MCPInstall

bioinformatics-visualization

by delphine-lEducation & Science

Publication-quality bioinformatics figures - phylogenetic trees, genome browsers, iTOL datasets, and data presentation

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/delphine-l/claude_global --skill bioinformatics-visualization
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads bioinformatics-visualization automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Bioinformatics Visualization

iTOL Dataset Formats and Troubleshooting

Choosing the Right Dataset Type

DATASET_BINARY (Recommended for markers/symbols):

  • More reliable than DATASET_SYMBOL
  • All species must be listed with binary values (0 or 1)
  • Simpler format, better iTOL compatibility
  • Use for: presence/absence markers, technology indicators, categorical highlights

Format example:

DATASET_BINARY
SEPARATOR TAB

DATASET_LABEL	CLR Technology
COLOR	#ff0000

LEGEND_TITLE	Sequencing Technology
LEGEND_SHAPES	2
LEGEND_COLORS	#ff0000
LEGEND_LABELS	CLR (PacBio)

FIELD_SHAPES	2
FIELD_COLORS	#ff0000
FIELD_LABELS	CLR

DATA
Species_name_1	1
Species_name_2	0
Species_name_3	1

DATASET_SYMBOL (Less reliable):

  • Can be finicky about format
  • Per-species shape/size/color specifications complex
  • May not display correctly even with valid format
  • Avoid unless BINARY doesn't meet needs

DATASET_COLORSTRIP (Good for gradients):

  • Reliable for color gradients (e.g., temporal data, continuous values)
  • Only species with data need to be listed
  • Good for non-binary categorical or continuous data

Common iTOL Errors and Fixes

Error: "Unknown variable 'SYMBOL_SHAPE'"

  • Cause: Mixing global symbol settings with per-species data
  • Fix: Switch to DATASET_BINARY format

Error: "Invalid color '1' for node X"

  • Cause: DATASET_SYMBOL data format mismatch
  • Fix: Use DATASET_BINARY instead, format: species<tab>0_or_1

Symbols not appearing on tree:

  • Likely cause: DATASET_SYMBOL format issues
  • Fix: Convert to DATASET_BINARY
  • Verify: Check that all species in config exist in tree file

Species Name Compatibility

Critical: Species names must match exactly between tree and annotation files

Common issues:

  1. Case sensitivity: "Alca Torda" vs "Alca_torda"
  2. Spaces vs underscores: Always use underscores in tree format
  3. Subspecies names: Handle three-part names carefully

Fix for case sensitivity:

# Convert scientific names to tree format with case normalization
df['species_tree'] = df['scientific_name'].str.replace(' ', '_')
# Fix uppercase after underscore (Alca_Torda -> Alca_torda)
df['species_tree'] = df['species_tree'].str.replace(
    r'_([A-Z])',
    lambda m: '_' + m.group(1).lower(),
    regex=True
)

Validation pattern:

# Always validate species compatibility
import re

# Extract species from tree
with open('tree.nwk') as f:
    tree_content = f.read()
tree_species = set(re.findall(r'([A-Z][a-z]+_[a-z]+)', tree_content))

# Check config species
config_species = set(df['species_tree'])
missing = config_species - tree_species

if missing:
    print(f"Species in config but not in tree: {missing}")

Color Gradients for Temporal Data

Effective color schemes:

Temporal progression (old → new):

  • Light Yellow → Dark Red (ColorBrewer YlOrRd)
  • Clearly shows progression from past to present
  • Example: #ffffcc (2019) → #b10026 (2025)

Avoid:

  • Blue → Yellow → Red (confusing middle point)
  • Diverging palettes for sequential data

ColorBrewer palettes for sequential data:

  • YlOrRd: Yellow-Orange-Red (temporal, intensity)
  • YlGn: Yellow-Green (growth, vegetation)
  • PuBuGn: Purple-Blue-Green (water, depth)

Debugging Workflow

  1. Generate config file
  2. Upload to iTOL (https://itol.embl.de)
  3. If errors: Save error messages to file
  4. Check format: BINARY vs SYMBOL vs COLORSTRIP
  5. Validate species names: Match against tree file
  6. Test with minimal dataset: 5-10 species first
  7. Switch formats if needed: SYMBOL → BINARY usually works

Related Skills

  • data-visualization: General visualization best practices
  • bioinformatics/fundamentals: Core bioinformatics concepts
  • bioinformatics/phylogenetics: Phylogenetic analysis workflows

FAQ

What does the bioinformatics-visualization skill do?

Publication-quality bioinformatics figures - phylogenetic trees, genome browsers, iTOL datasets, and data presentation

How do I install bioinformatics-visualization?

Run npx skills add https://github.com/delphine-l/claude_global --skill bioinformatics-visualization in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/bioinformatics-visualization/ (personal) or .claude/skills/bioinformatics-visualization/ (project).

Which AI agents can use bioinformatics-visualization?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is bioinformatics-visualization safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains bioinformatics-visualization?

It is published by delphine-l in delphine-l/claude_global. It has 125 recorded installs.

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