Computational Molecular Biology 2025, Vol.15, No.3, 151-159 http://bioscipublisher.com/index.php/cmb 159 Poplin R., Chang P., Alexander D., Schwartz S., Colthurst T., Ku A., Newburger D., Dijamco J., Nguyen N., Afshar P., Gross S., Dorfman L., McLean C., and DePristo M., 2018, A universal SNP and small-indel variant caller using deep neural networks, Nature Biotechnology, 36(10): 983-987. https://doi.org/10.1038/nbt.4235 Vats P., Sethia A., Samadi M., and Harkins T., 2022, Rapid variant detection and annotations from next-generation sequencing data using a GPU-accelerated framework, Cancer Research, 82(12_Supplement): 1900-1900. https://doi.org/10.1158/1538-7445.AM2022-1900 Xiao A., Dong S., Liu C., Zhang L., and Wu Z., 2018, CloudGT: a high-performance genome analysis toolkit leveraging pipeline optimization on Spark, In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp.1343-1350. https://doi.org/10.1109/BIBM.2018.8621495 Yang C.H., Zeng J.W., Liu C., and Hung S.H., 2020, Accelerating variant calling with parallelized DeepVariant, In: Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pp.13-18. https://doi.org/10.1145/3400286.3418243 Zhou Y., Kathiresan N., Yu Z., Rivera L.F., Thimma M., Manickam K., Chebotarov D., Mauleon R., Chougule K., Wei S., Gao T., Green C., Zuccolo A., Ware D., Zhang J., McNally K., and Wing R., 2023, HPC-based genome variant calling workflow (HPC-GVCW), bioRxiv, 25: 546520. https://doi.org/10.1101/2023.06.25.546420 Zhou Y., Wu Z., and Dong S., 2021, ADS-HCSpark: a scalable HaplotypeCaller leveraging adaptive data segmentation to accelerate variant calling on Spark, BMC Bioinformatics, 20(1): 76. https://doi.org/10.1186/s12859-019-2665-0
RkJQdWJsaXNoZXIy MjQ4ODYzNA==