1. DMS-Seq for In Vivo Genome-wide Mapping of Protein-DNA Interactions and Nucleosome Centers 
Taichi Umeyama, Takashi Ito 
Cell Reports 
Volume 21, Issue 1, Pages (October 2017)
DOI: /j.celrep
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2. Cell Reports 2017 21, 289-300DOI: (10.1016/j.celrep.2017.09.035)
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3. Figure 1 Principle and Proof of Concept of DMS-Seq
(A) Schematic representation of the principle of DMS-seq.
(B) Steps for DMS-seq library preparation and mapping.
(C) Base composition of DMS-mediated cleavage sites by in vivo and in vitro DMS-seq of the budding yeast. Base compositions are shown for both strands of the reference genome sequence, excluding rDNA and mitochondrial DNA.
(D) Reproducibility of DMS-seq. Scatterplots are shown for normalized depth (i.e., read distribution normalized to the genome-wide average) per 100-nt window between technical replicates (top), between biological replicates (middle), and between in vitro and in vivo DMS-seq (bottom).
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4. Figure 2 Chromatin Landscape Revealed by DMS-Seq
(A) Representative genome browser view of DMS-seq profile for the budding yeast.
(B) DMS-seq peaks at NFRs around TSSs and TTSs. Aggregate plots of normalized average depth are shown for MNase-digested short fragments (≤100 nt) (Henikoff et al., 2011) and DMS-cleaved fragments.
(C) Correlation between gene expression levels and DMS-seq peaks around TSSs and TTSs. Aggregate plots of DMS-seq fragments are shown for genes divided into quintiles based on their expression levels (Yassour et al., 2010).
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5. Figure 3 Pheromone-Induced Alterations of DMS-Seq Patterns
Genome browser views of DMS-seq data in the absence and presence of the mating pheromone α factor. The fragment depths were averaged using replicate data in the absence of the α factor (n = 3, libraries 2, 3, and 4; Table S1). Differential peaks indicated by red bars were called by model-based analysis of ChIP-seq (MACS) (Q < 1 × 10−2) (Zhang et al., 2008) using data generated with and without α factor as treatment and control samples, respectively.
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6. Figure 4 In Vivo Binding of Transcription Factors Revealed by DMS-Seq
(A) Representative genome browser view of DMS-seq and ChIP-seq profiles. For DMS-seq, the fragment depth (black), cleavage frequency of the forward (magenta) and reverse (cyan) strands, and p value of footprints detected using the Wellington program (Piper et al., 2013) (red) are shown. For ChIP-seq, the fragment depth (blue) is shown with the positions of peaks called by MACS (Zhang et al., 2008) (blue) and binding motifs (black).
(B) DMS-seq patterns around the candidate binding sites for Abf1 and Reb1. Aggregate plots are shown for the normalized average depth of in vivo and in vitro DMS-seq data around the Abf1-binding motifs (911 sites; p < 1 × 10−4) and the Reb1-binding motifs (789 sites; p < 1 × 10−4) in intergenic regions.
(C) Overlap between the Abf1-binding motifs associated with DMS footprints and those with ChIP-seq peaks. Data are shown for all motifs (1,796 sites; p < 1 × 10−4). DMS footprints and ChIP-seq peaks (Kasinathan et al., 2014) were detected using Wellington (p < 1 × 10−10) and MACS (Q < 1 × 10−2), respectively. Fisher’s exact test p value is shown for the overlap.
(D) DMS-seq patterns around the Abf1-binding motifs, either associated with or not associated with ChIP-seq signals. Among all motifs (1,796 sites; p < 1 × 10−4), 768 and 1,028 were associated with and not associated with Abf1 ChIP-seq peaks, respectively.
(E) DMS-seq patterns around Cbf1-binding motifs in wild-type and cbf1Δ cells. Aggregate plots of in vivo DMS-seq data generated using wild-type and cbf1Δ cells are shown for Cbf1-binding motifs with ChIP-seq signals (Zhou and O’Shea, 2011) (756 sites; p < 1 × 10−4).
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7. Figure 5 DMS-Seq Pattern around Replication Origins
Aggregate plots around 196 ARS consensus sequences taken from the Saccharomyces Genome Database are shown for both in vivo and in vitro DMS-seq. The fragment depths were averaged using replicate data in the absence of the α factor (n = 3, libraries 2, 3, and 4; Table S1). For comparison, ChIP-seq data (Belsky et al., 2015) are also included in the plot.
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8. Figure 6 Detection of Yeast Nucleosome Centers by DMS-Seq
(A) Representative genome browser view of DMS-seq and MNase-seq profiles. MNase-seq data obtained by 20 min MNase digestion were used (Henikoff et al., 2011).
(B) Anti-correlation between DMS-seq and MNase-seq. Aggregate plots are shown for DMS-seq and MNase-seq (Henikoff et al., 2011) around the TSSs. The middle 60 bp of DMS-seq fragments (any size) and MNase-seq fragments (100–200 nt) were used for plotting as described previously (Ishii et al., 2015). The correlation coefficient was −0.21 between the normalized average depths of DMS-seq and MNase-seq.
(C) Cleavage preference of DMS, MNase, and hydroxyl radicals around the nucleosome centers. Normalized average cleavage frequencies are shown for the DMS-seq, MNase-seq (Henikoff et al., 2011), and chemical cleavage data (Brogaard et al., 2012).
(D) Comparison of in vivo DMS-seq patterns between the budding and fission yeasts. Normalized average cleavage frequencies around the nucleosome centers of budding yeast (Brogaard et al., 2012) and fission yeast (Moyle-Heyrman et al., 2013) are shown.
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9. Figure 7 Application of DMS-Seq to Human Cells
(A) DMS-seq patterns around CTCF-binding sites. Aggregate plots around the CTCF-binding sites are shown for the in vivo and in vitro DMS-seq generated with IMR90 cells. The genomic sites with the top 50% of ChIP-seq peaks in IMR90 cells were used for plotting.
(B) Cleavage frequency by DMS and MNase around CTCF-binding sites. The genomic sites with the top 50% of the ChIP-seq peaks in IMR90 cells were used for plotting. The cleavage frequency was smoothed using a 70-bp sliding window for both DMS-seq and MNase-seq (Gaidatzis et al., 2014).
(C) DMS cleavage frequency around nucleosome centers in the human genome. The positions of nucleosome centers were predicted from a pooled DNase-seq dataset (Zhong et al., 2016).
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