Term: Fall 2021
References:
We were inspired by two assignments (A07 and T09) from the class CSC 226: Software Implementation and Design at Berea College. All the genomic sequences were obtained at the National Center for Biotechnology Information Search database. https://www.ncbi.nlm.nih.gov/
SARS-COVID-19 Genome Analyzer
Purpose: It transcribes and translates viral genome. It compares COVID-19 strands sequenced in 12 countries with the first COVID-19 Sequence from December 2019. The objective is to observe SARS-CoV-2 mutations from Dec 2019.
CRC Cards:
Once the user clicks the "Start Analyzing" botton, a world map will pop out. The user should select the COVID sequence they would like to analyze from the 12 available countries. After that, the percentage similarity will pop out in a new window.
Biological limitations:
- Data, the samples have been collected at different times. It would be more accurate to observe the mutation having the same collection date.
- Quality of DNA sequence, we rely on the quality of the DNA sequences
- Comparing the mutations as DNA nucleotides would be better to identify what type of mutations happened in the strands of interest.
Technical limitations:
- We end up having three pages at the end of the execution of the program: the main page, the map and the result page. Our program would be perhaps more user-friendly if we closed the main page once the result page is executed.
It is fascinating that we can replicate biological processes to understand more intriguing questions such as: how much coronavirus has changed since December 2019? Understanding this virus' rapid mutations could help scientists to develop vaccines more rapidly. Our current design is specifically targeted at COVID-19 analysis.
We started, however, with the idea of creating a program that compares two organisms from different species. Due to time constraints and bioinformatics experience, we made a program that identifies the differences of different COVID DNA sequences.
We learned more about the application of OOP: using tkinter and classes effectively and also we were able to practice pair programming effectively.
Some major challenges we faced while doing this task was implementing the tkinter because we did not have previous experience implementing GUI on our programs. Another big challenge we faced was trying to simulate with precision biological processes like transcription and translation. We have realized that molecular processes are way more difficult (but exciting) to simulate computationally. Dr. Rosen and Dr. Anderson from the Biology Department guided us to improve our project.
Some future steps for our program is to identify the type of mutation between the two strands in the DNA level, also in a future opportunity, we can use genomic databases from other organisms.
We have learned through this program where to get the right resources and we plan to use them in the future.
Thank you to Dr. Ronald Rosen, Professor of Biology, who guided us in the design of this project. Liberty Mupotsa, Student Developer, who helped us in the technical side of the project.