2025/04/22 by Peiyi He, Shengbo Wang, He, Peiyi +15 · 1 voice · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Emerging Technologies (cs.ET) #FOS: Biological sciences #FOS: Computer and information sciences #Quantitative Methods (q-bio.QM) #cs.ET #q-bio.QM
paper · pdf · doi:10.48550/arxiv.2504.15934
Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, hampering on-site genomic analysis due to prohibitive time and energy costs. These technologies generate a massive amount of noisy analog signals that traditionally require basecalling and digital mapping, both demanding frequent and costly data movement on von Neumann hardware. To overcome these challenges, we present a memristor-based hardware-software co-design that processes raw sequencer signals directly in analog memory, effectively combining the separated basecalling and read mapping steps. Here we demonstrate, for the first time, end-to-end memristor-based genomic analysis in a fully integrated memristor chip. By exploiting intrinsic device noise for locality-sensitive hashing and implementing parallel approximate searches in content-addressable memory, we experimentally showcase on-site applications including infectious disease detection and metagenomic classification. Our experimentally-validated analysis confirms the effectiveness of this approach on real-world tasks, achieving a state-of-the-art 97.15% F1 score in virus raw signal mapping, with 51x speed up and 477x energy saving compared to implementation on a state-of-the-art ASIC. These results demonstrate that memristor-based in-memory computing provides a viable solution for integration with portable sequencers, enabling truly real-time on-site genomic analysis for applications ranging from pathogen surveillance to microbial community profiling.