Sneha Goenka is a Ph.D. candidate in the Electrical Engineering Department at Stanford University where she is advised by Prof. Mark Horowitz. Her research centers on designing efficient computer systems for advancing genomic pipelines for clinical and research applications, with a focus on improving speed and cost. She is a 2023 Forbes 30 Under 30 Honoree in the Science category, 2022 NVIDIA Graduate Fellow, and 2021 Cadence Women in Technology Scholar. She has a B.Tech. and M.Tech. (Microelectronics) in Electrical Engineering from the Indian Institute of Technology, Bombay where she received the Akshay Dhoke Memorial Award for the most outstanding student in the program.
Accelerated identification of disease-causing variants with ultra-rapid nanopore genome sequencing.
Whole-genome sequencing (WGS) can identify variants that cause genetic disease, but the time required for sequencing and analysis has been a barrier to its use in acutely ill patients. In the present study, we develop an approach for ultra-rapid nanopore WGS that combines an optimized sample preparation protocol, distributing sequencing over 48 flow cells, near real-time base calling and alignment, accelerated variant calling and fast variant filtration for efficient manual review. Application to two example clinical cases identified a candidate variant in <8 h from sample preparation to variant identification. We show that this framework provides accurate variant calls and efficient prioritization, and accelerates diagnostic clinical genome sequencing twofold compared with previous approaches.
View details for DOI 10.1038/s41587-022-01221-5
View details for PubMedID 35347328
- Ultra-Rapid Nanopore Whole Genome Genetic Diagnosis of Dilated Cardiomyopathy in an Adolescent With Cardiogenic Shock. Circulation. Genomic and precision medicine 2022: CIRCGEN121003591
- Ultrarapid Nanopore Genome Sequencing in a Critical Care Setting. The New England journal of medicine 2022
SegAlign: A Scalable GPU-Based Whole Genome Aligner
International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
View details for DOI 10.1109/SC41405.2020.00043
- Darwin-WGA: A Co-processor Provides Increased Sensitivity in Whole Genome Alignments with High Speedup IEEE. 2019: 359–72