Claude-skill-registry track-generation

This skill generates normalized BigWig (.bw) tracks (and/or fold-change tracks) from BAM files for ATAC-seq and ChIP-seq visualization. It handles normalization (RPM or fold-change) and Tn5 offset correction automatically. What's more, this skill can help user visualize the signal profiles around TSS or target regions. Use this skill when you have filtered and generated the clean BAM file (e.g. `*.filtered.bam`).

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/6-track-generation" ~/.claude/skills/majiayu000-claude-skill-registry-track-generation && rm -rf "$T"

manifest: skills/data/6-track-generation/SKILL.md

Overview

This skill converts filtered BAM files into normalized signal tracks (BigWig) for genome browser visualization.
It supports both ATAC-seq and ChIP-seq datasets, automatically detecting genome assembly and chromosome size files.

Main steps include:

Refer to the Inputs & Outputs section to check inputs and build the output architecture. All the output file should located in
```
${proj_dir}
```
in Step 0.
Always use filtered BAM file (
```
*.filtered.bam
```
) if available.
Normalize all tracks to 1 million mapped reads (RPM normalization).
Generate the chrom.size file.
For ATAC-seq, apply Tn5 offset correction (+4/−5) and generate normalized BigWig (RPM).
For ChIP-seq, generat RPM-normalized track without applying Tn5 offset correction
Always prompt user for whether need to visualize the signal profiles around TSS or target regions.
Visualize the signal profiles around TSS or target regions if users require.

Decision Tree

Step 0: Initialize Project

Call:

```
mcp__project-init-tools__project_init
```

with:

```
sample
```
: all
```
task
```
: track_generation

The tool will:

Create
```
${sample}_track_generation
```
directory.
Return the full path of the
```
${sample}_track_generation
```
directory, which will be used as
```
${proj_dir}
```
.

Step 1: Generate Chromosome size file

Call:

```
mcp__bw-tools__generate_chrom_sizes
```
with:
```
bam_file
```
: Path for the BAM file for generating bigWig Tracks
```
output_path
```
: ${proj_dir}/temp/${sample}.chrom.sizes

Step 2: Calculate Scaling Factor

Call:

```
mcp__bw_tools__calculate_scaling_factor
```
with:
```
bam_file
```
: Path for the BAM file for generating bigWig Tracks

This step will store result as variable ${scale_factor}

Step 3: Create RPM-normalized BigWig scaled to 1M mapped reads.

(Option 1) For ATAC-seq data: Apply the standard Tn5 shift (+4/-5bp)

Call:

```
mcp__bw_tools__bam_to_bigwig
```
with:
```
bam_file
```
: ${bam_file}
```
chrom_sizes
```
: ${proj_dir}/temp/${sample}.chrom.sizes (from Step 2)
```
output_bw
```
: ${proj_dir}/tracks/${sample_name}.RPM.bw
```
scale_factor
```
: ${scale_factor}
```
shift_tn5
```
: True
```
temp_dir
```
: ${proj_dir}/temp
(Option 2) For ChIP-seq data: Do Not Apply the standard Tn5 shift by setting
```
shift_tn5
```
as False

Step 3: Visualize the signal profiles around TSS or target region (Optional)

Call:

```
mcp__bw_tools__visualize_signal_profile
```
with:
```
regions_bed
```
: GTF (for gene tss) or BED file (for target regions), always query user for this file if not provided.
```
signal_files
```
: Input BigWig signal files.
```
output_prefix
```
: Output prefix for matrix/plots.
```
reference_point
```
: use
```
TSS
```
for genes, and
```
center
```
for target regions.
```
upstream
```
: Upstream distance (bp).
```
downstream
```
: Downstream distance (bp).