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Status |
Public on Sep 29, 2023 |
Title |
Untransduced iPSC EC Diff Day 15 IgG C&R |
Sample type |
SRA |
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Source name |
BJFF.6
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Organism |
Homo sapiens |
Characteristics |
cell line: BJFF.6 cell type: iPSC cut&run antibody: IgG (#13-0042k, EpiCypher)
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Extracted molecule |
genomic DNA |
Extraction protocol |
Nuclei were isolated by incubating cells in Nuclear Extraction Buffer (NEB) which consists of 20mM HEPES KOH pH7.9, 10mM KCl, 0.1% Triton X-100, 20% Glycerol, EDTA-free Protease Inhibitor (11836170001, Roche) and water for 10 minutes on ice. Aliquots of 250,000 nuclei were spun down then resuspended in nuclear extraction buffer and slowly frozen at -80°C in a Mr. Frosty freezing container (C1562, Sigma-Aldrich). Libraries were prepared with the xGen ssDNA & Low-Input DNA Library Prep Kit (#10009859, IDT) and quantified using the Agilent 4150 TapeStation and Agilent Bioanalyzer before sequencing with NovaSeq6000 (Illumina).
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Library strategy |
OTHER |
Library source |
genomic |
Library selection |
other |
Instrument model |
Illumina NovaSeq 6000 |
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Description |
Untransduced iPSC EC Diff Day 15 IgG C&R SRM ID 2572644
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Data processing |
library strategy: CUT&RUN Paired-end CUT&RUN-seq reads were aligned first to the E. coli reference genome using bowtie (version 1.2.2) in paired-end with parameters -k 1 –best and –un to retain unmapped reads. Because of nonbiological variability among samples, the numbers of E. coli reads were not used for cell-number-normalization hereafter. Retained non-E. coli reads were then mapped to the mouse reference genome (mm10) or human reference genome (hg38) using bowtie in paired-end mode with parameters -p 20 -k 2 -m 2 –best. A BAM file containing CUT&RUN-seq fragments was computationally created from each aligned read-pair using samtools sort -n, bedtools bamToBed -bedpe, a manual conversion from BEDPE to BED3, and bedtools bedToBam. For display, the reference genomes were binned into 50bp windows using bedtools makewindows, and coverage of those bins by fragments was calculated using bedtools intersect -c. Fragment coverage was normalized per million mapped fragments, converted to bedGraph using bedGraphToBigWig, converted to wiggle using bigWigToWig, converted to TDF using igvtools toTDF, and then visualized in the IGV(version 2.16.2.) browser in hg38 for human samples. MACS1.4 was used to identify regions significantly enriched in CUT&RUN-seq reads. Prior to peak-calling, individual reads from each paired-end alignment were discarded if they overlapped the ENCODE-defined Problematic Regions list corresponding to the reference genome in use, and further filtered in mouse if they overlapped the region chr1:24611436-24616256, because of an observed genomically amplified region in all samples near the Col19a1 gene. Retained reads were used for peak-calling against each sample’s corresponding, identically processed, control IgG. In mouse, parameters –keep-dup=auto and –p 0.001 were used; in human, parameters –keep-dup=auto and -p 1e-9 were used. To compare TCP vs. MCP coverage at regions of interest, we collapsed peaks called separately in each sample (N=3 TCP and N=4 MCP in mouse) using bedtools merge. Bedtools closest -t first was used to annotate each peak with the name of the gene whose transcript has the nearest transcription start site from mm10_refGene.gtf downloaded from the UCSC Genome Browser. Individual read coverage in each of the seven samples was separately quantified using bedtools intersect -c. For comparative analyses (Fig.5c), read coverage in each sample was normalized to the millions of mapped reads (RPM), and the average RPMs from each condition were calculated. For statistical analysis (Fig. 5d) of differential coverage, raw reads from the three TCP samples and three representative MCP samples (MCP20_1, MCP20_2, and MCP_38) were used as input for DEseq2. Peaks were considered differentially covered if they had DEseq2 adjusted log2 fold-change >1 or <-1 and adjusted p value of 0.05. We used ROSE (https://bitbucket.org/young_computation/rose) to separately identify super-enhancers from each sample of MCP, TCP, Day 15 Untransduced p53KO iPSC, and Day 15 Lenti-P3F transduced p53KO iPSC CUT&RUN-seq data targeting H3K27ac, as previously described. Each sample was separately processed with its corresponding IgG. For each sample, we used MACS1.4 to identify two sets of peaks from each read set filtered as above. In mouse, we used parameter sets –keep-dup=auto –p 0.001 and –keep-dup=all –p 0.001; in human, we used parameter sets –keep-dup=auto -p 1e-9 and –keep-dup=all -p 1e-9. These two peak sets from each sample were collapsed into a per-sample set of peaks using bedtools merge; these collapsed peaks were used as input for ROSE with parameters -s 12500 -t 1000. Assembly: hg38 Supplementary files format and content: bigWig, bed (for Untransduced Day 15 and P3F Day 15 H3K27ac data)
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Submission date |
Aug 24, 2023 |
Last update date |
Sep 29, 2023 |
Contact name |
Hongjian Jin |
E-mail(s) |
hongjian.jin@STJUDE.ORG
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Organization name |
St Jude Children's Research Hospital
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Department |
Center for Applied Bioinformatics
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Street address |
262 Danny Thomas Place
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City |
Memphis |
State/province |
TN |
ZIP/Postal code |
38015 |
Country |
USA |
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Platform ID |
GPL24676 |
Series (2) |
GSE218274 |
PAX3-FOXO1 expression in endothelial progenitors dictates myogenic reprogramming and rhabdomyosarcoma identity |
GSE241644 |
PAX3-FOXO1 dictates myogenic reprogramming and rhabdomyosarcoma identity in endothelial progenitors [Cut&Run] |
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Relations |
BioSample |
SAMN37144264 |
SRA |
SRX21479522 |
Supplementary file |
Size |
Download |
File type/resource |
GSM7732620_ECD_d15_IgG_Untransduced.humanPE38.pebt.frag.w50.FPM.bw.tdf |
117.6 Mb |
(ftp)(http) |
TDF |
SRA Run Selector |
Raw data are available in SRA |
Processed data provided as supplementary file |
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