Detect lanes and bands in SDS-PAGE gel images and assign molecular weights.
npx skills add https://github.com/farnunglab/benchaid --skill gel-annotation
Please help me install this Agent Skill. Skill: gel-annotation Source: https://github.com/farnunglab/benchaid/tree/main/skills/gel-annotation Command: npx skills add https://github.com/farnunglab/benchaid --skill gel-annotation
Annotating SDS-PAGE gels manually is time-consuming and error-prone; this tool automates lane and band detection and MW assignment to produce publication-quality annotations.
Run the gel annotation tool on an input gel image to produce an annotated SVG or PNG output.
High-intent search queries and answers about installing and using this skill.
Automatically detect SDS-PAGE gel lanes and bands by processing gel images with Python using numpy, scipy, and Pillow. The tool calls bands and assigns molecular weights from standard ladder presets to generate annotated SVG or PNG outputs.
Assign molecular weights to gel bands using standard ladder presets to create publication-ready figures. The tool outputs annotated SVG or PNG files with labeled lanes and MW marker visualizations suitable for benchtop protein analysis workflows.
SDS-PAGE gel annotation with Python reads common image formats including PNG, JPEG, and HEIC via sips. It requires Python 3 with numpy, scipy, and Pillow installed to process input images and generate annotated outputs.
Automate molecular weight marker visualization instead of manual annotation by running the Python-based gel annotation tool. It automatically detects lanes, calls bands, and assigns MW values from ladder presets to produce annotated SVG or PNG files.
Annotating gels and assigning molecular weights requires Python 3 with numpy, scipy, and Pillow installed. These dependencies enable automatic lane detection, band calling, and MW assignment from standard ladder presets for publication-quality outputs.
Manual SDS-PAGE gel annotation is time-consuming and error-prone because it requires labeling lanes and assigning MW values by hand. Automating the process with Python detects bands and assigns molecular weights from ladder presets to produce SVG or PNG outputs quickly.
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