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Comprehensive maps of transcription factor (TF) binding sites in mouse (RNA-Seq)

ABSTRACT: Comprehensive maps of transcription factor (TF) binding sites in mouse (RNA-Seq)

PROVIDER: PRJNA692432 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR13443433.fastq.gz	Fastqsanger.gz
	SRR13443434.fastq.gz	Fastqsanger.gz
	SRR13443435.fastq.gz	Fastqsanger.gz
	SRR13443436.fastq.gz	Fastqsanger.gz
	SRR13443437.fastq.gz	Fastqsanger.gz

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Comprehensive maps of transcription factor (TF) binding sites in mouse (ATAC-Seq)

Project description:Comprehensive maps of transcription factor (TF) binding sites in mouse (ATAC-Seq)

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Comprehensive maps of transcription factor (TF) binding sites in mouse (ATAC-Seq)

Project description:Mammalian genome encodes approximately 1,700 transcription factors (TFs), 1,300 out of which have sequence specific binding motifs. Transcription in mammalian cells is regulated by the recruitment of TFs to specific cis-regulatory elements. In spite of consistent efforts on the function of individual TF, the question still remains how TFs bind to DNA and form enhancer. Here, we try to solve this problem by investigating the relationship between TF binding pattern and chromatin accessibility (ATAC-Seq). We first systematically acquired ATAC-Seq dataset as well as matched RNA-Seq dataset from different mouse primary tissues. A comprehensive TF binding map was built for each tissue/cell type by genomic approaches.

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Comprehensive maps of transcription factor (TF) binding sites in mouse (RNA-Seq)

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Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize

Project description:Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here, we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions. We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over two hundred thousand (epi)genetic variants (termed binding-QTL) linked to cis-element occupancy. Three lines of evidence support the functional significance of binding-QTL: i) coincidence with causative loci that regulate traits, including VGT1, ZmTRE1, and the MITE transposon near ZmNAC111 under drought; ii) bQTL allelic bias is shared between inbred parents and matches ChIP-seq results, iii) partitioning genetic variation across genomic regions demonstrates that binding-QTL capture the majority of heritable trait variation across ~72% of 143 phenotypes. Our study provides an auspicious approach to make functional cis-variation accessible at scale for genetic studies and targeted engineering of complex traits.

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TF-COMB - Discovering grammar of transcription factor binding sites.

Project description:Cooperativity between transcription factors is important to regulate target gene expression. In particular, the binding grammar of TFs in relation to each other, as well as in the context of other genomic elements, is crucial for TF functionality. However, tools to easily uncover co-occurrence between DNA-binding proteins, and investigate the regulatory modules of TFs, are limited. Here we present TF-COMB (Transcription Factor Co-Occurrence using Market Basket analysis) - a tool to investigate co-occurring TFs and binding grammar within regulatory regions. We found that TF-COMB can accurately identify known co-occurring TFs from ChIP-seq data, as well as uncover preferential localization to other genomic elements. With the use of ATAC-seq footprinting and TF motif locations, we found that TFs exhibit both preferred orientation and distance in relation to each other, and that these are biologically significant. Finally, we extended the analysis to not only investigate individual TF pairs, but also TF pairs in the context of networks, which enabled the investigation of TF complexes and TF hubs. In conclusion, TF-COMB is a flexible tool to investigate various aspects of TF binding grammar.

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Project description:Comprehensive discovery of endogenous Argonaute binding sites in C. elegans

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Project description:Understanding gene regulatory networks (GRNs) is essential for improving maize yield and quality through molecular breeding approaches. The lack of comprehensive transcription factor (TF)-DNA interaction data has hindered accurate GRN predictions, limiting our insight into the regulatory mechanisms. In this study, we performed large-scale profiling of maize TF binding sites. We obtained and collected reliable binding profiles for 513 TFs, identified 394,136 binding sites, and constructed an accuracy-enhanced maize GRN (mGRN+) by integrating chromatin accessibility and gene expression data. The mGRN+ comprises 397,699 regulatory relationships. We further divided the mGRN+ into multiple modules across six major tissues. Using machine-learning algorithms, we optimized the mGRN+ to improve the prediction accuracy of gene functions and key regulators. Through independent genetic validation experiments, we further confirmed the reliability of these predictions. This work provides the largest collection of experimental TF binding sites in maize and highly optimized regulatory networks, which serve as valuable resources for studying maize gene function and crop improvement.

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Transcription Factor Binding Sites clustered Regions of 133 Cell Lines

Project description:Purpose: 224 GSM Samples form GSE32970 and GSE29692 was reanalyzed to find the TFBS-clustered regions of 133 cell lines. TFBS-clustered regions were divided into ten classes belong to the TF complexity. Methods: 1. We assigned the binding sites of 542 TFs in 133 cell lines as our record GSE53962 . 2. We performed a Gaussian kernel density estimation across the genome with a bandwidth of 300 bp, using the centers of each of the TF binding peaks as points. Then, we scanned this density for peaks, and denoted each peak a TF region.To determine the complexity of the TF region, we summed the Gaussian kernalized distance from the peak to each TF that contributed at least 0.1 to its strength. The TF region around eat peak was derived by finding the maximum distance (in bp) from the peak to a contributing TF, and then adding 150 bp (one half of the bandwidth). Each TF region is centered on the peak, and have a TF complexity value. 3. According to TFBS complexity, we divided these TFBS-clustered regions into ten classes: from TC0 to TC9 with increasing TFBS complexity. Result: Using the binding sites of 542 TFs in 133 cell lines, we assigned a TF complexity score to each TF region corresponding to the number of distinct TFs bound, resulting in ten classes TFBS-clustered regions of 133 cell lines.

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