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Estimate Base Editing Efficiency with Sanger Sequencing. This program is created to deal with multiple base-edit data in a short time.
One of my colleague once asked me if I can help her to analyse thousands of sanger sequence data and show her the edit efficiency of each sample. I was shocked by the work. She uploaded the ab1 file to a web one by one, which is a tough job.
I know there is a R program, but I know nothing about R and R is very unfriendly to those known nothing about programing. And R program cannot be packed to an exe or other executable file. It's very annoying.
So I wrote this program with Python and Qt5. Every user can use it even you know nothing about programing! All you need to do is click you mouse and press "Ctr + C" and "Ctr + V". And the results will be added to a single EXCEL file ,which allows you to analyse your data in MicroSoft Excel or WPS.
Download the exe file here and execute it.
Fill the first colum sample name and the second colum guide is enough.
If you want the program summarize more information to you, the 3nd, 4th, 5th colum should be filled. Then it will return the edit efficiency of the unique position you want.
Or you can export the current table, and edit the table in Excel. After edited, Import the excel table to this program.
The target site is CCCGGCTCTGGCTAAA, and the base, C, will be converte to T.
For the sequencing file, just select multi samples and drag them to reorganization area. Or you can click the 2nd tab to auto fill the table.
After that, click Start! choose a directory and all will be done in minutes.
The results are show here, you can click the "Open Report" button and see the full report in a local webpage.
In the program, you can see the edit efficiency of each sample and edit efficiency at all position.
Most importantly, this table can be export to an Excel file! you don't need
to do more job in data coping!
For the report webpage, you can see the sequencing data and accuracy of your sample, though most of my user won't open it.
Sorry I have no Mac, so I cannot pack it to a mac version. If you have python in your Mac, download the code and find someone to help you.
Linux users can handle all the difficulties easily, so I have no suggestion.
I did't publish it, and I don't know if it can be published in any magazine. Citing this work in acknowledgement is fine!
If some day I put it to a magazine, I will update this info.