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Sample GSM3762891 Query DataSets for GSM3762891
Status Public on Apr 08, 2020
Title WT_H3K4me1ChIP_PFA_rep2
Sample type SRA
 
Source name Mouse embryonic stem cells
Organism Mus musculus
Characteristics genotype: WT
cell type: Mouse embryonic stem cells
chip antibody: RevMAB H3K4me1 #31-1046-00
Growth protocol mESCs were grown in chemically defined 2iL medium (defined naïve culture conditions): 50% DMEM/F-12; 50% Neurobasal Medium (no L-glutamine); B-27 Supplement, minus vitamin A (1:100); N-2 Supplement (1:200); 2 mM L-Glutamine or GlutaMAX (1:100); 0.1 mM β-Mercaptoethanol (1:500); 3 µM CHIR 99021 (GSK3β inhibitor) (1:1000 from 3 mM stock in DMSO); 1 µM PD 0325901 (MEK inhibitor) (1:1000 from 1 mM stock in DMSO); 1000 U/ml LIF (1:10,000 from 107 U/ml stock); 50 U/ml Pen/Strep (1:100).
Extracted molecule genomic DNA
Extraction protocol ChIP-seq for non-histone proteins (Dnttip1) was carried out with 5 x 10e7 cells and for histones with 2-2.5 x 10e7 mESCs, using a modified version of Lee et al., Nature Protocols, 2006.
ChIP-seq Library Preparation and Sequencing:DNA was quantified using the Quant-iT PicoGreen dsDNA Assay (Thermo Fisher Scientific, P11496). Libraries were prepared with the KAPA HyperPrep Library Kit (Roche, 07962363001) and analyzed for insert size distribution with the High Sensitivity DNA Kit (Agilent, 5067-4626) on a 2100 Bioanalyzer or the High Sensitivity D1000 ScreenTape Assay (Agilent, 5067-5584, 5067-5585, 5067-5587, 5067-5603) on a 4200 TapeStation. Libraries were quantified using the Quant-iT PicoGreen dsDNA Assay. Single end 50 cycle sequencing was performed on a HiSeq 2500, HiSeq 4000, or NovaSeq 6000 System (all from Illumina).
 
Library strategy ChIP-Seq
Library source genomic
Library selection ChIP
Instrument model Illumina HiSeq 2500
 
Description SJMMNORM050492_C57-mES_WTBRUCE4_rep1-H3K4me1-AB4
Data processing Mapping reads and visualizing data:ChIP-seq raw reads were aligned to the mouse and Drosophila melanogaster hybrid reference genomes (mm9+dm3) using BWA (version 0.7.12; default parameter) and duplicated reads were then marked with Picard (version 1.65), with only nonduplicated reads kept by samtools (version 1.3.1, parameter ‘‘-q 1 -F 1024’’). Mapped reads were then split into two bam files (mapped to mm9 and dm3 respectively). For data quality control and to estimate the fragment size, the nonduplicated version of SPP (version 1.11) was used to calculate the relative strand correlation value with support of R (version 3.3.1). To visualize ChIP-seq data on the integrated genome viewer (IGV) (version 2.3.82), we utilized genomeCoverageBed (bedtools 2.25.0) to obtain genome-wide coverage in BEDGRAPH file format and then converted it to bigwig file format by bedGraphToBigWig. The bigwig files were scaled to 15 million reads to allow comparison across samples.
Peak calling, annotation and motif analysis:MACS2 (version 2.1.1 20160309) was used to call narrow peaks (Dnttip1, Hdac1, H3K27ac and H3K4me3) with option “nomodel” and “extsize” defined as fragment size estimated by SPP and a FDR corrected p-value cutoff of 0.05. For broad peak/domain calling (H3K4me1 and H3K27me3). SICER (version 1.1, with parameters of redundancy threshold 1, window size 200 bp, effective genome fraction 0.86, gap size 600 bp, FDR 0.00001 with fragment size defined above) was used for broad peak/domain calling (H3K4me1 and H3K27me3). Enriched regions were identified by comparing the ChIP library file to input library file. Peak regions were defined to be the union of peak intervals from two ChIP replicates of WT or KO mESCs, respectively. Promoter regions were defined as ± 1000 bp from a TSS based on the mouse RefSeq annotation. Genomic feature annotation of peaks was carried out by annotatePeaks.pl, a program from HOMER suite (v4.8.3, http://homer.salk.edu/homer/). HOMER software was used to perform de novo motif discovery and to check for enrichment of known motifs from a set of Dnttip1 peaks (associated with up- or downregulated genes in Dnttip1 KO versus WT mESCs) or all Dnttip1 peaks.
Spike-in normalization and differential analysis:ChIP-seq raw read counts were reported for each region/each sample using bedtools 2.25.0. Spike-in normalization was performed by counting Drosophila reads and mouse reads in each ChIP sample and corresponding input sample and using those counts to generate a normalization factor for each sample, which was calculated as (ChIP_dm3.reads/ChIP_mm9.reads)/(Input_dm3.reads/Input_mm9.reads). Raw read counts were voom normalized and statistically contrasted using the pipeline limma in R (version 3.3.1). The normalization factor defined above was used to modify the mouse library size in edgeR (version 3.16.5) for CPM calculation and differential analysis. An empirical Bayes fit was applied to contrast KO samples to WT samples and to generate log2 fold changes, p-values and false discovery rates for each peak region. Histograms showing average ChIP-seq intensity over gene bodies were generated using ngsplot (v2.61).
Genome_build: mm9
Supplementary_files_format_and_content: bigwig files
 
Submission date May 12, 2019
Last update date Apr 08, 2020
Contact name Hans-Martin Herz
E-mail(s) hans-martin.herz@stjude.org
Phone 901-595-2058
Organization name St. Jude Children's Research Hospital
Street address 262 Danny Thomas Place
City Memphis
State/province TN
ZIP/Postal code 38105-3678
Country USA
 
Platform ID GPL17021
Series (2)
GSE131061 The histone deacetylase complex MiDAC regulates a neurodevelopmental gene expression to control neurite outgrowth [ChIP-Seq]
GSE131062 The histone deacetylase complex MiDAC regulates a neurodevelopmental gene expression to control neurite outgrowth
Relations
BioSample SAMN11632860
SRA SRX5822711

Supplementary file Size Download File type/resource
GSM3762891_SJMMNORM050492_C57-mES_WTBRUCE4_rep1-H3K4me1-AB4.bw 173.9 Mb (ftp)(http) BW
SRA Run SelectorHelp
Raw data are available in SRA
Processed data provided as supplementary file

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