RawHash2: Mapping Raw Nanopore Signals Using Hash-Based Seeding and Adaptive Quantization

TL;DR

RawHash2 advances real-time nanopore signal analysis by improving hash-based mapping accuracy and throughput through enhanced quantization, filtering, and support for new data formats and flow cells.

Contribution

RawHash2 introduces significant improvements over RawHash, including enhanced sensitivity, weighted mapping, frequency filtering, minimizers, and support for R10.4 flow cells and multiple data formats.

Findings

10.57% average F1 accuracy improvement

4.0x average throughput increase

Supports R10.4 flow cell and various data formats

Abstract

Summary: Raw nanopore signals can be analyzed while they are being generated, a process known as real-time analysis. Real-time analysis of raw signals is essential to utilize the unique features that nanopore sequencing provides, enabling the early stopping of the sequencing of a read or the entire sequencing run based on the analysis. The state-of-the-art mechanism, RawHash, offers the first hash-based efficient and accurate similarity identification between raw signals and a reference genome by quickly matching their hash values. In this work, we introduce RawHash2, which provides major improvements over RawHash, including a more sensitive quantization and chaining implementation, weighted mapping decisions, frequency filters to reduce ambiguous seed hits, minimizers for hash-based sketching, and support for the R10.4 flow cell version and various data formats such as POD5 and SLOW5. Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57% and up to 20.25%) and better throughput (on average by 4.0x and up to 9.9x) than RawHash. Availability and Implementation: RawHash2 is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.