RawHash：实现对大型基因组原始纳米孔信号的快速、准确实时分析
RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes
AI总结：
本文提出 RawHash，通过信号量化与哈希相似性搜索实现大型基因组纳米孔原始信号的实时分析，在三类应用中显著优于 UNCALLED 和 Sigmap。
摘要：
纳米孔测序仪在对长基因组链进行测序时，会实时产生电原始信号。这些原始信号可以在产生时即被分析，从而为实时基因组分析提供机会。纳米孔测序的一个重要特性 Read Until 能够在不完全测序链的情况下将其从测序仪中弹出，这为通过计算减少测序时间和成本提供了机会。然而，现有利用 Read Until 的工作要么：1）需要便携式测序仪可能无法提供的强大计算资源；要么 2）缺乏针对大型基因组的可扩展性，导致其不准确或无效。我们提出 RawHash，这是首个能够利用基于哈希的相似性搜索，对大型基因组的纳米孔原始信号准确且高效地进行实时分析的机制。为实现这一点，RawHash 确保对应相同 DNA 内容的信号产生相同的哈希值，而不受这些信号中轻微变化的影响。RawHash 通过对原始信号进行有效量化来实现准确的基于哈希的相似性搜索，使对应相同 DNA 内容的信号具有相同的量化值，并进而具有相同的哈希值。我们在三个应用上评估 RawHash：1）read 比对，2）相对丰度估计，以及 3）污染分析。我们的评估表明，RawHash 是唯一能够在实时分析大型基因组时同时提供高准确性和高吞吐量的工具。与最先进技术 UNCALLED 和 Sigmap 相比，RawHash 分别提供：1）25.8 倍和 3.4 倍的平均吞吐量提升，以及 2）针对大型基因组显著更好的准确性。源代码可在 https://github.com/CMU-SAFARI/RawHash 获取。
英文摘要：
Nanopore sequencers generate electrical raw signals in real-time 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) 25.8x and 3.4x better average throughput and 2) significantly better accuracy for large genomes, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
