Frequently Asked Questions
FAQ about the proMAD tool
What is proMAD? proMAD is a toolkit for analyzing protein membrane arrays and a novel systemic analytical concept for the quantification of optical signals detected on the membrane. Briefly, proMAD ensures an exact membrane alignment, utilizing basic computer vision techniques. It also provides a stable method to estimate the background light level. Utilizing the knowledge about the reaction kinetics of the underlying horseradish peroxidase-based signal detection method, the light production is modeled. Thereby, the concentration of the horseradish peroxidase enzyme is determined which directly reflects the target protein content bound on the membrane.
How can I use proMAD?
ProMAD can be used as a platform-independent web application.
There is no need to install any software on your computer.
To start using the WebApp, follow the steps here.
However, to analyze large data sets the library might be more suitable.
Which file formats are supported? At present, a range of commonly used image formats can be imported. Raw images (.scn) from ChemiDocMP systems (BioRad, Gladesville, Australia) can be used directly. To request support for other file types, please open an issue on our Github page.
Can I use proMAD if I don't have images at multiple exposure times? The core algorithm, the reaction-based model, is available when images at multiple exposure times are present. If there are images at various exposure times, but the app can not find that in the uploaded data, this information can be provided.
Other evaluation modes can be selected in cases where the exposure time information is missing:
|lib key||webApp selection||description|
|reac||Catalytic enzyme concentration||estimate of the catalytic enzyme concentration|
|raw||Spot average||a list of averaged gray values for the spots on all original images in the stack|
|raw_bg||Background corrected spot average||histogram-based background value deducted from raw list|
|local_bg||Ratio to local background||mean of the ratios between the original images, and the extracted backgrounds|
|hist_fg||Foreground to global background ratio||the linear correlation between background (histogram) evolution and the average foreground value|
|hist_raw||Ratio to global background||linear correlation between background (histogram) evolution to the average original image|
How are the results reported? At present, four report modules are available: json, csv, excel, and LateX. In the excel and the LateX report file, the average values of each analyte, as well as a graphical representation of the samples with the highest signals are presented. Moreover, an image of the membrane is included, which serves as an alignment check. Additional information about the software version and user-provided naming of the data set and membranes is also provided in the report.
Which types of membrane arrays do you support? Will there be any more in the future? Currently, proMAD supports four different membrane arrays from R&D Systems: ARY022B, ARY028, ARY015, and ARY007. To enquire about support for a certain membrane type, check the issue page Github site. Be advised that we will need images to implement other array kits.
Which file formats are supported when using the WebApp? Currently, the following file formats are implemented with different level of supports: .scn, .tif, and .png. To request support for other file types, please open an issue on our Github page.
Which types of membrane arrays do you support? Will there be any more in the future? We currently support following membrane arrays from R&D Systems: ARY022B, ARY028, ARY015, and ARY007. To enquire about support for a certain membrane type, check the issue page Github site. Be advised that we will need images to implement other array kits.
For how long are the request keys valid? You data will be stored for 2 days if you stopped the analysis process before the report was created. However, your reports are stored for 14 days.
What happens if I lose my request key? Unfortunately, you will need to start the analysis process again from step 1, the image upload.
How can I contribute to the project? Check out our Github page.