|
| Status |
Public on Feb 18, 2019 |
| Title |
ChIP_H3K27ac_MM029 |
| Sample type |
SRA |
| |
|
| Source name |
Melanoma cell line
|
| Organism |
Homo sapiens |
| Characteristics |
cell line: MM029
time point after sox10 kd: Base line
|
| Treatment protocol |
SOX10-KD was performed using a SMARTpool of four siRNAs against SOX10 (SMARTpool: ON-TARGETplus SOX10 siRNA, number L017192-00-0005, Dharmacon) at a concentration of 20nM using as medium Opti-MEM (Thermo Fished Scientific) and omitting antibiotics. The cells were incubated for 24, 48 or 72 hours before processing.
|
| Growth protocol |
Cells were kept at 37°C, with 5% CO2 and were maintained in Ham's F10 nutrient mix (Thermo Fished Scientific) supplemented with 10% fetal bovine serum (FBS; Invitrogen) and 100 µg ml-1 penicillin/streptomycin (Thermo Fished Scientific).
|
| Extracted molecule |
genomic DNA |
| Extraction protocol |
Chromatin was extracted using the MagnaChIP-seq kit (millipore). 4 µg of antibody was used on 2 million cells. Sonication was performed for 12 cycles, 30 sec pulses using the biorupter.
Illumina TruSeq
|
| |
|
| Library strategy |
ChIP-Seq |
| Library source |
genomic |
| Library selection |
ChIP |
| Instrument model |
Illumina HiSeq 2500 |
| |
|
| Description |
ChIP_H3K27ac_MM029.nofirst5bp.minMQ4.sorted.bw
|
| Data processing |
The reads from scATAC-seq samples were first cleaned for adapters using fastq-mcf (as part of ea utils; v1.1.2-686). Read quality was then checked using FastQC (v0.11.5). Paired-end reads were mapped to the human genome (hg19-Gencode v18) using STAR (v2.5.1) applying the parameters --alignIntronMax 1, --aslignIntronMin 2 and --alignMatesGapMax 2000. Mapped reads were filtered for quality using SAMtools (v1.2) view with parameter –q4, sorted with SAMtools sort and indexed using SAMtools index. Duplicates were removed using Picard (v1.134) MarkDuplicates using OPTICAL_DUPLICATE_PIXEL_DISTANCE=2500. To filter out cell of bad quality, transcription start site aggregation plots were made using a custom script and cell having a noisy profile were removed from further analyses. This lead to a final of 598 good quality cells over 8 Fluidigm C1 runs. Bam files of good quality single cells were aggregated per condition and peaks were called on these aggregated samples using MACS2 (v2.1.1) callpeak using the parameters --nomodel and --call-summits. The peak files per condition were merged (78,661 peaks in total before blacklisting) and a count matrix was generated by using featureCounts (as part of Subread; v1.4.6) of all separate single cell bam files on the merged peak file (after conversion of the merged peak bed file to a gff format using a custom script). To visualise the aggregated cells per sample, normalised bedGraphs were produced by genomeCoverageBed (as part of bedtools; v2.23.0) using as scaling parameter (-scale) size factors obtained from DEseq2 (v1.18.1). BedGraphs were converted to bigWigs by the bedtools suit functions bedSort to sort the bedGraphs, followed by bedGraphToBigWig to create the bigWigs.
The reads from OmniATAC-seq samples were first cleaned for adapters using fastq-mcf (as part of ea utils; v1.1.2-686). Read quality was then checked using FastQC (v0.11.5). The reads were mapped to the human genome (hg19-Gencode v18) using STAR (v2.5.1) applying the parameters --alignIntronMax 1 and --aslignIntronMin 2. Mapped reads were filtered for quality using SAMtools (v1.2) view with parameter –q4, sorted with SAMtools sort and indexed using SAMtools index. Peaks were called using MACS2 (v2.1.1) callpeak using the parameters --nomodel and --call-summits on the 8 conditions separately. A count matrix was generated by using featureCounts (as part of Subread; v1.4.6) of all separate bam files on the merged peak file (after conversion of the merged peak bed file to a gff format using a custom script). Normalised bedGraphs were produced by genomeCoverageBed (as part of bedtools; v2.23.0) using as scaling parameter (-scale) size factors obtained from DEseq2 (v1.18.1). BedGraphs were converted to bigWigs by the bedtools suit functions bedSort to sort the bedGraphs, followed by bedGraphToBigWig to create the bigWigs, which were used in IGV for visualisation.
Reads obtained from H3K27Ac ChIP-seq were mapped to the genome using Bowtie2 (v2.1.0). The sensitive-local setting for Bowtie2 was used to correct for a high percentage of mismatches at the start of a read. bigWig files were generated from bam files using BEDTools (v2.17.0) and the scores represent the coverage.
Genome_build: hg19
Supplementary_files_format_and_content: scATAC-seq abundance measurement is a raw count matrix containing peaks (called from the aggregates per condition) as row and the single cells as columns.
Supplementary_files_format_and_content: OmniATAC-seq abundance measurement is a raw count matrix containing peaks as row and the single cells as columns.
Supplementary_files_format_and_content: bigWig files were generated from bam files using BEDTools (v2.23.0) and the scores represent the coverage
|
| |
|
| Submission date |
May 16, 2018 |
| Last update date |
Feb 18, 2019 |
| Contact name |
Stein Aerts |
| E-mail(s) |
stein.aerts@kuleuven.vib.be
|
| Organization name |
KU Leuven
|
| Street address |
O&N4 Herestraat 49 PO Box 602
|
| City |
Leuven |
| ZIP/Postal code |
3000 |
| Country |
Belgium |
| |
|
| Platform ID |
GPL16791 |
| Series (1) |
| GSE114557 |
cisTopic: cis-regulatory topic modelling on single-cell ATAC-seq data |
|
| Relations |
| BioSample |
SAMN09216358 |
| SRA |
SRX4091685 |
