By checking the results with known interactions, we showed that the proposed algorithm could determine some meaningful pathways, many of which are supported by the existing literature.

A robust correlation estimator and nonlinear recurrent model to infer genetic interactions in Saccharomyces cerevisiae and pathways of pulmonary disease in Homo sapiens

In order to identify genes involved in complex diseases, it is crucial to study the genetic interactions at the systems biology level. By utilizing modern high throughput microarray technology, it has become feasible to obtain gene expressions data and turn it into knowledge that explains the regulatory behavior of genes. In this study, an unsupervised nonlinear model was proposed to infer gene regulatory networks on a genome-wide scale. The proposed model consists of two components, a robust correlation estimator and a nonlinear recurrent model. The robust correlation estimator was used to initialize the parameters of the nonlinear recurrent curve-fitting model. Then the initialized model was used to fit the microarray data. The model was used to simulate the underlying nonlinear regulatory mechanisms in biological organisms. The proposed algorithm was applied to infer the regulatory mechanisms of the general network in Saccharomyces cerevisiae and the pulmonary disease pathways in Homo sapiens. The proposed algorithm requires no prior biological knowledge to predict linkages between genes. The prediction results were checked against true positive links obtained from the YEASTRACT database, the TRANSFAC database, and the KEGG database. By checking the results with known interactions, we showed that the proposed algorithm could determine some meaningful pathways, many of which are supported by the existing literature.

Supervised inference of gene-regulatory networks

BackgroundInference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks.ResultsThe method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed.ConclusionResults show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.

Early transcriptional response of Saccharomyces cerevisiae to stress imposed by the herbicide 2,4-dichlorophenoxyacetic acid

The global gene transcription pattern of the eukaryotic experimental model Saccharomyces cerevisiae in response to sudden aggression with the widely used herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) was analysed. Under acute stress, 14% of the yeast transcripts suffered a greater than twofold change. The yeastract database was used to predict the transcription factors mediating the response registered in this microarray analysis. Most of the up-regulated genes in response to 2,4-D are known targets of Msn2p, Msn4p, Yap1p, Pdr1p, Pdr3p, Stp1p, Stp2p and Rpn4p. The major regulator of ribosomal protein genes, Sfp1p, is known to control 60% of the down-regulated genes, in particular many involved in the transcriptional and translational machinery and in cell division. The yeast response to the herbicide includes the increased expression of genes involved in the oxidative stress response, the recovery or degradation of damaged proteins, cell wall remodelling and multiple drug resistance. Although the protective role of TPO1 and PDR5 genes was confirmed, the majority of the responsive genes encoding multidrug resistance do not confer resistance to 2,4-D. The increased expression of genes involved in alternative carbon and nitrogen source metabolism, fatty acid beta-oxidation and autophagy was also registered, suggesting that acute herbicide stress leads to nutrient limitation.

The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae

We present the YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT; ) database, a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. This database is a repository of 12 346 regulatory associations between transcription factors and target genes, based on experimental evidence which was spread throughout 861 bibliographic references. It also includes 257 specific DNA-binding sites for more than a hundred characterized transcription factors. Further information about each yeast gene included in the database was obtained from Saccharomyces Genome Database (SGD), Regulatory Sequences Analysis Tools and Gene Ontology (GO) Consortium. Computational tools are also provided to facilitate the exploitation of the gathered data when solving a number of biological questions as exemplified in the Tutorial also available on the system. YEASTRACT allows the identification of documented or potential transcription regulators of a given gene and of documented or potential regulons for each transcription factor. It also renders possible the comparison between DNA motifs, such as those found to be over-represented in the promoter regions of co-regulated genes, and the transcription factor-binding sites described in the literature. The system also provides an useful mechanism for grouping a list of genes (for instance a set of genes with similar expression profiles as revealed by microarray analysis) based on their regulatory associations with known transcription factors.