Orchestrate bulk RNA-seq analysis from FASTQ reads to differential expression and figures.
npx skills add https://github.com/dralkh/seerai --skill bulk-rnaseq
Please help me install this Agent Skill. Skill: bulk-rnaseq Source: https://github.com/dralkh/seerai/tree/main/skills/bulk-rnaseq Command: npx skills add https://github.com/dralkh/seerai --skill bulk-rnaseq
Bulk RNA-seq studies often break down when analysis steps are scattered, counts are assembled incorrectly, or experimental design and quality control are skipped. This Skill gives you a defensible path from raw reads or quantification output to differential expression, enrichment, and publication-ready figures.
Use the bulk-rnaseq skill to validate your samplesheet and metadata, then choose the nf-core or standalone path to take your RNA-seq data from FASTQ files or quantification output through counts, differential expression, enrichment, and figures.
High-intent search queries and answers about installing and using this skill.
To build a gene-level counts matrix from STAR or Salmon output, you can use pytximport to assemble transcript-level quantifications into integer gene-by-sample matrices aligned with your metadata for differential expression analysis.
Yes, you can run differential expression from nf-core/rnaseq output by routing the pipeline results to build PyDESeq2-compatible count matrices and proceed to downstream enrichment and figure generation.
Validating bulk RNA-seq samplesheets involves checking metadata for replicate consistency, strandedness, and batch confounding before expensive analysis to ensure a defensible experimental design.
The best way to prepare RNA-seq counts for PyDESeq2 is assembling integer gene-by-sample count matrices with proper metadata alignment, ensuring the output format is fully compatible with downstream differential expression tools.
Yes, you need pandas and pytximport to assemble bulk transcriptomics counts matrices, as these dependencies handle the data manipulation and transcript-to-gene aggregation required for accurate counts.
Bulk RNA-seq differential expression workflows break down when counts are assembled incorrectly, experimental design skips quality control, or metadata alignment fails, making a structured end-to-end pipeline necessary.
jasrajtulsiBuild a bulk RNA-seq pipeline from raw reads to differential expression and pathway enrichment.
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