تقييم أداء خوارزميات المعلوماتية الحيوية لدراسة تسلسل البروتين في البيئة التسلسلية والبيئة التفرعية باستخدام منصات الحوسبة السحابية

Abstract

Changes in protein chains or genetic material can lead to the emergence of various diseases, some of which may be life-threatening, making early diagnosis crucial to protect the patient's health. In the relentless pursuit of discovering these changes early, specialized bioinformatics algorithms have been developed, aiming to analyze protein chains and study the sequence of the genetic code in DNA. With the large volume and complexity of biological data, using traditional systems to process these algorithms becomes a real challenge, as it takes a long time for systems to deal with this huge data.

In this paper, we used a bioinformatics sequencing algorithm to detect protein sequences, then developed this algorithm into a parallel form, and implemented it via parallel computing using GPUs available on the Google Colab platform. Then, we made a comprehensive comparison of the results in terms of accuracy, execution time, and resource consumption compared to the original sequencing algorithm. The goal of this comparison was to determine the optimal solution for detecting changes in protein sequences, so that we can achieve higher accuracy, faster execution time, and lower cost.