Comparison of the complete genome sequences of COVID-19 and Zika virus using BioPython
Keywords:
BioPython, Data Analyzing, Bioinformatics, Sequence analyzing, COVID-19Abstract
Background: Bioinformatics has always been one of the important tools in biological research. Increasing in the biological data, triggered some difficulties in the data analysis. To solve these problems BioPython developed, and created a better platform for researchers to conduct their study in a short way just by writing appropriate BioPython codes.
Methods: In this research, bioinformatics sites such as NCBI were first used so that the genome sequences of both Zika and Corona viruses were completely stored in the Festa file. The PyCharm program was then used to apply BioPython codes and facilitate the speed of data analysis. Finally, the sequences were analyzed with BioPython codes to determine the GC percentage, length and number of amino acids that each sequence encodes.
Results: The results showed that, although the zika virus has a shorter DNA sequence, the amino acids ratio encoded by the whole genome is almost the same as COVID-19 and the CG content of zika is more than coronavirus. After analyzing the sequence of both viruses, it was found that the three amino acids Thr, Ser and Leu are the three amino acids that both viruses had the most codons of their production in their genomes. In contrast, in both sequences, the amount of Met encoded by the entire genome is in fewer amounts.
Conclusions: In this research, the COVID-19 and Zika virus whole genomes are analyzed with BioPython and compared with each other. As a result, both viruses have historically been major threats to human health, so knowing enough and having the necessary information about their genomes will enable scientists to find the right drug or vaccine to deal with them in a shorter time. In this case, obtaining the desired information in the shortest possible time using programming can improve the speed of work and help to perform calculations in a shorter time. In the meantime, Python programming enables us to obtain valuable information about the desired strain in the shortest possible time in the next pandemic.
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