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

View ORCID ProfileCan Firtina, View ORCID ProfileNika Mansouri Ghiasi, View ORCID ProfileJoel Lindegger, View ORCID ProfileGagandeep Singh, View ORCID ProfileMeryem Banu Cavlak, View ORCID ProfileHaiyu Mao, View ORCID ProfileOnur Mutlu
doi: https://doi.org/10.1101/2023.01.22.525080
Can Firtina
1ETH Zurich
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  • For correspondence: canfirtina@gmail.com
Nika Mansouri Ghiasi
1ETH Zurich
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Joel Lindegger
1ETH Zurich
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Gagandeep Singh
1ETH Zurich
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Meryem Banu Cavlak
1ETH Zurich
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Haiyu Mao
1ETH Zurich
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Onur Mutlu
1ETH Zurich
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Abstract

Nanopore sequencers generate electrical raw signals in realtime while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opportunity for real-time genome analysis. An important feature of nanopore sequencing, Read Until, can eject strands from sequencers without fully sequencing them, which provides opportunities to computationally reduce the sequencing time and cost. However, existing works utilizing Read Until either 1) require powerful computational resources that may not be available for portable sequencers or 2) lack scalability for large genomes, rendering them inaccurate or ineffective.

We propose RawHash, the first mechanism that can accurately and efficiently perform real-time analysis of nanopore raw signals for large genomes using a hash-based similarity search. To enable this, RawHash ensures the signals corresponding to the same DNA content lead to the same hash value, regardless of the slight variations in these signals. RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value.

We evaluate RawHash on three applications: 1) read mapping, 2) relative abundance estimation, and 3) contamination analysis. Our evaluations show that RawHash is the only tool that can provide high accuracy and high throughput for analyzing large genomes in real-time. When compared to the state-of-the-art techniques, UNCALLED and Sigmap, RawHash provides 1) 25.8× and 3.4× better average throughput and 2) an average speedup of 32.1 × and 2.1 × in the mapping time, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 23, 2023.
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RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes
Can Firtina, Nika Mansouri Ghiasi, Joel Lindegger, Gagandeep Singh, Meryem Banu Cavlak, Haiyu Mao, Onur Mutlu
bioRxiv 2023.01.22.525080; doi: https://doi.org/10.1101/2023.01.22.525080
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RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes
Can Firtina, Nika Mansouri Ghiasi, Joel Lindegger, Gagandeep Singh, Meryem Banu Cavlak, Haiyu Mao, Onur Mutlu
bioRxiv 2023.01.22.525080; doi: https://doi.org/10.1101/2023.01.22.525080

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