RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes

Fırtına, Can; Ghiasi, Nika Mansouri; Lindegger, Joel; Singh, Gagandeep; Banu, Cavlak, Meryem; Mao, Haiyu; Mutlu, Onur

doi:10.1101/2023.01.22.525080

Cited by 7 publications

(7 citation statements)

References 162 publications

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“…This is an emerging and immature field and will inevitably require a substantial period of time to achieve the same level of maturity as base-level selective sequencing. Since the concept of nanopore selective sequencing was introduced, a range of different signal-level selective sequencing methods has been explored, including RUscripts [ 11 ], cwDTW [ 17 ], UNCALLED [ 15 ], sigmap [ 16 ], and, more recently, RawHash [ 21 ], DTWax [ 46 ], and RawMap [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

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Efficient real-time selective genome sequencing on resource-constrained devices

et al. 2022

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Background Third-generation nanopore sequencers offer selective sequencing or “Read Until” that allows genomic reads to be analyzed in real time and abandoned halfway if not belonging to a genomic region of “interest.” This selective sequencing opens the door to important applications such as rapid and low-cost genetic tests. The latency in analyzing should be as low as possible for selective sequencing to be effective so that unnecessary reads can be rejected as early as possible. However, existing methods that employ a subsequence dynamic time warping (sDTW) algorithm for this problem are too computationally intensive that a massive workstation with dozens of CPU cores still struggles to keep up with the data rate of a mobile phone–sized MinION sequencer. Results In this article, we present Hardware Accelerated Read Until (HARU), a resource-efficient hardware–software codesign-based method that exploits a low-cost and portable heterogeneous multiprocessor system-on-chip platform with on-chip field-programmable gate arrays (FPGA) to accelerate the sDTW-based Read Until algorithm. Experimental results show that HARU on a Xilinx FPGA embedded with a 4-core ARM processor is around 2.5× faster than a highly optimized multithreaded software version (around 85× faster than the existing unoptimized multithreaded software) running on a sophisticated server with a 36-core Intel Xeon processor for a SARS-CoV-2 dataset. The energy consumption of HARU is 2 orders of magnitudes lower than the same application executing on the 36-core server. Conclusions HARU demonstrates that nanopore selective sequencing is possible on resource-constrained devices through rigorous hardware–software optimizations. The source code for the HARU sDTW module is available as open source at https://github.com/beebdev/HARU, and an example application that uses HARU is at https://github.com/beebdev/sigfish-haru.

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Section: Discussionmentioning

confidence: 99%

“…Such a heuristic method that can currently map nanopore signals directly to gigabased-sized genomes does not exist. However, methods such as Sigmap [ 16 ], UNCALLED [ 15 ], and RawHash [ 21 ] are already setting the foundation for scalable direct signal mapping.…”

Section: Discussionmentioning

confidence: 99%

Efficient real-time selective genome sequencing on resource-constrained devices

et al. 2022

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“…State-of-the-art classifiers include hardware-accelerated solutions as well. SquiggleFilter [13] and RawHash [14] are virus detection and classification solutions that analyze the raw output (raw squiggles) of the ONT MinION sequencer [41]. On the opposite side of complexity spectrum, GenSLMs[61] applies large language models to identification and classification of viral variants using supercomputers such as Polaris at the Argonne Leadership Computing Facility and Selene at NVIDIA, as well as Cerebras CS-2 wafer-scale cluster.…”

Section: Background and Prior Artmentioning

confidence: 99%

DASH-CAM: Dynamic Approximate SearcH Content Addressable Memory for genome classification

Jahshan,

Merlin,

Garzón

et al. 2023

Preprint

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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% higherF1score 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.CCS CONCEPTS•Hardware→Bio-embedded electronics.

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“…For instance, real-time analysis platforms like EPI2ME by ONT (https://labs.epi2me.io/) and minoTour (Munro et al 2022) provide continuous access to real-time metrics and analysis, streamlining the sequencing process. Algorithmic tools such as BOSS-RUNS (Weilguny et al 2023), RawHash (Fırtına et al 2023), andBoardION (Bruno et al 2021) introduce dynamic decision strategies, hash-based similarity searches for efficient real-time analysis, and interactive web applications for ONT sequencing runs. Additional real-time detection tools, such as Metagenomic (Sanderson et al 2018) and NanoRTax (Rodríguez-Pérez et al 2022), provide immediate analytical pathways, concentrating on assessing metagenomic composition and viral detection tools.…”

Section: Introductionmentioning

confidence: 99%

Real-time transcriptomic profiling in distinct experimental conditions

Butto,

Pastore,

Müller

et al. 2024

Preprint

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Nanopore-technology offers real-time sequencing opportunities, providing rapid access to sequenced data and allowing researchers to manage the sequencing process efficiently, resulting in cost-effective strategies. Here, we present focused case studies demonstrating the versatility of real-time transcriptomics analysis in rapid quality control for long-read RNA-seq. We illustrate its utility through three experimental setups: 1) transcriptome profiling of distinct human cellular populations, 2) identification of experimentally enriched transcripts, and 3) identification of experimentally manipulated genes (knockout and overexpression) in several yeast strains. We show how to perform multiple layers of quality control as soon as sequencing has started, addressing both the quality of the experimental and sequencing traits. Real-time quality control measures comprise assessing sample/condition variability and determining the number of identified genes per sample/condition. Furthermore, real-time differential gene/transcript expression analysis can be conducted at various time points post-sequencing initiation (PSI), revealing dynamic changes in gene/transcript expression between two conditions. Using real-time analysis, which occurs in parallel to the sequencing run, we identified differentially expressed genes/transcripts as early as 1-hour PSI. These changes were consistently observed throughout the entire sequencing process. We discuss the new possibilities offered by real-time data analysis, which have the potential to serve as a valuable tool for rapid and cost-effective quality checks in specific experimental settings and can be potentially integrated into clinical applications in the future.

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RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes

Cited by 7 publications

References 162 publications

Efficient real-time selective genome sequencing on resource-constrained devices

Efficient real-time selective genome sequencing on resource-constrained devices

DASH-CAM: Dynamic Approximate SearcH Content Addressable Memory for genome classification

Real-time transcriptomic profiling in distinct experimental conditions

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