|
| Status |
Public on Jan 12, 2019 |
| Title |
Control_CTCF_ChIPSeq |
| Sample type |
SRA |
| |
|
| Source name |
Hela S3 cells
|
| Organism |
Homo sapiens |
| Characteristics |
cell line: Hela S3 cells
genotype/variation: stably expressed FLAG tag alone
chip antibody: CTCF(Active motif, 61311)
|
| Treatment protocol |
For loss or gain function of CTCF and CTCF-s, lentivirus was generated in 293T cells, After 48hrs of lentivirus production, harvested the virus and filtered with 0.45 μM filter (Millipore), and infected Hela S3 cells twice, after removing virus for 1 day, screened infected cells with 4 μg/ml of puromycine for at least one weak.
|
| Growth protocol |
HeLa-S3 cells were cultured in DMEM (Hyclone) supplemented with 10% fetal bovine serum. Cultured cells were maintained at 37°C in a 5% CO2 incubator.
|
| Extracted molecule |
genomic DNA |
| Extraction protocol |
ChIP was performed as previously described (Huang et al.,2013). Briefly, crosslinked cells was sonicated and incubated with protein A and protein G dynabeads (1:1 mix) and the indicated antibodies. Antibody bound DNA was subsequently washed with low salt wash buffer, high salt wash buffer, LiCl wash buffer once, respectively, and then TE wash buffer twice. ChIPed DNA was reverse-crosslinked and purified for DNA library construction followed by sequencing or ChIP-qPCR analysis.
ChIP-Seq and RNA-Seq libraries were prepared for sequencing using standard Illumina protocols
|
| |
|
| Library strategy |
ChIP-Seq |
| Library source |
genomic |
| Library selection |
ChIP |
| Instrument model |
Illumina NextSeq 500 |
| |
|
| Data processing |
Before alignment, reads were qualified by FastQC (v0.11.2). TrimGalore (v0.4.4) was used to trim adaptor and low-quality reads if necessary.
Trimmed reads were aligned to hg19 human genome assembly using Bowtie27 (v2.2.5) with parameters “--very-sensitive --end-to-end”.
Proper paired and high quality mapped reads (MAPQ>30) were selected with samtools (v1.2). PCR duplicates were removed by picard tools(v1.90).
reads were then subjected to MACS2 (v2.1.0.20150731).
Peaks with q value less than 0.01 were kept for subsequent analysis. Signal tracks for each sample were generated using the MACS2 pileup function and were normalized to 1 million reads. Bigwig files were generated using the bedGraphToBigWig command for visualization. ChIP signal subtracted with input was used to draw heatmap and profile. Peak annotation was performed using annotatePeaks function from homer. Stringent overlap was done using bedtools (v2.25.0) after peaks width was adjusted to 200 bp region around peak summit. Differential peaks were called by macs2 bdgdiff function with default parameters. *control_lambda.bw files include input signal while *treat_pileup.bw represents ChIP signal.
For RNA-Seq analysis, raw reads were aligned to a Bowtie2 (v2.2.5) indexed human genome (hg19 sourced from UCSC) using STAR (v2.5.2a).
Gene expression levels were quantified as read counts generated by RSEM (v1.2.22) with default settings.
Raw tag counts were normalized for GC content using EDASeq (v2.8.0).
Differential transcript expression was called using DESeq2 (v1.10.1).
Genome_build: hg19
Supplementary_files_format_and_content: peaks, bigWig for ChIP-Seq and tsv for RNA-Seq
|
| |
|
| Submission date |
Jan 08, 2018 |
| Last update date |
Jan 12, 2019 |
| Contact name |
Kaimeng Huang |
| E-mail(s) |
Kaimeng_huang@dfci.harvard.edu
|
| Organization name |
Dana-Farber Cancer Institute
|
| Street address |
4 Blackfan Circle
|
| City |
Boston |
| State/province |
MA |
| ZIP/Postal code |
02215 |
| Country |
USA |
| |
|
| Platform ID |
GPL18573 |
| Series (1) |
| GSE108869 |
An alternative CTCF isoform antagonizes canonical CTCF occupancy and changes chromatin architecture to promote apoptosis |
|
| Relations |
| BioSample |
SAMN08326626 |
| SRA |
SRX3540868 |
