We propose GenMClass, a genome classification system-on-chip (SoC) implementing two different classification approaches and comprising two separate classification engines: a DNN accelerator GenDNN, that classifies DNA reads converted to images using a classification neural network, and a similarity search-capable Error Tolerant Content Addressable Memory ETCAM, that classifies genomes by k-mer matching. Classification operations are controlled by an embedded RISCV processor. GenMClass classification platform was designed and manufactured in a commercial 65 nm process. We conduct a comparative analysis of ETCAM and GenDNN classification efficiency as well as their performance, silicon area and power consumption using silicon measurements. The size of GenMClass SoC is 3.4 mm 2 and its total power consumption (assuming both GenDNN and ETCAM perform classification at the same time) is 144 mW. This allows using GenMClass as a portable classifier for pathogen surveillance during pandemics, food safety and environmental monitoring, agriculture pathogen and antimicrobial resistance control, in the field or at points of care.

MOTIVATION: Rapid spread of viral diseases such as Coronavirus disease 2019 (COVID-19) highlights an urgent need for efficient surveillance of virus mutation and transmission dynamics, which requires fast, inexpensive and accurate viral lineage assignment. The first two goals might be achieved through low-coverage whole-genome sequencing (LC-WGS) which enables rapid genome sequencing at scale and at reduced costs. Unfortunately, LC-WGS significantly diminishes the genomic details, rendering accurate lineage assignment very challenging. RESULTS: We present ViTAL, a novel deep learning algorithm specifically designed to perform lineage assignment of low coverage-sequenced genomes. ViTAL utilizes a combination of MinHash for genomic feature extraction and Vision Transformer for fine-grain genome classification and lineage assignment. We show that ViTAL outperforms state-of-the-art tools across diverse coverage levels, reaching up to 87.7% lineage assignment accuracy at 1× coverage where state-of-the-art tools such as UShER and Kraken2 achieve the accuracy of 5.4% and 27.4% respectively. ViTAL achieves comparable accuracy results with up to 8× lower coverage than state-of-the-art tools. We explore ViTAL's ability to identify the lineages of novel genomes, i.e. genomes the Vision Transformer was not trained on. We show how ViTAL can be applied to preliminary phylogenetic placement of novel variants. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in https://github.com/zuherJahshan/vital and can be accessed with 10.5281/zenodo.10688110.

We propose a novel dynamic storage-based approximate search content addressable memory (DASH-CAM) for computational genomics applications, particularly for identification and classification of viral pathogens of epidemic significance. DASH-CAM provides 5.5 × better density compared to state-of-the-art SRAM-based approximate search CAM. This allows using DASH-CAM as a portable classifier that can be applied to pathogen surveillance in low-quality field settings during pandemics, as well as to pathogen diagnostics at points of care. DASH-CAM approximate search capabilities allow a high level of flexibility when dealing with a variety of industrial sequencers with different error profiles. DASH-CAM achieves up to 30% and 20% higher F1 score when classifying DNA reads with 10% error rate, compared to state-of-the-art DNA classification tools MetaCache-GPU and Kraken2 respectively. Simulated at 1GHz, DASH-CAM provides 1, 178 × and 1, 040 × average speedup over MetaCache-GPU and Kraken2 respectively.

We propose a novel edit distance-tolerant content addressable memory (EDAM) for energy-efficient approximate search applications. Unlike state-of-the-art approximate search solutions that tolerate certain Hamming distance between the query pattern and the stored data, EDAM tolerates edit distance, which makes it especially efficient in applications such as text processing and genome analysis. EDAM was designed using a commercial 65 nm 1.2 V CMOS technology and evaluated through extensive Monte Carlo simulations, while considering different process corners. Simulation results show that EDAM can achieve robust approximate search operation with a wide range of edit distance threshold levels. EDAM is functionally evaluated as a pathogen DNA detection and classification accelerator. EDAM achieves up to 1.7× higher F1 score for high-quality DNA reads and up to 19.55× higher F1 score for DNA reads with 15% error rate, compared to state-of-the-art DNA classification tool Kraken2. Simulated at 667 MHz, EDAM provides 1, 214× average speedup over Kraken2. This makes EDAM suitable for hardware acceleration of genomic surveillance of outbreaks, such as the ongoing Covid-19 pandemic.