Enhancing transcriptomic analysis by influencing de novoassembly using parallel computing
The efficient and accurate assembly of genomic data is a computationally intensive process that demands significant computational resources. Traditional sequential approaches often struggle to handle genomic data sets increasing volume and complexity, leading to prolonged execution times and subopti...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
UTM Press
2024
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/117603/ http://psasir.upm.edu.my/id/eprint/117603/1/117603.pdf |
| Summary: | The efficient and accurate assembly of genomic data is a computationally intensive process that demands significant computational resources. Traditional sequential approaches often struggle to handle genomic data sets increasing volume and complexity, leading to prolonged execution times and suboptimal results.The study aims to leverage parallel computing capabilities by employing the ABySS and Velvet Assembler tools on the MD2 Pineapple dataset hosted on the Quanta server. By systematically evaluating the performance of these tools across varying thread counts,the study seeks to identify optimal configurations that can enhance the efficiency and accuracy of the de novoassembly process, ultimately enabling more rapid and precise genomic analysis. The study found that for the ABySS assembler, an 8-core and 8-thread configuration exhibited the shortest execution time and greatest speedup, while an 8-core and 12-thread setup produced similar outcomes, demonstrating ABySS's flexibility to adjust to various thread configurations. Velvet assembler demonstrated exceptional performance by utilizing 8 cores and 16 threads for the velvetg command, and 8 cores and 8 threads for the velveth command.Significantly, this study provided implications for advancing genomic analysis methodologies by providing valuable guidance on optimizing the efficiency and accuracy of de novoassembly processes through careful selection of parallelization configurations, paving the way for future studies and applications in genetic data analysis |
|---|