ReddRadar
Agent skill
bio-data-visualization-genome-browser-tracks
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-data-visualization-genome-browser-tracks
SKILL.md
name: bio-data-visualization-genome-browser-tracks description: Generate genome browser visualizations using pyGenomeTracks or IGV batch scripting for publication figures. Use when creating publication figures of genomic regions with multiple data tracks. tool_type: mixed primary_tool: pyGenomeTracks measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Genome Browser Tracks
pyGenomeTracks INI Configuration
[x-axis]
where = top
[bigwig_coverage]
file = sample.bw
title = Coverage
height = 3
color = #4DBBD5
min_value = 0
max_value = auto
[spacer]
height = 0.5
[peaks]
file = peaks.bed
title = Peaks
color = #E64B35
height = 1
display = collapsed
[genes]
file = genes.gtf
title = Genes
height = 5
fontsize = 10
style = UCSC
color = navy
pyGenomeTracks Command
# Generate track plot
pyGenomeTracks --tracks tracks.ini --region chr1:1000000-2000000 \
--outFileName region.png --dpi 300
# Multiple regions
for region in chr1:1000000-2000000 chr2:5000000-6000000; do
pyGenomeTracks --tracks tracks.ini --region $region \
--outFileName "${region//:/_}.png" --dpi 300
done
pyGenomeTracks Python API
import pygenometracks.tracks as pygtk
from pygenometracks import plotTracks
# Programmatic track configuration
tracks = '''
[x-axis]
where = top
[bigwig]
file = coverage.bw
title = ChIP-seq
height = 4
color = #4DBBD5
[bed]
file = peaks.narrowPeak
title = Peaks
height = 1
color = #E64B35
'''
# Write config and plot
with open('tracks.ini', 'w') as f:
f.write(tracks)
# Using command line via subprocess
import subprocess
subprocess.run([
'pyGenomeTracks',
'--tracks', 'tracks.ini',
'--region', 'chr1:1000000-2000000',
'--outFileName', 'output.png',
'--dpi', '300'
])
Track Types
bigWig Coverage
[bigwig]
file = signal.bw
title = Coverage
height = 4
color = #4DBBD5
min_value = 0
max_value = auto
number_of_bins = 700
nans_to_zeros = true
summary_method = mean
# overlay_previous = share-y # For overlaying multiple tracks
BED/narrowPeak
[bed]
file = peaks.narrowPeak
title = Peaks
height = 2
color = #E64B35
display = collapsed # or stacked, interleaved, triangles
labels = false
# file_type = bed # auto-detected usually
[bed_links]
file = interactions.bedpe
title = Loops
height = 3
file_type = links
links_type = arcs
color = purple
line_width = 1
Gene Annotations
[genes]
file = genes.gtf
title = Genes
height = 6
fontsize = 10
style = UCSC # or flybase
prefered_name = gene_name
merge_transcripts = false
color = navy
border_color = black
# arrow_interval = 2 # Arrow frequency
[genes_bed12]
file = genes.bed12
title = Transcripts
height = 5
fontsize = 8
color = darkblue
Hi-C Matrix
[hic_matrix]
file = matrix.cool
title = Hi-C
height = 10
depth = 1000000
min_value = 0
max_value = 100
transform = log1p
colormap = RdYlBu_r
show_masked_bins = false
IGV Batch Scripting
# Create batch script
cat > igv_batch.txt << 'EOF'
new
genome hg38
load sample1.bam
load peaks.bed
snapshotDirectory ./snapshots
goto chr1:1000000-2000000
snapshot region1.png
goto chr2:5000000-6000000
snapshot region2.png
exit
EOF
# Run IGV in batch mode
igv -b igv_batch.txt
IGV Batch Commands
# Common IGV batch commands
new # New session
genome hg38 # Load genome
load file.bam # Load track
snapshotDirectory ./out # Set output dir
goto chr1:1000000-2000000 # Navigate to region
sort base # Sort reads
collapse # Collapse tracks
expand # Expand tracks
squish # Squish display
maxPanelHeight 500 # Set panel height
snapshot file.png # Take screenshot
exit # Exit IGV
Gviz (R)
library(Gviz)
library(GenomicRanges)
# Axis track
axTrack <- GenomeAxisTrack()
# Gene track from TxDb
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
grTrack <- GeneRegionTrack(txdb, chromosome = 'chr1', name = 'Genes')
# Data track from BigWig
dTrack <- DataTrack(range = 'coverage.bw', type = 'h',
chromosome = 'chr1', name = 'Coverage',
col = '#4DBBD5')
# Annotation track from BED
aTrack <- AnnotationTrack(range = 'peaks.bed', name = 'Peaks',
chromosome = 'chr1', fill = '#E64B35')
# Plot tracks
plotTracks(list(axTrack, dTrack, aTrack, grTrack),
from = 1000000, to = 2000000,
chromosome = 'chr1')
# Save to PDF
pdf('tracks.pdf', width = 10, height = 6)
plotTracks(list(axTrack, dTrack, aTrack, grTrack),
from = 1000000, to = 2000000)
dev.off()
Multi-Sample Comparison
# tracks.ini for multiple samples
[x-axis]
[sample1_bw]
file = sample1.bw
title = Sample 1
height = 3
color = #4DBBD5
min_value = 0
max_value = 100
[sample2_bw]
file = sample2.bw
title = Sample 2
height = 3
color = #E64B35
min_value = 0
max_value = 100
overlay_previous = share-y
[spacer]
height = 0.3
[sample1_peaks]
file = sample1_peaks.bed
title = S1 Peaks
height = 1
color = #4DBBD5
[sample2_peaks]
file = sample2_peaks.bed
title = S2 Peaks
height = 1
color = #E64B35
Publication Export
# High resolution PNG
pyGenomeTracks --tracks tracks.ini --region chr1:1-1000000 \
--outFileName figure.png --dpi 300 --width 40
# PDF for vector graphics
pyGenomeTracks --tracks tracks.ini --region chr1:1-1000000 \
--outFileName figure.pdf --width 40
# SVG for editing
pyGenomeTracks --tracks tracks.ini --region chr1:1-1000000 \
--outFileName figure.svg --width 40
Related Skills
- alignment-files/bam-statistics - Input BAM processing
- chip-seq/peak-calling - Peak files for tracks
- hi-c-analysis/matrix-operations - Hi-C visualization
- data-visualization/multipanel-figures - Combining track figures
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
FreedomIntelligence/OpenClaw-Medical-Skills
vcf-annotator
Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.
FreedomIntelligence/OpenClaw-Medical-Skills
chemist-analyst
Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.
FreedomIntelligence/OpenClaw-Medical-Skills
bio-alignment-io
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
FreedomIntelligence/OpenClaw-Medical-Skills
sleep-analyzer
分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。
FreedomIntelligence/OpenClaw-Medical-Skills
metabolomics-workbench-database
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
FreedomIntelligence/OpenClaw-Medical-Skills
bio-hi-c-analysis-matrix-operations
Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.
Didn't find tool you were looking for?