Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets via protocol-specific bias modeling
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ABSTRACT:
PROVIDER: PRJNA427530 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6418080_1.fastq.gz | Fastqsanger.gz | |||
| SRR6418080_2.fastq.gz | Fastqsanger.gz | |||
| SRR6418071_1.fastq.gz | Fastqsanger.gz | |||
| SRR6418071_2.fastq.gz | Fastqsanger.gz | |||
| SRR6418081_1.fastq.gz | Fastqsanger.gz |
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Project description:DNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBS) in regulatory regions via footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq. Here, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impacts the discrimination of footprint from background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints.
2018-11-23 | GSE108513 | GEO
Project description:BackgroundDNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBSs) in regulatory regions through footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq.ResultsHere, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite the differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impact the discrimination of footprint from the background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints.ConclusionsWe demonstrate that the impact of bias correction on footprinting performance is greater for DNase-seq than for ATAC-seq and that DNase-seq footprinting leads to better performance. It is possible to infer concordant footprints by using replicates, highlighting the importance of reproducibility assessment. The results presented here provide an overview of the advantages and limitations of footprinting analyses using ATAC-seq and DNase-seq.
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Project description:DNase I footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. A number of computational approaches have been developed to accurately identify DNase-seq footprints and these methods have been used as a predictor of TF-DNA interactions by itself or in combination with other epigenetic features. However, recent studies have pointed to a substantial cleavage bias of DNase and its impact on footprinting, casting doubts on its predictive performance. To assess the potential for using DNaseI to identify individual binding sites, we performed DNase-seq experiments on deproteinized naked genomic DNA isolated from two different cell types and determined sequence cleavage bias associated with the DNase-seq protocol. This allowed us to build cleavage bias corrected footprint models specific to individual transcription factors. The predictive performance of these DNase-seq-based binding site models demonstrated that predicted footprints corresponded to high confidence TF-DNA interactions. To quantify the DNase I sequence-dependent cleavage bias, we performed DNase-seq experiments using deproteinized DNA from K562 and MCF7 cell lines.
2014-09-04 | E-GEOD-61105 | biostudies-arrayexpress
Project description:DNase I footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. A number of computational approaches have been developed to accurately identify DNase-seq footprints and these methods have been used as a predictor of TF-DNA interactions by itself or in combination with other epigenetic features. However, recent studies have pointed to a substantial cleavage bias of DNase and its impact on footprinting, casting doubts on its predictive performance. To assess the potential for using DNaseI to identify individual binding sites, we performed DNase-seq experiments on deproteinized naked genomic DNA isolated from two different cell types and determined sequence cleavage bias associated with the DNase-seq protocol. This allowed us to build cleavage bias corrected footprint models specific to individual transcription factors. The predictive performance of these DNase-seq-based binding site models demonstrated that predicted footprints corresponded to high confidence TF-DNA interactions.
2014-09-04 | GSE61105 | GEO
Project description:Deep sequencing of size-selected DNaseI-treated chromatin (DNase-seq) allows high resolution measurement of chromatin accessibility to DNaseI cleavage, permitting identification of de novo active cis regulatory modules (CRMs) and individual transcription factor (TF) binding sites. We adapted DNase-seq to nuclei isolated from C. elegans embryos and L1 arrest larvae to generate high-resolution maps of TF binding. Over half of embryonic DNaseI hypersensitive sites (DHS) were annotated in noncoding sequences, with 23% in intergenic, 11% promoter regions and 21% in introns, with similar statistics in data collected from L1 arrest larvae. Noncoding DHS exhibit high evolutionary sequence conservation and are enriched in marks of enhancer activity and transcription. We validated noncoding DHS against a previously investigated set of enhancers from myo-2, myo-3, hlh-1, elt-2 and lin-26/lir-1 gene loci and recapitulated 15 of 17 known enhancers in these loci. We then mined the DNase-seq data to identify putative active CRMs and TF footprints. Our DNase-seq data could also be used to improve predictions of tissue-specific expression compared to motifs alone. In a pilot functional test, 10 of 15 DHS from pha-4, icl-1 and ceh-13 drove reporter gene expression in transgenic C. elegans. Overall, we provide experimental annotation of 26,644 putative CRMs in the embryo containing 55,890 TF footprints, and 15,841 putative CRMs in the L1 arrest larvae containing 32,685 TF footprints.
2017-08-01 | GSE97425 | GEO
Project description:Using DNaseI hypersensitivity (HS) assays (Dnase-seq), high resolution DNaseI digestion profiles were generated genome-wide in diverse human cell types. We showed that within general regions of DNaseI HS that are known to identify locations of gene regulatory elements, DNaseI digestion patterns allowed us to identify locations of individual transcription factor binding sites that protected against the bound DNA against digestion. To measure the accuracy of these footprints, we also generated ChIP-seq data for the CTCF DNA binding factor in the same cell growths. We found that DNaseI footprints containing the CTCF canonical binding motif show significant ChIP-seq signal while CTCF binding motifs not in footprints show almost no signal providing one measure of valdation of the DNaseI footprints. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Six cell lines representing cervica carcinoma, chronic myeloid leukemia, human embryonic stem cells, lymphoblastoid cells, human epidermal keratinocytes and umbilical vein endothelial cells were analyzed using Dnase-seq.
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Project description:Using DNaseI hypersensitivity (HS) assays (Dnase-seq), high resolution DNaseI digestion profiles were generated genome-wide in diverse human cell types. We showed that within general regions of DNaseI HS that are known to identify locations of gene regulatory elements, DNaseI digestion patterns allowed us to identify locations of individual transcription factor binding sites that protected against the bound DNA against digestion. To measure the accuracy of these footprints, we also generated ChIP-seq data for the CTCF DNA binding factor in the same cell growths. We found that DNaseI footprints containing the CTCF canonical binding motif show significant ChIP-seq signal while CTCF binding motifs not in footprints show almost no signal providing one measure of valdation of the DNaseI footprints. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
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