Specialized hardware-software systems for high-performance evolutionary and clinical genomics
Abstract/Contents
- Abstract
- The landscape of computing has undergone a significant transformation with the death of Dennard scaling and the slowing of Moore's Law: applications now drive innovations in computer systems architecture. At the same time, the advent of high throughput, low cost sequencing technology has revolutionized genomics. The massive volume of data generated in genomics has revealed significant computational challenges in performing large-scale, sensitive biological inference, primarily due to the limitations of software designed for traditional multicore systems. Similarly, the existing downstream processing of genomic data from acutely ill patients contributes to delayed diagnostics, directly impacting the speed at which critical medical decisions can be made. To address this challenge, my research employs a hardware-software-algorithm co-design approach to significantly improve computational performance (scale, sensitivity, speed) in key areas of comparative and clinical genomics. This dissertation presents systems that accelerate pipelines in both these domains of genomics. First, it describes SegAlign (GPU) and Darwin-WGA (FPGA/ASIC) cross-species whole genome alignment where co-design has yielded orders of magnitude increase in speed. Additionally, there are gains in accuracy while modifying the algorithm to improve the underlying hardware implementation. Next, it outlines the ultra-rapid nanopore whole genome sequencing pipeline that can deliver a genetic diagnosis in under 8 hours, making it the fastest pipeline to date. The scalable, cloud-based distributed infrastructure overcomes system bottlenecks to enable near real-time computation and improved variant identification. This pipeline has been deployed in critical care units in Stanford hospitals and applied to a cohort of multiple patients. Overall, these advancements not only redefine computational paradigms in genomics but also set a new standard for the integration of technological innovation in clinical settings, promising significant improvements in patient care and disease understanding.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2024; ©2024 |
| Publication date | 2024; 2024 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Goenka, Sneha Dilip Kumar |
|---|---|
| Degree supervisor | Horowitz, Mark |
| Thesis advisor | Horowitz, Mark |
| Thesis advisor | Ashley,Euan A |
| Thesis advisor | Raina, Priyanka |
| Degree committee member | Ashley,Euan A |
| Degree committee member | Raina, Priyanka |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Sneha D. Goenka. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2024. |
| Location | https://purl.stanford.edu/px421qh7904 |
Access conditions
- Copyright
- © 2024 by Sneha Dilip Kumar Goenka
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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