Alzheimer’s Disease Sequencing Project: Computational Challenges
- 講者Li-San Wang 教授 (Penn Neurodegeneration Genomics Center)
邀請人：林仲彥 - 時間2023-01-04 (Wed.) 10:30 ~ 12:30
- 地點資訊所新館106演講廳
摘要
Modern human genetic studies recruit hundreds of thousands of participants and analyze their complete genomes using dense SNP arrays or whole genome sequencing technology to identify genetic variants that correlate with traits or contribute to the risk or protection of diseases. Alzheimer’s Disease Sequencing Project (ADSP) is one such project with an ambitious goal to sequence up to 150,000 complete genomes from Alzheimer’s disease patients and cognitively normal participants worldwide to identify genetic variations that modulate the risk of disease and can be targets for therapeutic development.
In this talk I will focus on the computational challenges we face when implementing the data production, sharing and analysis for the ADSP project. Topics include (1) cloud-based hosting and sharing of genomic data with the global research community securely and in compliance with federal regulations, (2) processing large amounts of genomic data on the cloud with high quality, reliability and flexibility while minimizing the costs, (3) data platforms that enable genomic knowledge discovery and large-scale genomic data integration, (4) fine-mapping methods for refining noncoding genetic associations with cellular context and molecular mechanism. We look for collaborative opportunities that will develop best practices and innovative solutions to better leverage this valuable resource for AD gene discovery and therapeutic development.
In this talk I will focus on the computational challenges we face when implementing the data production, sharing and analysis for the ADSP project. Topics include (1) cloud-based hosting and sharing of genomic data with the global research community securely and in compliance with federal regulations, (2) processing large amounts of genomic data on the cloud with high quality, reliability and flexibility while minimizing the costs, (3) data platforms that enable genomic knowledge discovery and large-scale genomic data integration, (4) fine-mapping methods for refining noncoding genetic associations with cellular context and molecular mechanism. We look for collaborative opportunities that will develop best practices and innovative solutions to better leverage this valuable resource for AD gene discovery and therapeutic development.