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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.

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  • Local read haplotagging enables accurate long-read small variant calling. bioRxiv : the preprint server for biology Kolesnikov, A., Cook, D., Nattestad, M., McNulty, B., Gorzynski, J., Goenka, S., Ashley, E. A., Jain, M., Miga, K. H., Paten, B., Chang, P. C., Carroll, A., Shafin, K. 2023

    Abstract

    Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford nanopore technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for long-read sequencing platforms.

    View details for DOI 10.1101/2023.09.07.556731

    View details for PubMedID 37745389

    View details for PubMedCentralID PMC10515762

  • Accelerated identification of disease-causing variants with ultra-rapid nanopore genome sequencing. Nature biotechnology Goenka, S. D., Gorzynski, J. E., Shafin, K., Fisk, D. G., Pesout, T., Jensen, T. D., Monlong, J., Chang, P. C., Baid, G., Bernstein, J. A., Christle, J. W., Dalton, K. P., Garalde, D. R., Grove, M. E., Guillory, J., Kolesnikov, A., Nattestad, M., Ruzhnikov, M. R., Samadi, M., Sethia, A., Spiteri, E., Wright, C. J., Xiong, K., Zhu, T., Jain, M., Sedlazeck, F. J., Carroll, A., Paten, B., Ashley, E. A. 2022

    Abstract

    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 Gorzynski, J. E., Goenka, S. D., Shafin, K., Jensen, T. D., Fisk, D. G., Grove, M. E., Spiteri, E., Pesout, T., Monlong, J., Bernstein, J. A., Ceresnak, S., Chang, P., Christle, J. W., Chubb, H., Dunn, K., Garalde, D. R., Guillory, J., Ruzhnikov, M. R., Wright, C., Wusthoff, C. J., Xiong, K., Hollander, S. A., Berry, G. J., Jain, M., Sedlazeck, F. J., Carroll, A., Paten, B., Ashley, E. A. 2022: CIRCGEN121003591

    View details for DOI 10.1161/CIRCGEN.121.003591

    View details for PubMedID 35133172

  • Ultrarapid Nanopore Genome Sequencing in a Critical Care Setting. The New England journal of medicine Gorzynski, J. E., Goenka, S. D., Shafin, K., Jensen, T. D., Fisk, D. G., Grove, M. E., Spiteri, E., Pesout, T., Monlong, J., Baid, G., Bernstein, J. A., Ceresnak, S., Chang, P. C., Christle, J. W., Chubb, H., Dalton, K. P., Dunn, K., Garalde, D. R., Guillory, J., Knowles, J. W., Kolesnikov, A., Ma, M., Moscarello, T., Nattestad, M., Perez, M., Ruzhnikov, M. R., Samadi, M., Setia, A., Wright, C., Wusthoff, C. J., Xiong, K., Zhu, T., Jain, M., Sedlazeck, F. J., Carroll, A., Paten, B., Ashley, E. A. 2022

    View details for DOI 10.1056/NEJMc2112090

    View details for PubMedID 35020984

  • SegAlign: A Scalable GPU-Based Whole Genome Aligner International Conference for High Performance Computing, Networking, Storage and Analysis (SC) Goenka, S., Turakhia, Y., Paten, B., Horowitz, M. 2020: 540–552

    View details for DOI 10.1109/SC41405.2020.00043

  • Darwin-WGA: A Co-processor Provides Increased Sensitivity in Whole Genome Alignments with High Speedup Turakhia, Y., Goenka, S. D., Bejerano, G., Dally, W. J., IEEE IEEE. 2019: 359–72

    View details for DOI 10.1109/HPCA.2019.00050

    View details for Web of Science ID 000469766300028

https://orcid.org/0000-0002-1716-7769