ArticleBioinformatics (Oxford, England)2024
RawHash2: mapping raw nanopore signals using hash-based seeding and adaptive quantization.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Hash functions in nucleotide sequence analysis.Genome research · 2026Review
- SquiDBase: a community resource of raw nanopore data from microbes.NAR genomics and bioinformatics · 2026Article
- Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.Bioinformatics (Oxford, England) · 2026Article
- Campolina: a deep neural framework for accurate segmentation of nanopore signals.Genome biology · 2026Article
- A Hitchhiker's Guide to long-read genomic analysis.Genome research · 2025Review
- FPGA-based accelerator for adaptive banded event alignment in nanopore sequencing data analysis.BMC bioinformatics · 2025Article
- TargetCall: eliminating the wasted computation in basecalling via pre-basecalling filtering.Frontiers in genetics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
summaryRaw 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 more sensitive quantization and chaining algorithms, 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 POD5 and SLOW5 file formats. Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57% and up to 20.25%) and better throughput (on average by 4.0× and up to 9.9×) 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.