GSE61874
Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions.
Robust comparison of deep-sequencing data sets is often hampered by differences in their pattern structure or signal-to-noise ratio. Here, we established multi-scale correlation evaluation (MCORE), a method to dissect and compare the genome-wide topology of chromatin features. We applied MCORE to Hi-C, ChIA-PET, ChIP-seq, RNA-seq and Bisulfite-seq data from mouse embryonic stem cells and neural cells to track the extension, transformation and spatial repositioning of chromatin domains during differentiation. Overall design: ChIP-seq of histone modifications and RNA-seq of total RNA in mouse ESCs and retinoic acid derived NPCs