Yeni Nesil Dizileme Analizinde Hizalama Adımının GPU Başarımı

As the amount of biological data has increased, it has become difficult to process it effectively.This has brought the discipline of bioinformatics to the forefront and increased the need for the development of relevant tools.A sensitive data analysis process is required to make sense of this large amount of data produced by next generation sequencing technique.The most costly step in this process is the alignment step.One of the most effective techniques to reduce this cost is the use of a graphics processing unit.In this study, the performances of the CPU-based Burrows-Wheeler aligner and the GPU programming version BarraCUDA tools in the alignment step were compared in terms of alignment rates and computation times for different datasets.In the study, total runtime of these tools was also examined, as well as the runtime of the alignment sub-steps when using one or more GPUs.While there is a similarity in the alignment rates of the tools used in each data set, it has been observed that there is a significant time benefit in data of different sizes through GPU supported BarraCUDA.As a result, with the use of GPU in the alignment step, approximately 5 times acceleration was achieved in single-end data and approximately 9 times in paired-end data.