Integrated analysis of motif activity and gene expression changes of transcription factors
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA412987 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6128420.fastq.gz | Fastqsanger.gz | |||
| SRR6128430.fastq.gz | Fastqsanger.gz | |||
| SRR6128440.fastq.gz | Fastqsanger.gz | |||
| SRR6128450.fastq.gz | Fastqsanger.gz | |||
| SRR6128460.fastq.gz | Fastqsanger.gz |
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Project description:The ability to predict transcription factors based on sequence information in regulatory elements is a key step in systems-level investigation of transcriptional regulation. Here, we have developed a novel tool, IMAGE, for precise prediction of transcription factors based on transcriptome profiling and genome-wide maps of enhancer activity. High precision is obtained by combining a near-complete database of position weight matrices (PWMs), generated by compiling public databases and systematic prediction of PWMs for uncharacterized transcription factors, with a state-of-the-art method for PWM scoring and a novel machine learning strategy, based on both enhancers and promoters, to predict the contribution of motifs to transcriptional activity. We applied IMAGE to published data obtained during 3T3-L1 adipocyte differentiation and showed that IMAGE predicts causal transcriptional regulators of this process with higher confidence than other methods. Furthermore, we generated genome-wide maps of enhancer activity and transcripts during human mesenchymal stem cell commitment and adipocyte differentiation and used IMAGE to identify positive and negative transcriptional regulators of this process. Collectively, our results demonstrate that IMAGE is a powerful and precise method for prediction of regulators of gene expression.
2017-12-04 | GSE104537 | GEO
Project description:The ability to predict transcription factors based on sequence information in regulatory elements is a key step in systems-level investigation of transcriptional regulation. Here, we have developed a novel tool, IMAGE, for precise prediction of causal transcription factors based on transcriptome profiling and genome-wide maps of enhancer activity. High precision is obtained by combining a near-complete database of position weight matrices (PWMs), generated by compiling public databases and systematic prediction of PWMs for uncharacterized transcription factors, with a state-of-the-art method for PWM scoring and a novel machine learning strategy, based on both enhancers and promoters, to predict the contribution of motifs to transcriptional activity. We applied IMAGE to published data obtained during 3T3-L1 adipocyte differentiation and showed that IMAGE predicts causal transcriptional regulators of this process with higher confidence than existing methods. Furthermore, we generated genome-wide maps of enhancer activity and transcripts during human mesenchymal stem cell commitment and adipocyte differentiation and used IMAGE to identify positive and negative transcriptional regulators of this process. Collectively, our results demonstrate that IMAGE is a powerful and precise method for prediction of regulators of gene expression.
| S-EPMC5793788 | biostudies-literature
Project description:The information about when and where each gene is to be expressed is mainly encoded in the DNA sequence of enhancers, sequence elements that comprise binding sites (motifs) for different transcription factors (TFs). Most of the research on enhancer sequences has been focused on TF motif presence, while the enhancer syntax, i.e. the flexibility of important motif positions and how the sequence context modulates the activity of TF motifs, remain poorly understood. Here, we explore the rules of enhancer syntax by a two-pronged approach in Drosophila melanogaster S2 cells: we (1) replace important motifs by an exhaustive set of all possible 65,536 eight-nucleotide-long random sequences and (2) paste eight important TF motif types into 763 motif positions within 496 enhancers. These complementary strategies reveal that enhancers display constrained sequence flexibility and the context-specific modulation of motif function. Important motifs can be functionally replaced by hundreds of sequences constituting several distinct motif types, but only a fraction of all possible sequences and motif types restore enhancer activity. Moreover, TF motifs contribute with different intrinsic strengths that are strongly modulated by the enhancer sequence context (the flanking sequence, presence and diversity of other motif types, and distance between motifs), such that not all motif types can work in all positions. Constrained sequence flexibility and the context-specific modulation of motif function are also hallmarks of human enhancers and TF motifs, as we demonstrate experimentally. Overall, these two general principles of enhancer sequences are important to understand and predict enhancer function during development, evolution and in disease.
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Project description:Demonstration of CUT&RUN motif footprint analysis using key blood progenitor transcription factors
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Project description:We introduce CUT&RUNTools (https://bitbucket.org/qzhudfci/cutruntools/) as a flexible, general pipeline for facilitating the identification of chromatin-associated protein binding and genomic footprinting analysis from antibody-targeted CUT&RUN primary cleavage data. CUT&RUNTools extracts endonuclease cut site information from sequences of short read fragments and produces single-locus binding estimates, aggregate motif footprints, and informative visualizations to support the high-resolution mapping capability of CUT&RUN. We illustrate the functionality of CUT&RUNTools through analysis of CUT&RUN data acquired for GATA1, a master regulator in erythroid lineage cells. Results were compared initially to published GATA1 ChIP-seq data for cells under the same conditions. We performed de novo analysis of CUT&RUN peaks to retrieve not only GATA1’s primary motif, but also the GATA1-TAL1 composite motif, and co-factor motifs GCCCCGCCTC, CMCDCCC, and RTGASTCA that correspond to SP1, KLF1, and NFE2 co-factors. Cofactor binding was verified by independent TAL1 and KLF1 CUT&RUN, and other ChIP-seq experiments. CUT&RUNTools also generated base-pair resolution motif footprint for sequence-specific binding factors, and located likely direct binding sites by quantifying log-odds of binding scores. Overall, CUT&RUNTools should enable biologists to realize advantages of cleavage data provided by CUT&RUN, and make high-quality footprinting analysis accessible to a broad audience.
2019-08-24 | GSE136251 | GEO
Project description:Sequence-specific transcription factors (TFs) regulate gene expression by binding to cognate motifs in promoters and enhancers. However, predicting genomic TF binding events and their quantitative contribution to expression remains a major challenge. In principle, the binding and enhancer activity of specific sites in vivo might depend on: (i) latent properties of the motif instance, (ii) cooperative interactions with other TFs that bind in the immediate vicinity, and (iii) the chromatin state of the sites in the genome. Here, we used massively parallel reporter assays (MPRA) involving 32,115 natural and synthetic enhancers, together with high-throughput in vivo assays, to systematically dissect the contributions of motif affinity, cooperative interactions, and chromatin accessibility to the binding and regulatory activity of genomic sequences that contain motifs for PPARγ, a TF that serves as a key regulator of adipogenesis. We show that PPARγ binding and enhancer activity are governed by distinct features. Genomic PPARγ binding to motif sites is largely governed by on larger-scale features, such as chromatin accessibility, whereas the degree to which a PPARγ motif site enhances transcriptional activity depends on the sequence immediately surround the motif. We detect and functionally validate a network of TFs comprised of multiple functional classes that collaborate with PPARγ to drive transcription. We extensively perturb this network, revealing functional cooperativity among classes of TFs that does not depend on precise positioning. Together, these results present a clear picture of how chromatin and TFs from distinct functional classes interact with PPARγ to determine binding and enhancer activity, and provide a paradigm for studying any TF.
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Project description:The information about when and where each gene is to be expressed is mainly encoded in the DNA sequence of enhancers, sequence elements that comprise binding sites (motifs) for different transcription factors (TFs). Most of the research on enhancer sequences has been focused on TF motif presence, while the enhancer syntax, i.e. the flexibility of important motif positions and how the sequence context modulates the activity of TF motifs, remain poorly understood. Here, we explore the rules of enhancer syntax by a two-pronged approach in Drosophila melanogaster S2 cells: we (1) replace important motifs by an exhaustive set of all possible 65,536 eight-nucleotide-long random sequences and (2) paste eight important TF motif types into 763 motif positions within 496 enhancers. These complementary strategies reveal that enhancers display constrained sequence flexibility and the context-specific modulation of motif function. Important motifs can be functionally replaced by hundreds of sequences constituting several distinct motif types, but only a fraction of all possible sequences and motif types restore enhancer activity. Moreover, TF motifs contribute with different intrinsic strengths that are strongly modulated by the enhancer sequence context (the flanking sequence, presence and diversity of other motif types, and distance between motifs), such that not all motif types can work in all positions. Constrained sequence flexibility and the context-specific modulation of motif function are also hallmarks of human enhancers and TF motifs, as we demonstrate experimentally. Overall, these two general principles of enhancer sequences are important to understand and predict enhancer function during development, evolution and in disease.
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Project description:An integrated CUT&RUN quality control workflow for histone modifications and transcription factors
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Project description:Transcription factors (TFs) do not function alone but work together with other TFs (called co-TFs) in a combinatorial fashion to precisely control the transcription of target genes. Mining co-TFs is thus important to understand the mechanism of transcriptional regulation. Although existing methods can identify co-TFs, their accuracy depends heavily on the chosen background model and other parameters such as the enrichment window size and the PWM score cut-off. In this study, we have developed a novel web-based co-motif scanning program called CENTDIST (http://compbio.ddns.comp.nus.edu.sg/~chipseq/centdist/). In comparison to current co-motif scanning programs, CENTDIST does not require the input of any user-specific parameters and background information. Instead, CENTDIST automatically determines the best set of parameters and ranks co-TF motifs based on their distribution around ChIP-seq peaks. We tested CENTDIST on 14 ChIP-seq datasets and found CENTDIST is more accurate than existing methods. In particular, we applied CENTDIST on an Androgen Receptor (AR) ChIP-seq dataset from a prostate cancer cell line and correctly predicted all known co-TFs (8 TFs) of AR in the top 20 hits as well as discovering AP4 as a novel co-TF of AR (which was missed by existing methods). Taken together, CENTDIST, which exploits the imbalanced nature of co-TF binding, is a user-friendly, parameter-less, and powerful predictive web-based program for understanding the mechanism of transcriptional co-regulation. Genome-wide binding analyses of AP4 in LNCaP with DHT (5alpha-dihydrotestosterone) stimulation using ChIP-Seq.
2012-04-25 | E-GEOD-28857 | biostudies-arrayexpress