Automate bulk RNA-seq analysis with STAR or Salmon and DESeq2.
npx skills add https://github.com/awslabs/hcls-agent-skills --skill rna-seq-analysis
Please help me install this Agent Skill. Skill: rna-seq-analysis Source: https://github.com/awslabs/hcls-agent-skills/tree/main/skills/rna-seq-analysis Command: npx skills add https://github.com/awslabs/hcls-agent-skills --skill rna-seq-analysis
This skill addresses the common pitfalls in RNA-seq analysis, such as incorrect strandedness determination, improper normalization, and flawed statistical modeling, which often lead to inaccurate differential expression results.
Invoke the rna-seq-analysis skill to process your raw count matrix and sample metadata to generate a differential expression report using DESeq2.
High-intent search queries and answers about installing and using this skill.
Differential expression analysis with DESeq2 requires a raw integer count matrix and sample metadata to generate a robust report. This skill automates the statistical modeling, ensuring correct normalization and handling of batch effects for accurate results.
Choose Salmon for alignment-free quantification or STAR for alignment-based quantification based on your pipeline needs. This skill provides decision logic to help select the appropriate method while ensuring validated strandedness parameters are applied.
Inaccurate RNA-seq results often stem from incorrect strandedness determination, improper normalization, or flawed statistical modeling. This skill addresses these common pitfalls by guiding correct DESeq2 model setup and enforcing validated strandedness parameters.
Validated strandedness parameters are required for bulk RNA-seq pipeline automation to maintain biological accuracy. This skill ensures these parameters are correctly applied during alignment and quantification to prevent inaccurate differential expression outcomes.
Yes, PyDESeq2 and pandas are used together for transcriptomics differential expression testing. This skill leverages these dependencies to automate DESeq2 modeling, including LFC shrinkage, from raw integer count inputs.
The best way to handle batch effects in bulk RNA-seq DESeq2 modeling is through proper statistical model setup. This skill guides correct batch effect handling and LFC shrinkage to ensure statistical rigor in your differential expression analysis.
omicverseConverts bulk RNA-seq FASTQ or SRA inputs into gene-level count matrices.
tassiovaleAnalyze gene lists or ranked data for enriched biological pathways and gene sets.
pur3v4d3rQuery genomic data across 20+ databases via CLI and Python API.
ammawlaRun RNA-seq analysis from FASTQ files to expression matrices and QC reports.
