ScerTF: a comprehensive database of benchmarked position weight matrices for Saccharomyces species

Spivak, Aaron; Stormo, Gary D.

doi:10.1093/nar/gkr1180

Cited by 81 publications

(96 citation statements)

References 39 publications

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“…Similar results were obtained using PWMs based on in vivo data and manual curation (see Supplemental Fig. S1; Spivak and Stormo 2012).…”

Section: Top Netprophet Predictions Identify Direct Binding Potentialsupporting

confidence: 78%

“…At the high, medium, and low stringencies, the random baselines (percentage of random TF-gene pairs supported by binding potential) are 6.4%, 22.1%, and 36.8%, respectively. This process was repeated using the curated PWMs from the ScerTF database (Spivak and Stormo 2012), for which the random baselines at the high, medium, and low stringencies are 5.7%, 20.4%, and 35.9%, respectively.…”

Section: Chip Data Curationmentioning

confidence: 99%

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Mapping functional transcription factor networks from gene expression data

et al. 2013

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A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein–DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1.

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“…Similar results were obtained using PWMs based on in vivo data and manual curation (see Supplemental Fig. S1; Spivak and Stormo 2012).…”

Section: Top Netprophet Predictions Identify Direct Binding Potentialsupporting

confidence: 78%

Section: Chip Data Curationmentioning

confidence: 99%

Mapping functional transcription factor networks from gene expression data

et al. 2013

View full text Add to dashboard Cite

show abstract

“…A NetProphet-based binding motif was also inferred by inputting NetProphet's target confidence scores for each TF to FIRE (Elemento et al 2007). The motifs of orthologous TFs from S. cerevisiae were obtained from ScerTF (Spivak and Stormo 2012). If the motif for the best S. cerevisiae match was unknown, the next best match was used.…”

Section: Network Validationmentioning

confidence: 99%

Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation

et al. 2015

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Key steps in understanding a biological process include identifying genes that are involved and determining how they are regulated. We developed a novel method for identifying transcription factors (TFs) involved in a specific process and used it to map regulation of the key virulence factor of a deadly fungus-its capsule. The map, built from expression profiles of 41 TF mutants, includes 20 TFs not previously known to regulate virulence attributes. It also reveals a hierarchy comprising executive, midlevel, and "foreman" TFs. When grouped by temporal expression pattern, these TFs explain much of the transcriptional dynamics of capsule induction. Phenotypic analysis of TF deletion mutants revealed complex relationships among virulence factors and virulence in mice. These resources and analyses provide the first integrated, systems-level view of capsule regulation and biosynthesis. Our methods dramatically improve the efficiency with which transcriptional networks can be analyzed, making genomic approaches accessible to laboratories focused on specific physiological processes.[Supplemental material is available for this article.]In this paper we present an efficient means of comprehensively mapping the network of transcription factors (TFs) that regulate a particular physiological process. Our approach cycles through deletion of TFs, expression profiling of TF mutants, model construction, and model-directed selection of TFs for the next round of deletion. This predictive genetics approach identifies TFs that affect the process of interest, providing a valuable complement to undirected mutagenesis and screening. Simultaneously, it builds a network model that explains how the TFs affect the process, yielding novel insights into the biological system under study.Mapping the network that regulates a specific process requires knowing which TFs affect that process. One way to identify such TFs is to screen comprehensive mutant libraries, but generating such libraries is not always feasible. Furthermore, genome-scale screening assays must be fast and scalable; such assays may not exist for the process of interest or may be less sensitive than other, more laborious assays. An alternative approach is to map the targets of all TFs encoded in a genome by using methods such as chromatin-immunoprecipitation (ChIP) or large-scale TF deletion and expression analysis. However, undirected, genome-wide approaches are costly and inefficient for probing a specific biological process in detail. We report a model-guided approach that addresses all of these problems by focusing experimental effort on the TFs most likely to be involved in the process of interest. Furthermore, our approach generates a network that provides mechanistic explanations for the phenotypes of TF deletion mutants.Our approach alternates network building by using an algorithm we call NetProphet with identifying relevant TFs by using an algorithm we call PhenoProphet. NetProphet is a validated method for mapping direct, functional regulation that significantly out...

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“…We also introduce a systematic, genome-scale evaluation procedure for general transcriptome engineering algorithms. Saccharomyces cerevisiae is ideal for evaluating transcriptome engineering because of the ease of experimental testing in yeast and the wealth of data available for TF network mapping and algorithmic validation (17)(18)(19)(20).…”

Section: Significancementioning

confidence: 99%

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

Michael

Maier

Brown

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genomewide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.gene-regulatory networks | regulatory systems biology | transcriptome | engineering | Saccharomyces cerevisiae T he central premise of regulatory systems biology is that a systematic map of a cell's regulatory machinery will enable us to understand, predict, and rationally manipulate the cell's state or behavior. Manipulation of cellular state has many promising applications, including stem cell biology and regenerative medicine, biofuel production, and gene therapy. Progress toward cellular state control has been driven by both the systems biology and the synthetic biology research communities. Systems biology has produced whole-genome regulatory network maps (1), but relatively little research has focused on using these maps for predicting and manipulating cellular behavior (2). Regulatory synthetic biology has focused on creating molecular circuits that can be placed into a cell to control the transcription of a small number of transgenes, but genome-scale engineering of the cell's native regulatory apparatus is still rare, with most systems restricted to a limited set of controlled targets (3). Here, we demonstrate that transcription factor (TF) network mapping, gene expression profiling, and computational modeling can be integrated to rationally engineer transcriptional state. We call this activity, which bridges the gap between systems biology and synthetic biology, "transcriptome engineering

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ScerTF: a comprehensive database of benchmarked position weight matrices for Saccharomyces species

Cited by 81 publications

References 39 publications

Mapping functional transcription factor networks from gene expression data

Mapping functional transcription factor networks from gene expression data

Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

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