RawAlign: Accurate, Fast, and Scalable Raw Nanopore Signal Mapping via Combining Seeding and Alignment
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910661767856128 |
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| author | Lindegger, Joël Firtina, Can Ghiasi, Nika Mansouri Sadrosadati, Mohammad Alser, Mohammed Mutlu, Onur |
| author_facet | Lindegger, Joël Firtina, Can Ghiasi, Nika Mansouri Sadrosadati, Mohammad Alser, Mohammed Mutlu, Onur |
| contents | Nanopore sequencers generate raw electrical signals representing the contents of a biological sequence molecule passing through the nanopore. These signals can be analyzed directly, avoiding basecalling entirely. We observe that while existing proposals for raw signal analysis typically do well in all metrics for small genomes (e.g., viral genomes), they all perform poorly for large genomes (e.g., the human genome). Our goal is to analyze raw nanopore signals in an accurate, fast, and scalable manner. To this end, we propose RawAlign, the first work to integrate fine-grained signal alignment into the state-of-the-art raw signal mapper. To enable accurate, fast, and scalable mapping with alignment, RawAlign implements three algorithmic improvements and hardware acceleration via a vectorized implementation of fine-grained alignment. Together, these significantly reduce the overhead of typically computationally expensive fine-grained alignment. Our extensive evaluations on different use cases and various datasets show RawAlign provides 1) the most accurate mapping for large genomes and 2) and on-par performance compared to RawHash (between 0.80x-1.08x), while achieving better performance than UNCALLED and Sigmap by on average (geo. mean) 2.83x and 2.06x, respectively. Availability: https://github.com/CMU-SAFARI/RawAlign. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_05037 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RawAlign: Accurate, Fast, and Scalable Raw Nanopore Signal Mapping via Combining Seeding and Alignment Lindegger, Joël Firtina, Can Ghiasi, Nika Mansouri Sadrosadati, Mohammad Alser, Mohammed Mutlu, Onur Genomics Quantitative Methods Nanopore sequencers generate raw electrical signals representing the contents of a biological sequence molecule passing through the nanopore. These signals can be analyzed directly, avoiding basecalling entirely. We observe that while existing proposals for raw signal analysis typically do well in all metrics for small genomes (e.g., viral genomes), they all perform poorly for large genomes (e.g., the human genome). Our goal is to analyze raw nanopore signals in an accurate, fast, and scalable manner. To this end, we propose RawAlign, the first work to integrate fine-grained signal alignment into the state-of-the-art raw signal mapper. To enable accurate, fast, and scalable mapping with alignment, RawAlign implements three algorithmic improvements and hardware acceleration via a vectorized implementation of fine-grained alignment. Together, these significantly reduce the overhead of typically computationally expensive fine-grained alignment. Our extensive evaluations on different use cases and various datasets show RawAlign provides 1) the most accurate mapping for large genomes and 2) and on-par performance compared to RawHash (between 0.80x-1.08x), while achieving better performance than UNCALLED and Sigmap by on average (geo. mean) 2.83x and 2.06x, respectively. Availability: https://github.com/CMU-SAFARI/RawAlign. |
| title | RawAlign: Accurate, Fast, and Scalable Raw Nanopore Signal Mapping via Combining Seeding and Alignment |
| topic | Genomics Quantitative Methods |
| url | https://arxiv.org/abs/2310.05037 |