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Nanopore-based sequencers generate a series of raw electrical signalmeasurements that represent the contents of a biological sequence moleculepassing through the sequencer's nanopore. If the raw signal is analyzed inreal-time, an irrelevant molecule can be ejected from the nanopore before it iscompletely sequenced, reducing sequencing time. To meet the low-latency andhigh-throughput requirements of the real-time analysis, a number of recentworks propose the direct analysis of raw nanopore signals instead oftraditional basecalling-based analysis approaches. We observe that whileexisting proposals for raw signal read mapping typically do well in all metricsfor small reference databases (e.g., viral genomes), they all fail to scale tolarge reference databases (e.g., the human genome) in some aspect. Our goal isto analyze raw nanopore signals with high accuracy, high throughput, lowlatency, low memory usage, and needing few bases to be sequenced for a widerange of reference database sizes. To this end, we propose RawAlign, the firstSeed-Filter-Align mapper for raw nanopore signals. Our evaluation shows thatRawAlign is the only tool that can map raw nanopore signals to large referencedatabases $\geq$3117Mbp with high accuracy. Our evaluation shows that RawAligngeneralizes well to a wide range of reference database sizes. In particular,RawAlign has a similar throughput to the overall prior state-of-the-art RawHash(between 0.80$\times$-1.08$\times$) while improving accuracy on all datasets(between 1.02$\times$-1.64$\times$ F-1 score). RawAlign provides a 2.83$\times$(2.06$\times$) speedup over Uncalled (Sigmap) on average (geo. mean) whileimproving accuracy by 1.35$\times$ (1.34$\times$) in terms of F-1 score onaverage (geo. mean). Availability: https://github.com/cmu-safari/RawAlign

RawAlign: Accurate, Fast, and Scalable Raw Nanopore Signal Mapping via Combining Seeding and Alignment