Mouse RNA-seq Expression Matrix and Differential Analysis

Weichao Chen published processed RNA-seq data from mice on figshare in April 2026. The dataset includes a gene expression matrix and results from differential expression analysis. It was generated using a workflow involving HISAT2, StringTie, and DESeq2.

Use Cases

• Identify differentially expressed genes between sample groups using the DESeq2 results in the 'U_vs_V.gene_DE.xls' file.
• Perform gene co-expression network analysis using the expression values in the 'gene_expression.xls' matrix with tools like WGCNA.
• Validate novel transcription regions identified by GffCompare against known gene models from the assembly data.
• Analyze alternative splicing patterns using the outputs generated by the ASprofier tool mentioned in the workflow.

Strengths

• Includes both raw expression data and processed differential expression results.
• Analysis workflow is documented with specific tools like FastQC, HISAT2, StringTie, and DESeq2.
• Data is published under a permissive CC0 1.0 public domain license.

Limitations

• Dataset is small at 2.0 MB, suggesting limited gene or sample coverage.
• Specific row counts, sample sizes, and mouse strains are unknown.
• Data is processed; raw sequencing reads are not included.

Provenance

Source

Weichao Chen via figshare.

Collection Method

RNA-seq data processed from mouse samples using a documented bioinformatics pipeline.

Freshness

Last updated April 2026.