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Rawsamble: Overlapping and Assembling Raw Nanopore Signals using a Hash-based Seeding Mechanism

Can Firtina, Maximilian Mordig, Harun Mustafa, Sayan Goswami, Nika Mansouri Ghiasi, Stefano Mercogliano, Furkan Eris, Joël Lindegger, Andre Kahles, Onur Mutlu

TL;DR

Rawsamble enables all-vs-all overlapping of raw nanopore signals without basecalling, addressing the lack of reference genomes to identify similarities directly between reads. It extends hash-based seeding (based on RawHash2) with aggressive noise filtering, longer-anchored chaining, and a deterministic mapping strategy, producing Pairwise Mapping Format (PAF) overlaps that can feed de novo assemblers like miniasm. The authors demonstrate substantial speedups and memory reductions compared to a CPU-based Dorado basecalling plus minimap2 workflow, while achieving overlaps that are substantially similar to those found by basecalled approaches in many cases. They also show the first de novo assemblies from raw signal overlaps, with unitigs up to 2.3 Mb in E. coli, illustrating the potential for end-to-end raw-signal genome analysis and new directions for basecalling and assembly. Limitations include lack of reverse-complement handling and challenges for dynamic real-time indexing and graph updates, which point to future work.

Abstract

Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e., without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw signal comparison. Our goal is to enable the direct analysis of raw signals without a reference genome. To this end, we propose Rawsamble, the first mechanism that can identify regions of similarity between all raw signal pairs, known as all-vs-all overlapping, using a hash-based search mechanism. We use these overlaps to construct de novo assembly graphs with an existing assembler, miniasm, off-the-shelf. To our knowledge, these are the first de novo assemblies ever constructed directly from raw signals without basecalling. Our extensive evaluations across multiple genomes of varying sizes show that Rawsamble provides a significant speedup (on average by 5.01x and up to 23.10x) and reduces peak memory usage (on average by 5.74x and up to by 22.00x) compared to a conventional genome assembly pipeline using the state-of-the-art tools for basecalling (Dorado's fastest mode) and overlapping (minimap2) on a CPU.We find that around one-third of Rawsamble 's overlapping pairs are also found by minimap2. We find that when we use overlapping reads from Rawsamble, we can construct unitigs that are 1) as accurate as those built from minimap2's overlaps and 2) up to half a chromosome in length (e.g., 2.3 million bases for E. coli). Source code: https://github.com/CMU-SAFARI/RawHash

Rawsamble: Overlapping and Assembling Raw Nanopore Signals using a Hash-based Seeding Mechanism

TL;DR

Rawsamble enables all-vs-all overlapping of raw nanopore signals without basecalling, addressing the lack of reference genomes to identify similarities directly between reads. It extends hash-based seeding (based on RawHash2) with aggressive noise filtering, longer-anchored chaining, and a deterministic mapping strategy, producing Pairwise Mapping Format (PAF) overlaps that can feed de novo assemblers like miniasm. The authors demonstrate substantial speedups and memory reductions compared to a CPU-based Dorado basecalling plus minimap2 workflow, while achieving overlaps that are substantially similar to those found by basecalled approaches in many cases. They also show the first de novo assemblies from raw signal overlaps, with unitigs up to 2.3 Mb in E. coli, illustrating the potential for end-to-end raw-signal genome analysis and new directions for basecalling and assembly. Limitations include lack of reverse-complement handling and challenges for dynamic real-time indexing and graph updates, which point to future work.

Abstract

Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e., without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw signal comparison. Our goal is to enable the direct analysis of raw signals without a reference genome. To this end, we propose Rawsamble, the first mechanism that can identify regions of similarity between all raw signal pairs, known as all-vs-all overlapping, using a hash-based search mechanism. We use these overlaps to construct de novo assembly graphs with an existing assembler, miniasm, off-the-shelf. To our knowledge, these are the first de novo assemblies ever constructed directly from raw signals without basecalling. Our extensive evaluations across multiple genomes of varying sizes show that Rawsamble provides a significant speedup (on average by 5.01x and up to 23.10x) and reduces peak memory usage (on average by 5.74x and up to by 22.00x) compared to a conventional genome assembly pipeline using the state-of-the-art tools for basecalling (Dorado's fastest mode) and overlapping (minimap2) on a CPU.We find that around one-third of Rawsamble 's overlapping pairs are also found by minimap2. We find that when we use overlapping reads from Rawsamble, we can construct unitigs that are 1) as accurate as those built from minimap2's overlaps and 2) up to half a chromosome in length (e.g., 2.3 million bases for E. coli). Source code: https://github.com/CMU-SAFARI/RawHash

Paper Structure

This paper contains 19 sections, 3 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Overview
  4. Constructing an Index from Noisy Raw Signals via Aggressive Filtering
  5. Adjusting the Chaining Mechanism for Overlapping
  6. Adjusting the Mapping Strategy
  7. Results
  8. Evaluation Methodology
  9. Performance and Memory
  10. All-vs-All Overlapping Statistics
  11. Contiguity and Estimated Accuracy of the Assembly Graphs
  12. Discussion and Future Work
  13. Conclusion
  14. RawHash2 Overview
  15. Estimating the AWS Cloud Computing Costs
  16. ...and 4 more sections

Figures (3)

  • Figure 1: Overview of Rawsamble. We use colors for the inputs, steps, and outputs to highlight the parts that Rawsamble modifies over RawHash2.
  • Figure 2: Filtering in Rawsamble. Values in gray boxes show the filtered signals as their values are close to the previous signal (in a blue box) that is not filtered out.
  • Figure S1: Overview of RawHash2.