ATAC-seq raw data files and analyses from Ink4a.1 and Met38 mouse pancreatic cancer cell lines

Cells were harvested and frozen in culture media containing 5% DMSO. Frozen cells were sent to Active Motif to perform the ATAC-seq assay. The cells were then thawed in a 37oC water bath, pelleted, washed with cold PBS, and fragmented as previously described (Buenrostro et al., 2013) with some modifications (Corces et al., 2017). Briefly, cell pellets were resuspended in lysis buffer, pelleted, and tagmented using the enzyme and buffer provided in the Nextera Library Prep Kit (Illumina). Tagmented DNA was then purified using the MinElute PCR purification kit (Qiagen), amplified with 10 cycles of PCR, and purified. Resulting material was quantified using the KAPA Library Quantification Kit for Illumina platforms (KAPA Biosystems), and sequenced with PE42 sequencing on the NextSeq 500 sequencer (Illumina). Analysis of ATAC-seq data was very similar to the analysis of ChIP-Seq data. Reads were aligned to the human genome (hg38) using the BWA algorithm (MEME mode; default settings). 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. Data tracks were loaded on the Integrated Genome Browser (Bioviz.org) to visualize chromatin open peaks.

细胞收获后，置于含5%二甲基亚砜（DMSO）的培养基中冻存。将冻存细胞送至Active Motif进行转座酶可及性测序（ATAC-seq）实验。随后将细胞于37℃水浴中解冻，离心收集沉淀，用预冷的磷酸盐缓冲液（PBS）洗涤，并参照已发表方案（Buenrostro等人，2013）并结合部分优化步骤（Corces等人，2017）进行片段化处理。简要而言，将细胞沉淀重悬于裂解缓冲液中，离心收集后，使用Illumina公司的Nextera文库制备试剂盒提供的酶与缓冲液进行转座标记。随后使用Qiagen公司的MinElute PCR纯化试剂盒对转座标记后的DNA进行纯化，经10个循环的PCR扩增后再次纯化。所得产物采用KAPA Biosystems公司的Illumina平台适配KAPA文库定量试剂盒进行定量，随后在Illumina NextSeq 500测序仪上采用PE42测序模式进行测序。ATAC-seq数据分析流程与染色质免疫共沉淀测序（ChIP-seq）数据分析流程高度相似。使用BWA算法的MEME模式（默认参数）将测序读段比对至人类参考基因组hg38。去除重复读段，仅保留成对匹配的读段及唯一比对读段（比对质量值≥1）用于后续分析。将比对得到的序列在3'端进行计算机模拟延伸至200 bp，并沿基因组划分为32 nt的基因组窗口。所得的直方图（基因组"signal maps"）存储为bigWig格式文件。使用MACS 2.1.0算法鉴定染色质开放峰，设置p值阈值为1e-7，不使用对照文件，并启用–nomodel参数。移除ENCODE黑名单中已报道的假阳性ChIP-seq峰。将信号图谱与峰位置作为输入数据导入Active Motif的专有分析程序，该程序可生成包含样本比较、峰统计量、峰位置及基因注释等详细信息的Excel表格。将数据轨道加载至整合基因组浏览器（Integrated Genome Browser，Bioviz.org）以可视化染色质开放峰。