bulk-rnaseq

Name: bulk-rnaseq
Author: K-Dense-AI

398installs3.1kforks

How to install bulk-rnaseq?

k-dense-ai/scientific-agent-skills/bulk-rnaseq

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq

Installs into the current project.

Summary

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA e

Files · 1

View on GitHub

SKILL.md

1	# Bulk RNA-seq
2
3	## Overview
4
5	This skill orchestrates a complete, defensible bulk RNA-seq differential-expression study, from raw sequencing reads to enriched pathways and figures. It is a router, not a reimplementation: most stages already have dedicated skills in this repo, and this skill connects them in the right order, fills the one real gap (raw reads → a gene-level counts matrix), and enforces the design and QC decisions that determine whether the final result is trustworthy.
6
7	"Defensible" means three things, applied throughout:
8	- Reproducible — pinned pipeline/tool versions, containers where possible, recorded parameters, fixed random seeds.
9	- Quality-gated — QC is inspected and acted on before, during, and after quantification, not skipped.
10	- Statistically sound — adequate replication, a design that matches the biology, counts handled correctly, and FDR-controlled testing.
11
12	The pipeline is: FastQC/trim → align/quant (STAR/Salmon) → counts → DE (pydeseq2) → enrichment (pathway-enrichment) → figures.
13
14	## When to Use This Skill
15
16	Use this skill when the user wants to:
17	- Go from FASTQ files (or a sequencing run) to differentially expressed genes and pathways.
18	- Run or configure `nf-core/rnaseq`, or align/quantify with STAR, Salmon, or featureCounts.
19	- Turn Salmon/STAR/featureCounts output into a counts matrix ready for DESeq2/PyDESeq2.
20	- Design or sanity-check a bulk RNA-seq experiment (replicates, batch, strandedness) before committing compute.
21	- Scope an end-to-end RNA-seq analysis and decide which tools and skills to chain.
22
23	This is bulk RNA-seq (samples = biological specimens). For single-cell/nuclei data use `scanpy`; for the DE statistics alone use `pydeseq2`; for enrichment alone use `pathway-enrichment`.
24
25	## The Pipeline at a Glance
26
27	```mermaid
28	flowchart TD
29	fastq["Raw FASTQ + samplesheet"] --> qc["FastQC + MultiQC"]
30	qc --> trim["Trim: fastp / Trim Galore"]
31	trim --> align["Align + quant: STAR and/or Salmon"]
32	align --> counts["Gene-level counts matrix"]
33	counts --> de["Differential expression"]
34	de --> enrich["Pathway / GSEA enrichment"]
35	de --> fig["Figures"]
36	enrich --> fig
37	nfcore["nf-core/rnaseq via nextflow skill"] -.->\|"path A"\| align
38	manual["Standalone recipes (this skill)"] -.->\|"path B"\| align
39	bridge["build_counts_matrix.py (this skill)"] -.-> counts
40	pydeseq2skill["pydeseq2 skill"] -.-> de
41	pwskill["pathway-enrichment skill"] -.-> enrich
42	vizskill["scientific-visualization skill"] -.-> fig
43	```
44
45	## Two Upstream Paths — Pick One
46
47	The reads → counts stage can be run two ways. They produce equivalent gene counts; choose by context, then stay on that path.
48
49	\| Use Path A — `nf-core/rnaseq` when… \| Use Path B — standalone tools when… \|
50	\|------------------------------------------\|------------------------------------------\|
51	\| You want the field-standard, audited, citable pipeline with one command \| You have a few samples and want to learn/inspect each step \|
52	\| Many samples, or you'll scale to HPC/cloud \| No Nextflow/containers available, or a constrained environment \|
53	\| Reproducibility and a full MultiQC report matter most \| You need a non-standard step the pipeline doesn't expose \|
54	\| → Drive it through the `nextflow` skill \| → Follow `references/upstream-manual.md` \|
55
56	When unsure, prefer Path A: `nf-core/rnaseq` already wires together FastQC → trimming → STAR/Salmon → quantification → tximport → MultiQC with sensible, reviewed defaults, which is the most defensible option. Path B exists for transparency and constrained setups.
57
58	Both paths converge on a gene-level counts matrix, after which the workflow is identical.
59
60	## Setup
61
62	```bash
63	# This skill's glue (bridge + handoffs) — Python
64	uv pip install pytximport pandas
65
66	# Downstream skills install their own deps:
67	# pydeseq2 skill -> uv pip install pydeseq2
68	# pathway-enrichment skill -> uv pip install gseapy gprofiler-official
69
70	# Path A (nf-core): on

People who liked this also viewed

6 picks

Type

bulk-rnaseq

398installs3.1kforks

View on GitHub

How to install bulk-rnaseq?

k-dense-ai/scientific-agent-skills/bulk-rnaseq

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Bulk RNA-seq
2
3	## Overview
4
5	This skill orchestrates a complete, defensible bulk RNA-seq differential-expression study, from raw sequencing reads to enriched pathways and figures. It is a router, not a reimplementation: most stages already have dedicated skills in this repo, and this skill connects them in the right order, fills the one real gap (raw reads → a gene-level counts matrix), and enforces the design and QC decisions that determine whether the final result is trustworthy.
6
7	"Defensible" means three things, applied throughout:
8	- Reproducible — pinned pipeline/tool versions, containers where possible, recorded parameters, fixed random seeds.
9	- Quality-gated — QC is inspected and acted on before, during, and after quantification, not skipped.
10	- Statistically sound — adequate replication, a design that matches the biology, counts handled correctly, and FDR-controlled testing.
11
12	The pipeline is: FastQC/trim → align/quant (STAR/Salmon) → counts → DE (pydeseq2) → enrichment (pathway-enrichment) → figures.
13
14	## When to Use This Skill
15
16	Use this skill when the user wants to:
17	- Go from FASTQ files (or a sequencing run) to differentially expressed genes and pathways.
18	- Run or configure `nf-core/rnaseq`, or align/quantify with STAR, Salmon, or featureCounts.
19	- Turn Salmon/STAR/featureCounts output into a counts matrix ready for DESeq2/PyDESeq2.
20	- Design or sanity-check a bulk RNA-seq experiment (replicates, batch, strandedness) before committing compute.
21	- Scope an end-to-end RNA-seq analysis and decide which tools and skills to chain.
22
23	This is bulk RNA-seq (samples = biological specimens). For single-cell/nuclei data use `scanpy`; for the DE statistics alone use `pydeseq2`; for enrichment alone use `pathway-enrichment`.
24
25	## The Pipeline at a Glance
26
27	```mermaid
28	flowchart TD
29	fastq["Raw FASTQ + samplesheet"] --> qc["FastQC + MultiQC"]
30	qc --> trim["Trim: fastp / Trim Galore"]
31	trim --> align["Align + quant: STAR and/or Salmon"]
32	align --> counts["Gene-level counts matrix"]
33	counts --> de["Differential expression"]
34	de --> enrich["Pathway / GSEA enrichment"]
35	de --> fig["Figures"]
36	enrich --> fig
37	nfcore["nf-core/rnaseq via nextflow skill"] -.->\|"path A"\| align
38	manual["Standalone recipes (this skill)"] -.->\|"path B"\| align
39	bridge["build_counts_matrix.py (this skill)"] -.-> counts
40	pydeseq2skill["pydeseq2 skill"] -.-> de
41	pwskill["pathway-enrichment skill"] -.-> enrich
42	vizskill["scientific-visualization skill"] -.-> fig
43	```
44
45	## Two Upstream Paths — Pick One
46
47	The reads → counts stage can be run two ways. They produce equivalent gene counts; choose by context, then stay on that path.
48
49	\| Use Path A — `nf-core/rnaseq` when… \| Use Path B — standalone tools when… \|
50	\|------------------------------------------\|------------------------------------------\|
51	\| You want the field-standard, audited, citable pipeline with one command \| You have a few samples and want to learn/inspect each step \|
52	\| Many samples, or you'll scale to HPC/cloud \| No Nextflow/containers available, or a constrained environment \|
53	\| Reproducibility and a full MultiQC report matter most \| You need a non-standard step the pipeline doesn't expose \|
54	\| → Drive it through the `nextflow` skill \| → Follow `references/upstream-manual.md` \|
55
56	When unsure, prefer Path A: `nf-core/rnaseq` already wires together FastQC → trimming → STAR/Salmon → quantification → tximport → MultiQC with sensible, reviewed defaults, which is the most defensible option. Path B exists for transparency and constrained setups.
57
58	Both paths converge on a gene-level counts matrix, after which the workflow is identical.
59
60	## Setup
61
62	```bash
63	# This skill's glue (bridge + handoffs) — Python
64	uv pip install pytximport pandas
65
66	# Downstream skills install their own deps:
67	# pydeseq2 skill -> uv pip install pydeseq2
68	# pathway-enrichment skill -> uv pip install gseapy gprofiler-official
69
70	# Path A (nf-core): on

People who liked this also viewed

6 picks

Type