Objectives: Gastric cancer (GC) is the fourth most common cancer worldwide, with the highest incidence and mortality regardless of sex. Despite technological advances in diagnosing and treating gastric cancer, GC still has high incidence and mortality rates. Therefore, continuous research is needed to overcome GC. In various studies, cell lines are used to find and verify the cause of specific diseases. Large-scale genomic studies such as ENCODE and Roadmap epigenomic projects provide multiomics data from various organisms and samples. However, few multi-omics data for gastric tissues and cell lines have been generated. Therefore, we performed RNA-seq, Exome-seq, and ChIP-seq with several gastric cell lines to generate a multi-omics data set in gastric cancer.
Data description: Multiomic data, such as RNA-seq, Exome-seq, and ChIP-seq, were produced in gastric cancer and normal cell lines. RNA-seq data were generated from nine GC and one normal gastric cell line, mapped to a human reference genome (hg38) using the STAR alignment tool, and quantified with HTseq. Exome sequence data were produced in nine GC and two normal gastric lines. Sequenced reads were mapped and processed using BWA-MEM and GATK, variants were called by stralka2, and annotation was performed using ANNOVAR. Finally, for the ChIP-seq, nine GC cell lines and four GC cell lines were used in two experimental sets; chip-seq was performed to confirm changes in H3K4me3 and H3K27me3. Data was mapped to human reference hg38 with BWA-MEM, and peak calling and annotation were performed using the Homer tool. Since these data provide multi-omics data for GC cell lines, it will be useful for researchers who use the GC cell lines to study.
Keywords: ChIP-sequencing; Exome sequencing; Gastric cancer; Gastric cancer cell lines; RNA sequencing.
© 2023. The Author(s).