title: GSM6246281: CHIP-seq CD44 HIGH rep 1; Mus musculus; ChIP-Seq
library_construction_protocol: ATAC-seq: 1 x 10^5 parental MCA205 cells and their IFN-CSC counterparts were treated with DNase I at 37°C for 30min. Cells were then washed and cryopreserved in culture medium containing FBS and 5% DMSO in 1,5 mL eppendorf microtubes. Cryopreserved cells were sent to Epigenetics Services Active Motif, Inc. (Carlsband, CA) to perform the ATAC-seq assay. Cells were thawed in a 37°C water bath, pelleted, washed with cold PBS, and tagmented. In details, cell pellets were resuspended in lysis buffer, pelleted, and tagmented using the enzyme and buffer provided in the Nextera Library Prep Kit. Tagmented DNA was then purified using the MinElute PCR purification kit, amplified with 10 cycles of PCR, and purified using Agencourt AMPure SPRI beads. Resulting material was quantified using the KAPA Library Quantification Kit for Illumina platforms, and sequenced with PE42 sequencing on the NextSeq 500 sequencer (Illumina). RNA-seq: 2.5 x 10^5 parental MCA205 cells and their IFN-CSC counterparts were harvested, washed and cryopreserved in 1,5 mL eppendorf microtubes. As above, RNA-seq analysis was performed by Epigenetics Services Active Motif, Inc. Total RNA was isolated from cells by using the RNeasy Mini Kit. For each sample, 2 µg of total RNA was then used in Illumina's TruSeq Stranded mRNA Library kit. Analysis of ATAC-seq data was performed as follows. Reads were aligned to the mouse genome (mm10) using the BWA algorithm. Duplicate reads were removed, only reads mapping as matched pairs and only uniquely mapped reads (mapping quality ≥1) were used for further analysis. Alignments were extended in silico at their 3'-ends to a length of 200 bp and assigned to 32-nt bins along the genome. The resulting histograms (genomic “signal maps”) were stored in bigWig files. Peaks were identified using the MACS 2.1.0 algorithm at a cutoff of p-value 1e-7, without control file, and with the –nomodel option. Peaks that were on the ENCODE blacklist of known false ChIP-Seq peaks were removed. Signal maps and peak locations were used as input data to Active Motifs proprietary analysis program, which creates Excel tables containing detailed information on sample comparison, peak metrics, peak locations and gene annotations. A peak re-calling strategy was used to reduce false positives. Analysis of RNA-seq: libraries were sequenced on Illumina NextSeq 500 as paired-end 42-nt reads. Sequence reads were analyzed with the STAR alignment – DESeq2, edgeR, limma-voom software pipelines. Analysis of CHIP-seq: reads were aligned to the mouse genome (mm10) using the BWA algorithm. Duplicate reads were removed. The resulting histograms (genomic “signal maps”) were stored in bigWig files. Peaks were identified using the MACS 2.1.0 algorithm.