The Role of EMT Modulators in Hematopoiesis and Leukemic Transformation

Goossens, Steven; Haigh, Jody J.

doi:10.5772/34816

Cited by 1 publication

(1 citation statement)

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“…In contrast, motifs for SOX and FOX TFs were preferentially enriched in the endothelial cell cluster whereas the RUNX motif showed strong enrichment in blood but not endothelial cells, supporting results from a prior study that found differential expression of SOX and FOXC genes in hemangioblast-like cells and RUNX1 in the committed hematopoietic cells sorted from human-derived embryoid bodies (Garcia-Alegria et al 2018). The hematopoietic cluster also showed preferential enrichment of SNAI motifs, potentially reflecting the well-characterized involvement of SNAI1 in epithelial-to-mesenchymal transitions, including the development of blood cell precursors from hemogenic endothelium (Goossens and Haigh 2012).…”

Section: Clustering Of Motif Enrichment Data From Medea Reveals Novelsupporting

confidence: 84%

MEDEA: analysis of transcription factor binding motifs in accessible chromatin

et al. 2020

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Deciphering the interplay between chromatin accessibility and transcription factor (TF) binding is fundamental to understanding transcriptional regulation, control of cellular states, and the establishment of new phenotypes. Recent genomewide chromatin accessibility profiling studies have provided catalogs of putative open regions, where TFs can recognize their motifs and regulate gene expression programs. Here, we present motif enrichment in differential elements of accessibility (MEDEA), a computational tool that analyzes high-throughput chromatin accessibility genomic data to identify cell-type-specific accessible regions and lineage-specific motifs associated with TF binding therein. To benchmark MEDEA, we used a panel of reference cell lines profiled by ENCODE and curated by the ENCODE Project Consortium for the ENCODE-DREAM Challenge. By comparing results with RNA-seq data, ChIP-seq peaks, and DNase-seq footprints, we show that MEDEA improves the detection of motifs associated with known lineage specifiers. We then applied MEDEA to 610 ENCODE DNaseseq data sets, where it revealed significant motifs even when absolute enrichment was low and where it identified novel regulators, such as NRF1 in kidney development. Finally, we show that MEDEA performs well on both bulk and single-cell ATACseq data. MEDEA is publicly available as part of our Glossary-GENRE suite for motif enrichment analysis.

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Section: Clustering Of Motif Enrichment Data From Medea Reveals Novelsupporting

confidence: 84%