1
|
Beck TL, Carloni P, Asthagiri DN. All-Atom Biomolecular Simulation in the Exascale Era. J Chem Theory Comput 2024; 20:1777-1782. [PMID: 38382017 DOI: 10.1021/acs.jctc.3c01276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.
Collapse
Affiliation(s)
- Thomas L Beck
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Paolo Carloni
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
- Department of Physics, RWTH Aachen University, D-52078 Aachen, Germany
| | - Dilipkumar N Asthagiri
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| |
Collapse
|
2
|
Naito T, Okada Y. Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology. J Hum Genet 2024:10.1038/s10038-023-01213-6. [PMID: 38225263 DOI: 10.1038/s10038-023-01213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/23/2023] [Accepted: 12/04/2023] [Indexed: 01/17/2024]
Abstract
The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation methods for genome-wide variants with the capability of learning complex linkage disequilibrium patterns have been developed. Additionally, deep learning-based imputation has been applied to a distinct genomic region known as the major histocompatibility complex, referred to as HLA imputation. Despite their various advantages, the current deep learning-based genotype imputation methods do have certain limitations and have not yet become standard. These limitations include the modest accuracy improvement over statistical and conventional machine learning-based methods. However, their benefits include other aspects, such as their "reference-free" nature, which ensures complete privacy protection, and their higher computational efficiency. Furthermore, the continuing evolution of deep learning technologies is expected to contribute to further improvements in prediction accuracy and usability in the future.
Collapse
Affiliation(s)
- Tatsuhiko Naito
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan.
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, 2-2, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
| |
Collapse
|
3
|
Chi Duong V, Minh Vu G, Khac Nguyen T, Tran The Nguyen H, Luong Pham T, S Vo N, Hong Hoang T. A rapid and reference-free imputation method for low-cost genotyping platforms. Sci Rep 2023; 13:23083. [PMID: 38155188 PMCID: PMC10754833 DOI: 10.1038/s41598-023-50086-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 12/15/2023] [Indexed: 12/30/2023] Open
Abstract
Most current genotype imputation methods are reference-based, which posed several challenges to users, such as high computational costs and reference panel inaccessibility. Thus, deep learning models are expected to create reference-free imputation methods performing with higher accuracy and shortening the running time. We proposed a imputation method using recurrent neural networks integrating with an additional discriminator network, namely GRUD. This method was applied to datasets from genotyping chips and Low-Pass Whole Genome Sequencing (LP-WGS) with the reference panels from The 1000 Genomes Project (1KGP) phase 3, the dataset of 4810 Singaporeans (SG10K), and The 1000 Vietnamese Genome Project (VN1K). Our model performed more accurately than other existing methods on multiple datasets, especially with common variants with large minor allele frequency, and shrank running time and memory usage. In summary, these results indicated that GRUD can be implemented in genomic analyses to improve the accuracy and running-time of genotype imputation.
Collapse
Affiliation(s)
- Vinh Chi Duong
- Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
- GeneStory Joint Stock Company, Hanoi, Vietnam
| | - Giang Minh Vu
- Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
- GeneStory Joint Stock Company, Hanoi, Vietnam
| | | | - Hung Tran The Nguyen
- Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
- Nanyang Technological University, Singapore, Singapore
| | | | - Nam S Vo
- Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam.
- GeneStory Joint Stock Company, Hanoi, Vietnam.
| | - Tham Hong Hoang
- Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam.
- GeneStory Joint Stock Company, Hanoi, Vietnam.
| |
Collapse
|