Suite of Jupyter Notebook-based tools for interpreting and analyzing standard and capillary gel signals. Also acts as the companion software to our paper entitled 'Multiplexed Label-Free Biomarker Detection by Targeted Disassembly of Variable-Length DNA Payload Chains'.
Capabilities:
- GUI for manual selection of gel lanes and bands, with features available for enforcing lane length/width with arbitrary rotations.
- Analysis of lane data with automatic background correction and publication-quality plotting.
- Analysis and publication-quality plotting of capillary electrophoresis signal data from Agilent Fragment Analyzers.
Limitations:
- Can only work with one band per lane (currently)
- No options for inverting gel images.
- Capillary electrophoresis lane labelling requires manual intervention.
- UI unoptimized.
To install, ideally create a new environment using Conda (or preferred Python environment manager):
conda create -n gel_analysis python=3.7 # replace environment name and python version with user preference
Then installed required packages as follows:
conda install --file=requirements.txt -c conda-forge -c anaconda
If you already have Scipy, Numpy, Jupyter, Ipywidgets, PIL, Matplotlib and Pandas, there is no need to create a new environment.
Tested using both Python 3.7 and 3.8 on Mac OS 12.x. However, no OS-specific configuration/codes were used.
- All capillary electrophoresis analysis code can be found in
capillary_analysis.ipynb - The gel lane/band finder can be found in
gel_lane_finder.ipynb - The gel image analysis script can be found in
gel_trace_analysis.ipynb
Example standard gel/capillary gel data can be found in the supplementary files of the associated paper here.
Capillary electrophoresis analysis
Lane selection output
Standard gel trace plot (after background subtraction)
Any additional contributions welcome, especially on user interface (both command-line and GUI).