AgentSkillsCN

gel-annotation

使用分子量标记与泳道标签为 SDS-PAGE 凝胶图像添加注释。适用于用户要求标注、注释或分析凝胶照片时使用。通过强度分析（scikit-image）自动检测泳道与条带，依据已知蛋白梯度计算分子量，并输出高质量的 SVG 格式注释图像。

SKILL.md
--- frontmatter
name: gel-annotation
description: Annotate SDS-PAGE gel images with molecular weight markers and lane labels. Use when asked to label, annotate, or analyze gel photos. Detects lanes and bands automatically via intensity profiling (scikit-image), assigns MW from known protein ladders, and outputs SVG-quality annotated images.

Gel Annotation

Automated SDS-PAGE gel annotation: band detection, MW assignment, lane labeling.

Quick Start

bash
# Basic annotation with PageRuler 26616 ladder (default)
python3 scripts/annotate_gel.py input.png -o annotated.png

# Specify ladder and ladder lane
python3 scripts/annotate_gel.py input.png --ladder pageruler-26616 --ladder-lane 0

# Custom MW values
python3 scripts/annotate_gel.py input.png --mw 140,115,80,70,50,40,30,25,15,10

# Custom lane labels (comma-separated; use "Ladder" for the marker lane)
python3 scripts/annotate_gel.py input.png --labels "Ladder,Input,FT,Wash,Elution"

# Output SVG (vector labels, raster gel)
python3 scripts/annotate_gel.py input.png --format svg -o annotated.svg

# High-res PNG from SVG (2x default)
python3 scripts/annotate_gel.py input.png --format png --scale 2 -o annotated_hires.png

CLI Reference

Located at skills/gel-annotation/scripts/annotate_gel.py.

FlagDefaultDescription
input(required)Input gel image (PNG, JPEG, HEIC via sips conversion)
-o, --outputannotated.pngOutput file path
--ladderpageruler-26616Preset ladder name
--mw(from ladder)Comma-separated MW values (kDa), overrides ladder
--ladder-lane0Lane index for the MW ladder (0 = leftmost)
--labelsauto (Ladder,A,B,...)Comma-separated lane labels
--formatpngOutput format: png, svg
--scale2PNG render scale (via rsvg-convert)
--ref-bands70,40,25Reference bands to highlight in red
--band-sigma4Gaussian sigma for band detection smoothing
--lane-sigma8Gaussian sigma for lane detection smoothing
--min-prominence3Minimum peak prominence for band detection
--font-size16Base font size for labels
--no-linesfalseSkip dashed MW indicator lines

Supported Ladders

NameBands (kDa)Catalog
pageruler-26616140, 115, 80, 70, 50, 40, 30, 25, 15, 10Thermo 26616
pageruler-26619250, 130, 100, 70, 55, 35, 25, 15, 10Thermo 26619

Add new ladders in the LADDERS dict in the script.

Pipeline

  1. •Convert HEIC → PNG via sips (macOS) if needed
  2. •Detect lanes — vertical intensity profile aggregated across gel height, peak detection
  3. •Detect bands — per-lane horizontal intensity profile, Gaussian-smoothed peak detection
  4. •Assign MW — match detected bands in ladder lane to known MW values (count must match)
  5. •Render SVG — gel as embedded raster <image>, all labels as vector <text> elements
  6. •Export — SVG direct or PNG via rsvg-convert at specified scale

Tuning

If band detection misses bands or picks up noise:

  • •Adjust --band-sigma (higher = smoother, fewer peaks)
  • •Adjust --min-prominence (higher = only strong bands)
  • •For overloaded lanes, increase prominence threshold

If lane detection fails:

  • •Adjust --lane-sigma
  • •Manually specify lane count or positions (future flag)

Dependencies

  • •Python 3: numpy, scipy, scikit-image, Pillow
  • •rsvg-convert (for PNG export from SVG; install via brew install librsvg)
  • •sips (macOS built-in, for HEIC conversion)