https://scholars.lib.ntu.edu.tw/handle/123456789/581438
標題: | Accelerating Variant Calling with Parallelized DeepVariant | 作者: | Yang C.-H Zeng J.-W Liu C.-Y SHIH-HAO HUNG |
關鍵字: | Deep neural network; DeepVariant; Genome; Next-generation sequencing; Variant calling | 公開日期: | 2020 | 起(迄)頁: | 13-18 | 來源出版物: | ACM International Conference Proceeding Series | 摘要: | Due to the rapid evolution of the next-generation sequencing (NGS) technology, the sequence of an individual's genome can be determined from billions of short reads at a decreasing cost, which has advanced the fields of medical research and precision medicine with the ability to correlate mutations between genomes. Analysis of genome sequences, especially variant calling, is exceedingly computationally intensive, as it demands large storage capacity, computing power, and high-speed network to reduce the processing time. In the case of DeepVariant, an open-source software package which employs a deep neural network (DNN) to calls genetic variants, it took four hours to complete the analysis on a workstation with a high-performance GPU device to accelerate the DNN. Therefore, we profiled the performance of DeepVariant and refactored the code to reduce the time and cost of the NGS pipeline with a series of code optimization works. As a result, our distributed version of DeepVariant can finish the same job within 8 minutes on 8 dual-CPU nodes and 8 GPUs, which outperforms commercial versions in the market. ? 2020 ACM. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097436511&doi=10.1145%2f3400286.3418243&partnerID=40&md5=85f01d3fb5abaca3e1ec32dde59a5b0b https://scholars.lib.ntu.edu.tw/handle/123456789/581438 |
DOI: | 10.1145/3400286.3418243 | SDG/關鍵字: | Deep neural networks; HIgh speed networks; Open source software; Program processors; Code optimization; Computing power; Genetic variants; Genome sequences; Medical research; Next-generation sequencing; Processing time; Storage capacity; Open systems |
顯示於: | 資訊工程學系 |
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