An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis

Abstract

Thousands of epigenomic datasets have been generated in the past decade, but it is difficult for researchers to effectively utilize all the data relevant to their projects. Systematic integrative analysis can help meet this need, and the VISION project was established for ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis. Here, we systematically integrated extensive data recording epigenetic features and transcriptomes from many sources, including individual laboratories and consortia, to produce a comprehensive view of the regulatory landscape of differentiating hematopoietic cell types in mouse. By employing IDEAS as our Integrative and Discriminative Epigenome Annotation System, we identified and assigned epigenetic states simultaneously along chromosomes and across cell types, precisely and comprehensively. Combining nuclease accessibility and epigenetic states produced a set of over 200,000 candidate cis-regulatory elements (cCREs) that efficiently capture enhancers and promoters. The transitions in epigenetic states of these cCREs across cell types provided insights into mechanisms of regulation, including decreases in numbers of active cCREs during differentiation of most lineages, transitions from poised to active or inactive states, and shifts in nuclease accessibility of CTCF-bound elements. Regression modeling of epigenetic states at cCREs and gene expression produced a versatile resource to improve selection of cCREs potentially regulating target genes. These resources are available from our VISION website (usevision.org) to aid research in genomics and hematopoiesis.

Systematic integration of GATA transcription factors and epigenomes via IDEAS paints the regulatory landscape of hematopoietic cells

“…The registry captured virtually all the known erythroid cCREs, and it included a large majority (two-thirds) of DNA segments bound by the transcription co-activator EP300 in murine erythroleukemia cells, CH12 cells (a model for B cells), and fetal liver. 47 Thus, the recall appears to be reasonable. Further experimental tests should provide insight into the precision or specificity of the cCRE predictions.…”

“…However, this collection does look promising in several respects. The registry captured virtually all the known erythroid cCREs, and it included a large majority (two‐thirds) of DNA segments bound by the transcription co‐activator EP300 in murine erythroleukemia cells, CH12 cells (a model for B cells), and fetal liver . Thus, the recall appears to be reasonable.…”

“…Thus, consistency in data processing and appropriate normalization of the data were also key components of the IDEAS segmentation pipeline. More complete descriptions of the approaches developed and utilized are given elsewhere . The impact of normalization is illustrated in Figure .…”

“…More complete descriptions of the approaches developed and utilized are given elsewhere. 46,47 The impact of normalization is illustrated in Figure 2. The upper tracks of individual features show the raw numbers of reads mapped to DNA intervals for one of two replicates, all set to the same scale.…”

“…The integrative segmentation from IDEAS allowed us to take a straightforward approach to predicting candidate cis-regulatory elements or cCREs. 47 The nucleaseaccessible DNA intervals (hypersensitive sites or HSs) in each cell type were determined by peak-calling method on the DNase-seq and ATAC-seq data. We then gathered all HSs from all cell types (requiring replication within a cell type if available) and merged the overlapping ones.…”