RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections

ABSTRACTTranscription Factor (TF) databases contain multitudes of motifs from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq peaks) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant collections of motifs. A feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools and highlights biologically relevant variations of similar motifs. By clustering 24 entire databases (>7,500 motifs), we show that matrix-clustering correctly groups motifs belonging to the same TF families, and can drastically reduce motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines.

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Genome-wide association between transcription factor expression and chromatin accessibility reveals chromatin state regulators

10.1101/043414 ◽

2016 ◽

Author(s):

David Felix Lamparter ◽

Daniel Marbach ◽

Rico Rueedi ◽

Sven Bergmann ◽

Zoltan Kutalik

Keyword(s):

Transcription Factor ◽

Transcription Factors ◽

Chromatin Accessibility ◽

Binding Motif ◽

Open Chromatin ◽

Data Sets ◽

Transcription Factor Binding Motif ◽

Data Set ◽

Data Driven Approach ◽

Transcription Factor Expression

To better understand genome regulation, it is important to uncover the role of transcription factors in the process of chromatin structure establishment and maintenance. Here we present a data-driven approach to systematically characterize transcription factors that are relevant for this process. Our method uses a linear mixed modeling approach to combine data sets of transcription factor binding motif enrichments in open chromatin and gene expression across the same set of cell lines. Applying this approach to the ENCODE data set we confirm already known and imply numerous novel transcription factors in playing a role in the establishment or maintenance of open chromatin.

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Transcription Factor Binding Motif (TFBM)

Dictionary of Bioinformatics and Computational Biology ◽

10.1002/9780471650126.dob1114 ◽

2004 ◽

Author(s):

Jacques van Helden

Keyword(s):

Transcription Factor ◽

Transcription Factor Binding ◽

Binding Motif ◽

Transcription Factor Binding Motif ◽

Factor Binding

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Activity of Cordycepin From Cordyceps sinensis Against Drug-Resistant Tumor Cells as Determined by Gene Expression and Drug Sensitivity Profiling

Natural Product Communications ◽

10.1177/1934578x21993350 ◽

2021 ◽

Vol 16 (2) ◽

pp. 1934578X2199335

Author(s):

Nadire Özenver ◽

Joelle C. Boulos ◽

Thomas Efferth

Keyword(s):

Transcription Factor ◽

Drug Sensitivity ◽

Experimental Studies ◽

Cordyceps Sinensis ◽

Parasitic Fungus ◽

Cross Resistance ◽

Binding Motif ◽

Tumor Type ◽

Transcription Factor Binding Motif

Cordycepin is one of the substantial components of the parasitic fungus Cordyceps sinensis as well as other Cordyceps species. It exerts various effects such as antimetastatic, antiinflammatory, antioxidant, and neuroprotective activities. Assorted studies revealed in vitro and in vivo anticancer influence of cordycepin and put forward its potential for cancer therapy. However, the role of multidrug resistance-associated mechanisms for the antitumor effect of cordycepin has not been investigated in great detail thus far. Therefore, we searched cordycepin’s cytotoxicity with regard to well-known anticancer drug resistance mechanisms, including ABCB1, ABCB5, ABCC1, ABCG2, EGFR, and TP53, and identified putative molecular determinants related to the cellular responsiveness of cordycepin. Bioinformatic analyses of NCI microarray data and gene promoter transcription factor binding motif analyses were performed to specify the mechanisms of cordycepin towards cancer cells. COMPARE and hierarchical analyses led to the detection of the genes involved in cordycepin’s cytotoxicity and sensitivity and resistance of cell lines towards cordycepin. Tumor-type dependent response and cross-resistance profiles were further unravelled. We found transcription factors potentially involved in the common transcriptional regulation of the genes identified by COMPARE analyses. Cordycepin bypassed resistance mediated by the expression of ATP-binding cassete (ABC) transporters (P-gp, ABCB5, ABCC1 and BCRP) and mutant epidermal growth factor receptor (EGFR). The drug sensitivity profiles of several DNA Topo I and II inhibitors were significantly correlated with those of cordycepin’s activity. Among eight different tumor types, prostate cancer was the most sensitive, whereas renal carcinoma was the most resistant to cordycepin. NF-κB was discovered as a common transcription factor. The potential of cordycepin is set forth as a potential new drug lead by bioinformatic evaluations. Further experimental studies are warranted for better understanding of cordycepin’s activity against cancer.

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Rational gRNA Design Based on Transcription Factor Binding Data

Synthetic Biology ◽

10.1093/synbio/ysab014 ◽

2021 ◽

Author(s):

David Bergenholm ◽

Yasaman Dabirian ◽

Raphael Ferreira ◽

Verena Siewers ◽

Florian David ◽

...

Keyword(s):

Transcription Factor ◽

Genome Engineering ◽

Transcription Factor Binding ◽

Central Carbon Metabolism ◽

Binding Motif ◽

Transcription Factor Binding Motif ◽

Factor Binding ◽

Transcription Factor Motif ◽

Binding Data ◽

Genes Encoding

Abstract The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas9 system has become a standard tool in many genome engineering endeavors. The endonuclease-deficient version of Cas9 (dCas9) is also a powerful programmable tool for gene regulation. In this study, we made use of Saccharomyces cerevisiae transcription factor binding data to obtain a better understanding of the interplay between transcription factor binding and binding of dCas9 fused to an activator domain, VPR. More specifically, we targeted dCas9-VPR towards binding sites of Gcr1-Gcr2 and Tye7 present in several promoters of genes encoding enzymes engaged in the central carbon metabolism. From our data, we observed an upregulation of gene expression when dCas9-VPR was targeted next to a transcription factor binding motif, whereas downregulation or no change was observed when dCas9 was bound on a transcription factor motif. This suggests a steric competition between dCas9 and the specific transcription factor. Integrating transcription factor binding data, therefore, proved to be useful for designing gRNAs for CRISPRi/a applications.

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Faculty Opinions recommendation of Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.737940069.793575948 ◽

2020 ◽

Author(s):

Deyou Zheng

Keyword(s):

Transcription Factor ◽

Transcription Factor Binding ◽

Binding Motif ◽

Transcription Factor Binding Motif ◽

Factor Binding

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A PARALLEL SCHEME FOR COMPARING TRANSCRIPTION FACTOR BINDING SITES MATRICES

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720010004689 ◽

2010 ◽

Vol 08 (03) ◽

pp. 485-502 ◽

Cited By ~ 3

Author(s):

SOLENNE CARAT ◽

RÉMI HOULGATTE ◽

JÉRÉMIE BOURDON

Keyword(s):

Transcription Factor ◽

Regulatory Networks ◽

Motif Discovery ◽

Specific Pattern ◽

Data Sets ◽

Original Graph ◽

Motif Model ◽

Parallel Scheme ◽

Eukaryotic Organism ◽

Graph Motif

Gene regulation implies many mechanisms. Their identification is a crucial task to construct regulatory networks, and is necessary to understand the pathology in many cases. This requires the identification of transcription factors that play a role in regulation. Numerous motif discovery tools are now available. Combining efficiently their results appears useful for comparing and clustering these motifs in order to reduce redundancies and to identify the corresponding transcription factor. We develop a method that produces, compares and clusters a set of motifs and identifies some close motifs in databases like JASPAR and the public version of Transfac. Unlike previous comparison methods, where each matrix column is compared independently, we have developed a global method to compare motifs that also helps to reduce the number of false positives. We also propose an original graph motif model that generalizes the classical position specific pattern matrices. Finally, we present an application of our method to study ChIP-chip data sets in the context of an eukaryotic organism.

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