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Fig. 1 | Genome Biology

Fig. 1

From: ChromTime: modeling spatio-temporal dynamics of chromatin marks

Fig. 1

Overview of the ChromTime method. a Examples of H3K4me2 peaks with steady, expanding, and contracting boundary dynamics, shown from left to right, respectively, across five time points during mouse T-cell development [17]. Time points 1, 2, and 3 correspond to in vitro differentiated T-cell precursors (FLDN1, FLDN2a, and FLDN2b), whereas time points 4 and 5 correspond to in vivo purified thymocytes (ThyDN3 and ThyDP). Normalized ChIP-seq signal, MACS2 [38] peaks (black rectangles), and ChromTime output are shown for each time point. Peaks upstream of the Zfp148 gene are called steady by ChromTime despite fluctuations of MACS2 peak boundaries. In contrast, ChromTime calls a peak at the Skap1/GM11529 promoter to expand after time points 2 and 3. Conversely, ChromTime calls a peak upstream of the GPR141 gene to contract after time points 2, 3, and 4. b Overview of the ChromTime method. During the block-finding stage, input foreground and, optionally, control reads are used to determine blocks of signal enrichment. In the dynamics prediction stage, for each block, peak boundary positions are predicted at each time point and peak boundary dynamics are predicted at each pair of consecutive time points. c Predicting dynamics for one block. Boxes represent genomic bins at each time point. Foreground signal is depicted as blue bars for each bin whose height represents the number of reads mapped to the bin. ChromTime learns a probabilistic mixture model from the input data to partition each block at each time point into peak and background components. Bins in the peak component (orange) mark peaks of signal enrichment whereas those in the background component (white) mark flanking background signal. The movement of the boundaries on the left and the right side of peaks between consecutive time points is estimated by reasoning jointly about the input data from all time points

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