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  • ChIP-seq study analysing adult Drosophila melanogaster head, glial, neuronal and fat body, as well as embryonic RNA pol II and H2A.v binding by employing the GAL4-UAS system to generate GFP-fusion proteins and ChIP-seq
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  • The Drosophila ubiquitin receptor dDsk2 associates to chromatin and stabilizes binding of the euchromatic dHP1c/WOC/ROW-complex (dHP1EU) to the transcription-start site (TSS) of active genes ChIP-Seq peak calling of WOC, ROW, Z4, HP1c and Dsk2 against Input sample in Drosophila melanogaster S2 cells
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  • ChIP-seq was performed to compare binding the genome-wide binding profile of the CLAMP transcription factor in two different Drosophila species. ChIP seq experiments compare the binding profile of CLAMP in female larvae to identify conservation of its binding sequence.
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  • Drosophila
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  • ChIP-seq and mRNA-seq experiments were performed to understand the role of the CLAMP protein in dosage compensation ChIP-seq experiments compared the binding profiles of CLAMP in male and female cells and mRNA-seq data to define the role of CLAMP in regulating genes on the X-chromosome
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  • UCSC Browser Screenshot of Sarkosyl Dependent and NELF RNAi GRO-Seq Experiments, Related to Figure 2 TSS-RNA reads (Nechaev et al., 2010) marking TSSs are in dark blue for the plus and minus strand (read/base/10ˆ6 reads). GRO-seq reads (reads/base/10ˆ6 reads) aligning to the plus strand are shown in red; minus strand in blue. ChIP-seq for total Pol II (α-Rpb3) is shown in green (reads/25bp bin), and gene annotations are shown at the bottom in blue. The arrowheads depict TSSs and the ∗ denotes a TSS that is reannoated with our data sets. ... Pol II at Promoters Is Predominantly Engaged and Competent for Elongation (A) Representative browser shot showing Pol II Chip-seq (green) and GRO-seq (red) with y axis in reads/bp/10exp6. The regions used for calculating the engaged and competent fraction (ECF) at promoters are indicated below. (B) Schematic explaining the workflow used to calculate the ECF for Pol II at promoters. (C) Histogram showing the distribution of ECF values for significantly bound promoters (n = 3,168). The vertical lines represent a 50% (black), the average (red), and the Hsp70 (green) ECFs. Promoters with the lowest ECFs are highlighted in purple. (D) Boxplots showing Pol II ChIP-seq levels at promoters with different ranges of ECF. Promoters with the lowest (purple) and the highest (dark red) ECF values have less Pol II bound at promoters in ChIP-seq experiments than promoters with less extreme ECF values (middle 20% shown), suggesting that the ChIP and GRO discrepancies here could be due to experimental noise. The box spans the first quartile (Q1, bottom) to third quartile (Q3, top), the horizontal line in the box represents the median, and the whiskers extend as follows: (Q1 or Q3 + 1.5 )∗(Q3-Q1). See also Figures S6 and S7. ... RNA Polymerase Distribution on mRNA-Encoding Genes Using GRO-Seq (A) A representative view of GRO-seq data from S2 cells in the UCSC genome browser (Kent et al., 2002). GRO-seq reads (reads/base) aligning to the plus strand are shown in red; minus strand in blue. ChIP-seq for total Pol II (α-Rpb3) is shown in green (reads/25 bp bin), and gene annotations are shown at the bottom in blue. (B) GRO-seq data aligned to transcription start sites (TSSs). For all genes, reads aligning to the sense strand of the gene are in red; antisense strand in blue. For nonbidirectional genes (head-to-head promoters within 1 kb removed), reads aligning to the sense strand of the gene are in green; antisense strand in orange. (C) Comparison of directionality of Drosophila and human promoters. The distribution of the ratios of sense and antisense reads around promoters (log2) is plotted for active promoters (>25 reads) in IMR90 cells (green) and Drosophila S2 cells (blue). How different types of directionality of transcription from promoters are reflected in the ratio are indicated in italicized lettering. (D) GRO-seq profiles from ±1.5 kb relative to TSS are shown for all human promoters (green, sense; orange, antisense) or human promoters that contain a TATA box (red, sense; blue, antisense). (E) GRO-seq data aligned to gene end for all genes (red, sense; blue, antisense), and after convergent genes within 1.5 kb are removed (green, sense; orange, antisense). See also Figures S1 and S2. ... Supporting Genomic Data at Enhancers in This Study, Related to Figure 4 (A) GRO-seq data at putative human enhancers (n = 34,915). GRO-seq data is from IMR90 cells (Core et al., 2008). Data was compiled relative to the center of DHS sites. (B and C) Pol II ChIP-seq and NELF ChIP-chip data (Gilchrist et al., 2010), respectively, around Drosophila putative enhancers. ... Comparison between Assays that Detect Polymerase at Promoters and in Genes, Related to Figure 5 (A–C) Shown are scatter-plots comparing amount of sequencing reads between (A) GRO-seq and Pol II ChIP-seq, (B) small-RNA-seq to ChIP-seq, and (C) GRO-seq to small-RNA-seq at promoters. All unique promoters are shown in black (n = 12,541); promoters called Pol II-bound by ChIP-seq (n = 3,168) in red. rho is Spearman's correlation coefficient between the two data sets. (D) Normalization between ChIP-seq and GRO-seq data sets through fitting of signal within gene bodies. Plotting of the signal for each assay within all genes (black) shows a poor correlation (rho = 0.54), whereas plotting the signal for each after selecting genes that are highly active gives (red) an excellent correlation between data sets (rho = 0.87). Highly active genes are classified as those with the top 10% of Ser2P ChIP (n = 1874) signal within the gene (mark of active polymerases). The fit of the gene data for highly active genes is shown in green. This equation is used for calculating the engaged, competent fraction of polymerase at promoters.
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  • This SuperSeries is composed of the following subset Series: GSE33546: Polycomb repressive complex 2-dependent and –independent functions of Jarid2 in transcriptional regulation in Drosophila [ChIP-Seq] GSE36038: Polycomb repressive complex 2-dependent and –independent functions of Jarid2 in transcriptional regulation in Drosophila [Affymetrix] Refer to individual Series
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  • No Redistribution of Pol II over Transposons Is Observed in piwi Mutant Files (A) Scatterplot displaying Pol II ChIP-seq RPM values versus input RPM values over consensus transposable elements in wild-type and piwi mutant flies. (B) Shown are Pol II ChIP-seq and input RPM levels over the transposon consensus sequences of F-element and mdg3. ... The Huang et al. Data Processing Pipeline Generates Artificial Enrichment over Repetitive Regions The Piwi ChIP-seq and input/background datasets were processed following the Huang et al. pipeline (”Piwi ChIP”). In addition, the pipeline was also run swapping the ChIP and the input, i.e., the control sample was treated as ChIP and vice versa, resulting in the “background” track. (A) The fraction of signal mapping to transposable elements was calculated, revealing higher “enrichment” in the background than in the Piwi ChIP-seq dataset. (B) Strong apparent enrichment over individual transposable elements was observed in the ChIP track (upper track), as reported by Huang et al., but also in the background track (lower track), and even over different portions of the same transposable element in both tracks (middle track), strongly arguing that the enrichment over transposable elements reported by Huang et al. is a computational artifact. Signal observed on individual copies correlates well with enrichment profiles when mapped to the consensus sequence of the respective transposons (shown below each track). Sequences showing “enrichment” in the background are indicated with gray blocks to depict the correlations between the signal on individual TE copies and the consensus sequence. (C) Fraction of signal (calculated with the Huang et al. pipeline) mapping to transposable elements for the modENCODE transcription factor set. ... Piwi Is Not Enriched over Transposons in the Huang et al. Dataset (A) Absence of enrichment in the Piwi ChIP-seq dataset and high enrichment of H3K9me3 (from Muerdter et al., 2013) over consensus transposons; each dot corresponds to a transposon consensus sequence. (B) The concentration of Piwi signal over transposons in the Huang et al. dataset arises from failure to normalize multiply mapping reads. Shown is the region from Figure 2C of Huang et al. (2013). Top: Piwi ChIP-seq and background (input) data from Huang et al. showing (1) unique alignments; (2) all alignments, with reads normalized for mapping multiplicity; and (3) all alignments, with each alignment treated as a uniquely mapped read. Bottom: data processed per Huang et al. The enrichment of Piwi over repetitive elements is only observed when no multi-read normalization is applied and is seen in both ChIP and control datasets. (C) The minimal Piwi ChIP-seq enrichment observed over some individual transposable elements is well within the range of experimental noise. Shown is the cumulative distribution function (CDF) of the ratio between total ChIP RPM and control/background RPM for each DNA, LINE, or LTR repetitive element (each dot represents an individual TE insertion). Piwi ChIP-seq data from Huang et al. (red) and H3K9me3 data from Muerdter et al. (blue) are plotted alongside the cumulative distribution for 11 transcription factor ChIP-seq datasets from modENCODE (gray), for which there is no expectation of enrichment at repetitive elements. Only repeat instances with at least 10 RPM in at least one of the ChIP and control datasets for each ChIP/background pairing were included. H3K9me3 showed high average enrichment over background at most of the elements in all three classes. In contrast, the Piwi ChIP-seq data were well within the range of the distributions for modENCODE transcription factors.
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  • Drosophila melanogaster
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  • This dataset contains zebrafish (Danio rerio) raw RNA and ChIP sequencing data: RNA-seq: RNAseq_Wildtype_rep[12].fastq.gz: 2 biological replicates of single-end RNA-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates RNAseq_Wildtype_rep[3-6].fastq.gz 4 biological replicates of paired-end RNA-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates ChIP-seq: lane1_MPZezh2WT-24hpf-Ezh2__R[12].fastq.gz: 1 sample of paired-end Ezh2 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates lane1_MPZezh2WT-24hpf-Rnf2__R[12].fastq.gz: 1 sample of paired-end Rnf2 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates lane1_MPZezh2WT-24hpf-H3K27me3__R[12].fastq.gz: 1 sample of paired-end H3K27me3 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates *MPZezh2WT-24hpf-H3K4me3*: 2 biological replicates of paired-end H3K4me3 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates MPZezh2WT-24hpf-Ezh2-spikein-13277_R[12].fastq.gz: 1 sample of paired-end Ezh2 ChIP-seq data (with Drosophila H2Ay spike in) from 24hpf wild-type (TU/TL background) whole embryo lysates MPZezh2WT-24hpf-H3K27A[cC]*: 2 biological replicates of paired-end H3K27ac ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates MPZezh2WT-24hpf-H3K27me3-spikein-13275_R[12].fastq.gz: 1 sample of paired-end H3K27me3 ChIP-seq data (with Drosophila H2Ay spike in) from 24hpf wild-type (TU/TL background) whole embryo lysates
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