GelQuant

Simple band quantification software for protein and DNA gels/blots.

Overview

GelQuant processes gel/blot images for quantification. The software allows users to:

Import and crop gel images as desired.
Split the image into user-specified vertical lanes.
Convert lanes to numerical data using NumPy.
Calculate average RGB intensity for each row in the lane.
Apply Gaussian weighting to emphasize the middle of the lane while reducing edge interference.
Baseline correction based on user-specified regions.
Plot the data and perform band intensity quantification.

For an example of how GelQuant works, check out:
notebook-example.ipynb
gel-example.png

To install a development version, clone this repository and install it with pip:

GelQuant requires the following Python libraries:

matplotlib ,numpy ,pillow (PIL) ,pandas ,os ,natsort ,scipy

note: for os (built-in, no installation needed)

To Install pip install matplotlib numpy pillow pandas natsort scipy

Name		Name	Last commit message	Last commit date
.ipynb_checkpoints		.ipynb_checkpoints
bioimage_quant.egg-info		bioimage_quant.egg-info
bioimage_quant		bioimage_quant
.DS_Store		.DS_Store
.json		.json
LICENSE		LICENSE
README.md		README.md
Use case(brief)		Use case(brief)
_decay_curve.svg		_decay_curve.svg
a_data.json		a_data.json
a_data_decay_curve.svg		a_data_decay_curve.svg
aggregate_data_example.png		aggregate_data_example.png
b_data.json		b_data.json
b_data_decay_curve.svg		b_data_decay_curve.svg
data.svg		data.svg
data_output.csv		data_output.csv
f_p1_row1.npy		f_p1_row1.npy
f_p1_row2.npy		f_p1_row2.npy
f_p1_row3.npy		f_p1_row3.npy
gel-example.png		gel-example.png
sds-page-example.ipynb		sds-page-example.ipynb
setup.py		setup.py
spotting-assay-example.ipynb		spotting-assay-example.ipynb
yeast_spotting_example.png		yeast_spotting_example.png