DOI: 10.57760/sciencedb.26122
title: RNA Sequencing (KYSE450SLC8A1KO vs KYSE450WT)
title: RNA Sequencing (KYSE450SLC8A1KO vs KYSE450WT)

description: The data uploaded herein represents the transcriptomic sequencing results from the human esophageal squamous cell carcinoma cell line KYSE450, specifically comparing SLC8A1-knockout cells (KYSE450SLC8A1KO) with wild-type controls (KYSE450WT). The data generation process began with the separate cultivation of KYSE450SLC8A1KO and KYSE450WT cells until they reached a specific growth state. Qualified RNA samples were used to construct sequencing libraries, involving steps such as RNA fragmentation, cDNA synthesis, end repair, addition of A-tails, and adapter ligation. Finally, high-throughput sequencing was performed using the Illumina platform, generating a large volume of short-read sequence data.The data processing and analysis workflow started with quality control of the raw sequencing data (in .fastq format). Tools such as Fastp were employed to remove low-quality bases, adapter sequences, and ambiguous reads, thereby obtaining high-quality clean reads. Subsequently, these clean reads were aligned to the human reference genome using alignment software. After alignment, tools like featureCounts or HTSeq-count were used to count the number of reads mapping to each gene or transcript, thereby quantifying gene expression levels and generating a gene expression matrix. This matrix records the read counts (Read Count) for each gene within each sample and was ultimately organized and saved in .xls format. The uploaded data files primarily consist of this gene expression matrix, encompassing expression data from two samples (KYSE450SLC8A1KO and KYSE450WT). The data covers changes in gene expression levels across the entire genome. Temporal and spatial resolution are not applicable, as this is an in vitro cell line study. This dataset provides a foundation for studying the regulatory role of the SLC8A1 gene in esophageal squamous cell carcinoma and facilitates subsequent analyses such as differential expression analysis and pathway enrichment.
description: The data uploaded herein represents the transcriptomic sequencing results from the human esophageal squamous cell carcinoma cell line KYSE450, specifically comparing SLC8A1-knockout cells (KYSE450SLC8A1KO) with wild-type controls (KYSE450WT). The data generation process began with the separate cultivation of KYSE450SLC8A1KO and KYSE450WT cells until they reached a specific growth state. Qualified RNA samples were used to construct sequencing libraries, involving steps such as RNA fragmentation, cDNA synthesis, end repair, addition of A-tails, and adapter ligation. Finally, high-throughput sequencing was performed using the Illumina platform, generating a large volume of short-read sequence data.The data processing and analysis workflow started with quality control of the raw sequencing data (in .fastq format). Tools such as Fastp were employed to remove low-quality bases, adapter sequences, and ambiguous reads, thereby obtaining high-quality clean reads. Subsequently, these clean reads were aligned to the human reference genome using alignment software. After alignment, tools like featureCounts or HTSeq-count were used to count the number of reads mapping to each gene or transcript, thereby quantifying gene expression levels and generating a gene expression matrix. This matrix records the read counts (Read Count) for each gene within each sample and was ultimately organized and saved in .xls format. The uploaded data files primarily consist of this gene expression matrix, encompassing expression data from two samples (KYSE450SLC8A1KO and KYSE450WT). The data covers changes in gene expression levels across the entire genome. Temporal and spatial resolution are not applicable, as this is an in vitro cell line study. This dataset provides a foundation for studying the regulatory role of the SLC8A1 gene in esophageal squamous cell carcinoma and facilitates subsequent analyses such as differential expression analysis and pathway enrichment.