Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS).

By Gregory E. Crawford, I. Holt, J. Whittle, B. Webb, Denise Tai, Sean Davis, Elliott H. Margulies, Yidong Chen, J. Bernat, D. Ginsburg and 6 more. Genome Research, 2005

522 citations recorded by the source corpus.

A major goal in genomics is to understand how genes are regulated in different tissues, stages of development, diseases, and species. Mapping DNase I hypersensitive (HS) sites within nuclear chromatin is a powerful and well-established method of identifying many different types of regulatory elements, but in the past it has been limited to analysis of single loci. We have recently described a protocol to generate a genome-wide library of DNase HS sites. Here, we report high-throughput analysis, using massively parallel signature sequencing (MPSS), of 230,000 tags from a DNase library generated from quiescent human CD4+ T cells. Of the tags that uniquely map to the genome, we identified 14,190 clusters of sequences that group within close proximity to each other. By using a real-time PCR strategy, we determined that the majority of these clusters represent valid DNase HS sites. Approximately 80% of these DNase HS sites uniquely map within one or more annotated regions of the genome believed to contain regulatory elements, including regions 2 kb upstream of genes, CpG islands, and highly conserved sequences. Most DNase HS sites identified in CD4+ T cells are also HS in CD8+ T cells, B cells, hepatocytes, human umbilical vein endothelial cells (HUVECs), and HeLa cells. However, approximately 10% of the DNase HS sites are lymphocyte specific, indicating that this procedure can identify gene regulatory elements that control cell type specificity. This strategy, which can be applied to any cell line or tissue, will enable a better understanding of how chromatin structure dictates cell function and fate.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/49d95685b36e2a1d00c4d226038adacabd7c732e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

High-resolution profiling of histone methylations in the human genome. (2007, methodology): “…with enhancers in an unbiased Cell 129, 823–837, May 18, 2007 ª2007 Elsevier Inc. 829 8 30 Cell 129, 823–837, May 18, 2007 ª2007 Elsevier Inc. way, we compared histone methylation patterns with a list of 14,184 DNase hypersensitive (HS) sites in human CD4+ T cells (Crawford et al., 2006).”
Linking disease associations with regulatory information in the human genome (2012, background): “ing (DNase-seq) (Crawford et al. 2006; Boyle et al. 2008) can experimentally detect functional regions such as transcription factor”
Combinatorial patterns of histone acetylations and methylations in the human genome (2008, background): “We next examined histone modification patterns at 4,179 intergenic DNase I hypersensitive site”
Benchmark and integration of resources for the estimation of human transcription factor activities (2018, background): “DNase I hypersensitivity (DNase) (Crawford et al. 2006) assays coupled to DNA sequencing (ChIP-seq or DNase-seq).”
FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (2017, methodology): “Among these methods are ChIP-seq, which applies 6 a large-scale chromatin immunoprecipitation assay that maps in vivo tran7 scription factor (TF) binding sites or histone modifications genome-wide [1], 8 and DNase-seq, which identifies genome-wide locations of open chromatin, 9 or “hotspots”, by seq”
PEDLA: predicting enhancers with a deep learning-based algorithmic framework (2016, methodology): “The DNase-Seq and RNA-Seq mapping reads were also binned into 200 bp intervals, and the RPKM value of each bin was calculated.”
High-Throughput Sequencing Technologies (2015, methodology): “A more general method for discoveringmany putative regulatory regions is tomap ‘‘open’’ regions of the genome using DNase I digestion, followed by DNA sequencing of the ends of the fragments (Crawford et al., 2006).”
Analysis of computational footprinting methods for DNase sequencing experiments (2016, methodology): “15 and (2) all aligned reads for naked DNA experiments (termed naked DNA sequence bias) following Yardımcı et al.”
G-quadruplexes in promoters throughout the human genome (2006, background): “(24,25) and were generated from quiescent human CD4 T cells.”
Impacts of the ubiquitous factor Zelda on Bicoid-dependent DNA binding and transcription in Drosophila (2014, background): “Integration of binding activities by Bcd-dependent enhancers Despite recent technical and computational advances (Crawford et al. 2006; Giresi et al. 2007; Heintzman et al. 2007; Visel et al. 2009; modEncode Consortium et al. 2010; Zhu et al. 2013), identifying active enhancers in genome sequences…”
High-resolution mapping and characterization of open chromatin across the genome. (2008, methodology): “In agreement with our previous estimates (Crawford et al., 2004, 2006b), our combined DNase I HS annotation identifies 94,925 DNase I HS sites covering 60 million bases (2.1”
Differential analysis of gene regulation at transcript resolution with RNA-seq (2012, methodology): “Sequencing is now used to map histone modifications and protein-DNA interactions (ChIP-Seq 43,44 ), chromatin accessibility (DNase hypersensitivity 45 , FAIRE 46 ) and conformation (ChIA-PET 47 ), and processing of RNA by protein (RIP-Seq 48 , CLIP-Seq 49 ).”