Pre-assembly NGS correction of ONT reads achieves HiFi-level assembly quality
Bibliographic record
Abstract
Recently developed hybrid assemblies can achieve telomere-to-telomere (T2T) completeness of some chromosomes. However, such approaches involve sequencing a large volume of both Pacific Biosciences high-fidelity (HiFi) and Oxford Nanopore Technologies (ONT) sequencing reads. Along with this, third-generation sequencing techniques are rapidly advancing, increasing the available length and accuracy. To reduce the final cost of genome assembly, here we investigated the possibility of assembly from low-coverage samples and with only ONT corrected by next-generation sequencing (NGS) sequencing reads. We demonstrated that haploid ONT-based assembly approaches corrected by NGS can achieve performance metrics comparable to more expensive hybrid approaches based on HiFi sequencing. We investigated the assembly of different chromosomes and the low-coverage performance of state-of-the-art hybrid assembly tools, including Verkko and Hifiasm, as well as ONT-based assemblers such as Shasta and Flye. We found that even with one-contig T2T assembly Verkko and Hifiasm still have numerous misassemblies within centromere. Therefore, we recommend using a combination of regular R9 or simplex R10 ONT reads and accurate NGS reads for assembly without aiming for T2T completeness. Additionally, we rigorously evaluated the performance of MGI, Illumina, and stLFR NGS technologies across various aspects of hybrid genome assembly, including pre-assembly correction, haplotype phasing, and polishing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".