deeptools

Name: deeptools
Author: LeonChaoX

698stars61forks

How to install deeptools?

leonchaox/qinyan-academic-skills/deeptools

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill deeptools

Installs into the current project.

Summary

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

Files · 1

View on GitHub

SKILL.md

1	# deepTools: NGS Data Analysis Toolkit
2
3	## Overview
4
5	deepTools is a comprehensive suite of Python command-line tools designed for processing and analyzing high-throughput sequencing data. Use deepTools to perform quality control, normalize data, compare samples, and generate publication-quality visualizations for ChIP-seq, RNA-seq, ATAC-seq, MNase-seq, and other NGS experiments.
6
7	Core capabilities:
8	- Convert BAM alignments to normalized coverage tracks (bigWig/bedGraph)
9	- Quality control assessment (fingerprint, correlation, coverage)
10	- Sample comparison and correlation analysis
11	- Heatmap and profile plot generation around genomic features
12	- Enrichment analysis and peak region visualization
13
14	## When to Use This Skill
15
16	This skill should be used when:
17
18	- File conversion: "Convert BAM to bigWig", "generate coverage tracks", "normalize ChIP-seq data"
19	- Quality control: "check ChIP quality", "compare replicates", "assess sequencing depth", "QC analysis"
20	- Visualization: "create heatmap around TSS", "plot ChIP signal", "visualize enrichment", "generate profile plot"
21	- Sample comparison: "compare treatment vs control", "correlate samples", "PCA analysis"
22	- Analysis workflows: "analyze ChIP-seq data", "RNA-seq coverage", "ATAC-seq analysis", "complete workflow"
23	- Working with specific file types: BAM files, bigWig files, BED region files in genomics context
24
25	## Quick Start
26
27	For users new to deepTools, start with file validation and common workflows:
28
29	### 1. Validate Input Files
30
31	Before running any analysis, validate BAM, bigWig, and BED files using the validation script:
32
33	```bash
34	python scripts/validate_files.py --bam sample1.bam sample2.bam --bed regions.bed
35	```
36
37	This checks file existence, BAM indices, and format correctness.
38
39	### 2. Generate Workflow Template
40
41	For standard analyses, use the workflow generator to create customized scripts:
42
43	```bash
44	# List available workflows
45	python scripts/workflow_generator.py --list
46
47	# Generate ChIP-seq QC workflow
48	python scripts/workflow_generator.py chipseq_qc -o qc_workflow.sh \
49	--input-bam Input.bam --chip-bams "ChIP1.bam ChIP2.bam" \
50	--genome-size 2913022398
51
52	# Make executable and run
53	chmod +x qc_workflow.sh
54	./qc_workflow.sh
55	```
56
57	### 3. Most Common Operations
58
59	See `assets/quick_reference.md` for frequently used commands and parameters.
60
61	## Installation
62
63	```bash
64	uv pip install deeptools
65	```
66
67	## Core Workflows
68
69	deepTools workflows typically follow this pattern: QC → Normalization → Comparison/Visualization
70
71	### ChIP-seq Quality Control Workflow
72
73	When users request ChIP-seq QC or quality assessment:
74
75	1. Generate workflow script using `scripts/workflow_generator.py chipseq_qc`
76	2. Key QC steps:
77	- Sample correlation (multiBamSummary + plotCorrelation)
78	- PCA analysis (plotPCA)
79	- Coverage assessment (plotCoverage)
80	- Fragment size validation (bamPEFragmentSize)
81	- ChIP enrichment strength (plotFingerprint)
82
83	Interpreting results:
84	- Correlation: Replicates should cluster together with high correlation (>0.9)
85	- Fingerprint: Strong ChIP shows steep rise; flat diagonal indicates poor enrichment
86	- Coverage: Assess if sequencing depth is adequate for analysis
87
88	Full workflow details in `references/workflows.md` → "ChIP-seq Quality Control Workflow"
89
90	### ChIP-seq Complete Analysis Workflow
91
92	For full ChIP-seq analysis from BAM to visualizations:
93
94	1. Generate coverage tracks with normalization (bamCoverage)
95	2. Create comparison tracks (bamCompare for log2 ratio)
96	3. Compute signal matrices around features (computeMatrix)
97	4. Generate visualizations (plotHeatmap, plotProfile)
98	5. Enrichment analysis at peaks (plotEnrichment)
99
100	Use `scripts/workflow_generator.py chipseq_analysis` to generate template.
101
102	Complete command sequences in `references/workflows.md` → "ChIP-seq Analysis Workflow"
103
104	### RNA-seq Coverage Workflow
105
106	For strand-specific RNA-seq coverage tracks:
107
108	Use bamCoverage with `--filterRNAstrand` to separate forward and reverse strands.
109
110	Important: NEVER use `--extendReads` for RNA-seq (would extend over splice junctions).
111
112	Use normalization: CPM for fixed bins, RPKM for gene-level analysis.
113
114	Template available: `scripts/workflow_generator.py rnaseq_coverage`
115
116	Details in `references/workflows.md` → "RNA-seq Coverage Workflow"
117
118	### ATAC-seq Analysis Workflow
119
120	ATAC-seq requires Tn5 offset correction:
121
122	1. Shift reads using alignmentSieve with `--ATACshift`
123	2. Generate coverage with bamCoverage
124	3. Analyze fragment sizes (expect nucleosome ladder pattern)
125	4. Visualize at peaks if available
126
127	Template: `scripts/workflow_generator.py atacseq`
128
129	Full workflow in `references/workflows.md` → "ATAC-seq Workflow"
130
131	## Tool Categor

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deeptools

698stars61forks

View on GitHub

How to install deeptools?

leonchaox/qinyan-academic-skills/deeptools

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill deeptools

Installs into the current project.

Summary

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

Files · 1

View on GitHub

SKILL.md

1	# deepTools: NGS Data Analysis Toolkit
2
3	## Overview
4
5	deepTools is a comprehensive suite of Python command-line tools designed for processing and analyzing high-throughput sequencing data. Use deepTools to perform quality control, normalize data, compare samples, and generate publication-quality visualizations for ChIP-seq, RNA-seq, ATAC-seq, MNase-seq, and other NGS experiments.
6
7	Core capabilities:
8	- Convert BAM alignments to normalized coverage tracks (bigWig/bedGraph)
9	- Quality control assessment (fingerprint, correlation, coverage)
10	- Sample comparison and correlation analysis
11	- Heatmap and profile plot generation around genomic features
12	- Enrichment analysis and peak region visualization
13
14	## When to Use This Skill
15
16	This skill should be used when:
17
18	- File conversion: "Convert BAM to bigWig", "generate coverage tracks", "normalize ChIP-seq data"
19	- Quality control: "check ChIP quality", "compare replicates", "assess sequencing depth", "QC analysis"
20	- Visualization: "create heatmap around TSS", "plot ChIP signal", "visualize enrichment", "generate profile plot"
21	- Sample comparison: "compare treatment vs control", "correlate samples", "PCA analysis"
22	- Analysis workflows: "analyze ChIP-seq data", "RNA-seq coverage", "ATAC-seq analysis", "complete workflow"
23	- Working with specific file types: BAM files, bigWig files, BED region files in genomics context
24
25	## Quick Start
26
27	For users new to deepTools, start with file validation and common workflows:
28
29	### 1. Validate Input Files
30
31	Before running any analysis, validate BAM, bigWig, and BED files using the validation script:
32
33	```bash
34	python scripts/validate_files.py --bam sample1.bam sample2.bam --bed regions.bed
35	```
36
37	This checks file existence, BAM indices, and format correctness.
38
39	### 2. Generate Workflow Template
40
41	For standard analyses, use the workflow generator to create customized scripts:
42
43	```bash
44	# List available workflows
45	python scripts/workflow_generator.py --list
46
47	# Generate ChIP-seq QC workflow
48	python scripts/workflow_generator.py chipseq_qc -o qc_workflow.sh \
49	--input-bam Input.bam --chip-bams "ChIP1.bam ChIP2.bam" \
50	--genome-size 2913022398
51
52	# Make executable and run
53	chmod +x qc_workflow.sh
54	./qc_workflow.sh
55	```
56
57	### 3. Most Common Operations
58
59	See `assets/quick_reference.md` for frequently used commands and parameters.
60
61	## Installation
62
63	```bash
64	uv pip install deeptools
65	```
66
67	## Core Workflows
68
69	deepTools workflows typically follow this pattern: QC → Normalization → Comparison/Visualization
70
71	### ChIP-seq Quality Control Workflow
72
73	When users request ChIP-seq QC or quality assessment:
74
75	1. Generate workflow script using `scripts/workflow_generator.py chipseq_qc`
76	2. Key QC steps:
77	- Sample correlation (multiBamSummary + plotCorrelation)
78	- PCA analysis (plotPCA)
79	- Coverage assessment (plotCoverage)
80	- Fragment size validation (bamPEFragmentSize)
81	- ChIP enrichment strength (plotFingerprint)
82
83	Interpreting results:
84	- Correlation: Replicates should cluster together with high correlation (>0.9)
85	- Fingerprint: Strong ChIP shows steep rise; flat diagonal indicates poor enrichment
86	- Coverage: Assess if sequencing depth is adequate for analysis
87
88	Full workflow details in `references/workflows.md` → "ChIP-seq Quality Control Workflow"
89
90	### ChIP-seq Complete Analysis Workflow
91
92	For full ChIP-seq analysis from BAM to visualizations:
93
94	1. Generate coverage tracks with normalization (bamCoverage)
95	2. Create comparison tracks (bamCompare for log2 ratio)
96	3. Compute signal matrices around features (computeMatrix)
97	4. Generate visualizations (plotHeatmap, plotProfile)
98	5. Enrichment analysis at peaks (plotEnrichment)
99
100	Use `scripts/workflow_generator.py chipseq_analysis` to generate template.
101
102	Complete command sequences in `references/workflows.md` → "ChIP-seq Analysis Workflow"
103
104	### RNA-seq Coverage Workflow
105
106	For strand-specific RNA-seq coverage tracks:
107
108	Use bamCoverage with `--filterRNAstrand` to separate forward and reverse strands.
109
110	Important: NEVER use `--extendReads` for RNA-seq (would extend over splice junctions).
111
112	Use normalization: CPM for fixed bins, RPKM for gene-level analysis.
113
114	Template available: `scripts/workflow_generator.py rnaseq_coverage`
115
116	Details in `references/workflows.md` → "RNA-seq Coverage Workflow"
117
118	### ATAC-seq Analysis Workflow
119
120	ATAC-seq requires Tn5 offset correction:
121
122	1. Shift reads using alignmentSieve with `--ATACshift`
123	2. Generate coverage with bamCoverage
124	3. Analyze fragment sizes (expect nucleosome ladder pattern)
125	4. Visualize at peaks if available
126
127	Template: `scripts/workflow_generator.py atacseq`
128
129	Full workflow in `references/workflows.md` → "ATAC-seq Workflow"
130
131	## Tool Categor

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6 picks

Type