Wellington-bootstrap: differential DNase-seq footprinting identifies cell-type determining transcription factors

Piper, Jason; Assi, Salam A.; Cauchy, Pierre; Ladroue, Christophe; Cockerill, Peter N.; Bonifer, Constanze; Ott, Sascha

doi:10.1186/s12864-015-2081-4

Cited by 52 publications

(51 citation statements)

References 32 publications

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“…Altogether, 2,731,616 TF footprints were identified post-filtering with a median 3,693 binding sites per unique TF motif. Step ( B ): Analysis of dynamic TF binding was performed using the Wellington-bootstrap algorithm for differential footprinting (Piper et al, 2015) against all post-filtering TF footprints identified by PIQ. First, differential footprinting was applied using the pyDNase wellington-bootstrap.py script with the command-line option for ATAC-seq input “-A”.…”

Section: Star Methodsmentioning

confidence: 99%

Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development

et al. 2017

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SUMMARY The three-dimensional arrangement of the human genome comprises a complex network of structural and regulatory chromatin loops important for coordinating changes in transcription during human development. To better understand the mechanisms underlying context-specific 3D chromatin structure and transcription during cellular differentiation, we generated comprehensive in situ Hi-C maps of DNA loops in human monocytes and differentiated macrophages. We demonstrate that dynamic looping events are regulatory rather than structural in nature and uncover widespread coordination of dynamic enhancer activity at preformed and acquired DNA loops. Enhancer-bound loop formation and enhancer-activation of preformed loops together form multi-loop activation hubs at key macrophage genes. Activation hubs connect 3.4 enhancers per promoter and exhibit a strong enrichment for Activator Protein 1 (AP-1) binding events, suggesting multi-loop activation hubs involving cell-type specific transcription factors may represent an important class of regulatory chromatin structures for the spatiotemporal control of transcription.

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Section: Star Methodsmentioning

confidence: 99%

Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development

et al. 2017

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“…S4A, S4B). To this end, we analyzed the mouse liver, kidney, and heart DNase-Seq datasets using pyDNase (25) and identified the genome-wide location of TF footprints.…”

Section: Tissue-specific Bmal1 Peaksmentioning

confidence: 99%

“…Footprint detection. Detection of footprints was performed using the python script wellington_footprints.py from the pyDNase suite (25,48). All parameters were set to default, and a p-value of -20 was used along with an FDR of 0.01.…”

Section: Sequencing Datasets Analysismentioning

confidence: 99%

Tissue-specific BMAL1 cistromes reveal that enhancer-enhancer interactions regulate rhythmic transcription

Beytebiere

Trott

Greenwell

et al. 2018

Preprint

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AbtsractThe mammalian circadian clock relies on the transcription factor CLOCK:BMAL1 to coordinate the rhythmic expression of thousands of genes. Consistent with the various biological functions under clock control, rhythmic gene expression is tissue-specific despite an identical clockwork mechanism in every cell. Here we show that BMAL1 DNA binding is largely tissue-specific, due to differences in chromatin accessibility between tissues and co-binding of tissue-specific transcription factors. Our results also indicate that BMAL1 ability to drive tissue-specific rhythmic transcription not only relies on the activity of BMAL1 cis-regulatory elements (CREs), but also on the activity of neighboring CREs. Characterization of the physical interactions between BMAL1 CREs and other CREs in the mouse liver reveals that interactions are quite stable, and that BMAL1 controls rhythmic transcription by regulating the activity of other CREs. This supports that much of BMAL1 target gene transcription depends on BMAL1 capacity to rhythmically regulate a network of enhancers.

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“…The resultant TBL file is an input for the seqOutBias tabulate subcommand, which tallies the k-mer counts across the selected regions (or full genome), as well as the k-mers corresponding to observed aligned reads from the BAM file. In contrast to other methods (10)(11)(12)15), these numbers are used to scale the reads without the need for Naked DNA to calibrate. This subcommand produces a k-mer count table based on the TBL sequence information and the optional sorted BAM file.…”

Section: ) Tallying the K-mer Counts In The Reference Sequence And Tmentioning

confidence: 99%

“…High throughput DNase-seq experiments described a cleavage pattern at the footprint that was interpreted as a measure of TF/DNA interactions (9); however, subsequent work attributed these artifactual signatures to differential substrate specificity of DNase conferred by the presence of the TF motif (10)(11)(12). As a result, some footprint detection programs now incorporate sequence biases into their algorithms (12,15,22). SeqOutBias provides the option to correct enzymatic sequence bias prior to footprint detection and the output files can be used with existing footprinting algorithms that do not incorporate a correction step.…”

Section: Correction Of Individual Dnase-seq Readsmentioning

confidence: 99%

Universal correction of enzymatic sequence bias reveals molecular signatures of protein/DNA interactions

Martins

Walavalkar

Anderson

et al. 2017

Preprint

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Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and structure. Sequence preferences of enzymes can interfere with the accurate interpretation of these data. We developed seqOutBias to characterize enzymatic sequence bias from experimental data and scale individual sequence reads to correct intrinsic enzymatic sequence biases. SeqOutBias efficiently corrects DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data. We show that seqOutBias correction facilitates identification of true molecular signatures resulting from transcription factors and RNA polymerase interacting with DNA. IntroductionThe field of molecular genomics emerged as classical molecular biology techniques were coupled to high throughput sequencing technology to provide unprecedented genome-wide measurements of molecular features. Molecular genomics assays, such as DNase-seq (1, 2), ChIP-exo (3), and PRO-seq (4, 5), are converging on single-nucleotide resolution measurements. The enzymes that are routinely used in molecular biology and cloning have inherent and often uncharacterized sequence preferences. These preferences manifest more prominently as the resolution of genomic assays increases. Therefore, we developed seqOutBias (https://github.com/guertinlab/seqOutBias) to characterize and correct enzymatic biases that can obscure proper interpretation of molecular genomics data.Enzymatic hypersensitivity assays, such as DNase-seq (1, 2), TACh-seq (6), and ATAC-seq (7), have the potential to measure transcription factor (TF) binding sites genome-wide in a single experiment. These assays strictly measure enzymatic (DNase, Tn5 transposase, Benzonase, or Cyanase) accessibility to DNA and not a specific biological event, making data challenging to deconvolve. Standard algorithms scan for footprints, which are depletions of signal in larger regions of hypersensitivity (8-12). Many transcription factors, however, do not exhibit composite footprints if enzymatic cut frequency is averaged at all ChIP-seq validated binding sites with strong consensus motifs (10-13). Moreover, the inability to detect a footprint at any individual TF binding site results in high false negative rates for footprinting algorithms (14). Accurate footprinting is also confounded by the artifactual molecular signatures that result from enzymatic sequence preference (10-12). DNase footprinting algorithms can incorporate DNase cut preference data to abrogate this bias (12, 15). However, no existing tools specialize in correcting intrinsic sequence bias for a diverse set of enzymes and experimental methodologies.We find that correcting for enzymatic sequence bias highlights true molecular signatures that result from TF/DNA interactions. Despite the limitations of enzymatic hypersensitivity footprinting and sequence bias signatures, hypersensitive regions reveal a near-comprehensive set of functional regulatory regio...

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Wellington-bootstrap: differential DNase-seq footprinting identifies cell-type determining transcription factors

Cited by 52 publications

References 32 publications

Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development

Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development

Tissue-specific BMAL1 cistromes reveal that enhancer-enhancer interactions regulate rhythmic transcription

Universal correction of enzymatic sequence bias reveals molecular signatures of protein/DNA interactions

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