Professor Ruibang Luo from the School of Computing and Data Science and Professor Can Li from the Department of Electrical and Electronic Engineering their teams, worked on the research for the topic “Real-time raw signal genomic analysis using fully integrated memristor hardware”. The research findings were published by Nature Computational Science on September 12, 2025.
Details of the publication:
Real-time raw signal genomic analysis using fully integrated memristor hardware
Peiyi He, Shengbo Wang, Ruibin Mao, Mingrui Jiang, Sebastian Siegel, Giacomo Pedretti, Jim Ignowski, John Paul Strachan, Ruibang Luo & Can Li
Article in Nature Computational Science
https://www.nature.com/articles/s43588-025-00867-w
Abstract
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. These technologies generate noisy analog signals that traditionally require basecalling and read mapping, both demanding costly data movement on von Neumann hardware. Here, to overcome this, we present a memristor-based hardware–software codesign that processes raw sequencer signals directly in analog memory, combining the two separated steps. 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 on a fully integrated memristor chip. Our experimentally validated analysis confirms the effectiveness of this approach on real-world tasks, achieving a 97.15% F1 score in virus raw signal mapping, with 51× speed-up and 477× energy saving over an application-specific integrated circuit. These results demonstrate that in-memory computing hardware provides a viable solution for integration with portable sequencers, enabling real-time and on-site genomic analysis.