Bulk RNA-seq: Hepa1-6 normal control vs. acvr2a KO

OverviewThis dataset comprises Bulk RNA-seq data from mouse models used in our study to investigate the role of ACVR2A attenuation in hepatocellular carcinoma (HCC). The data provides insight into transcriptional changes associated with lactate metabolism, immune cell recruitment, and response to immunotherapy.Data Generation ProceduresSample Preparation: Mouse hepatocellular carcinoma (HCC) cells were isolated and cultured under controlled conditions.RNA Extraction: Total RNA was extracted using the TRIzol method and purified with the RNA Clean & Concentrator Kit (Zymo Research).Library Preparation: RNA libraries were prepared using the NEBNext Ultra RNA Library Prep Kit (New England Biolabs) following the manufacturer’s protocol.Sequencing Platform: Libraries were sequenced on an Illumina NovaSeq 6000 system in paired-end mode (150 bp reads).Data ProcessingQuality Control: Raw sequencing reads were checked using FastQC (v0.11.9) and filtered to remove low-quality reads.Alignment: Reads were aligned to the mouse genome (GRCm39) using HISAT2 (v2.2.1).Gene Quantification: Gene expression levels were quantified using featureCounts (v2.0.1).Normalization: Data was normalized using DESeq2 (v1.34.0) in R for differential expression analysis.Data Structure and FormatRaw Data: FASTQ files (paired-end)Aligned Data: BAM files (with per-base quality scores)Processed Data: Normalized count tables (CSV format)Metadata: Includes sample conditions, experimental details, and QC reportsData Interpretation and UseThis dataset can be used to examine gene expression changes in ACVR2A-deficient mouse HCC models.Potential applications include differential expression analysis, pathway enrichment, and immune cell infiltration studies.Users should refer to the accompanying metadata for detailed sample annotations.Missing Data and LimitationsThere are no missing reads in the sequencing data; however, some low-expression genes may be filtered out during normalization.Batch effects were minimized using Combat-seq batch correction.File Formats and CompatibilityFASTQ files: Can be processed using FASTQC, HISAT2, STAR, or Salmon.BAM files: Compatible with IGV, Samtools, or HTSeq.CSV count files: Can be used in R (DESeq2, EdgeR), Python (Scanpy), or Excel.For further details, please refer to the accompanying README file or contact the corresponding author.