LINKS: Scalable, alignment-free scaffolding of draft genomes with long reads
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Owing to the complexity of the assembly problem, we do not yet have complete genome sequences. The difficulty in assembling reads into finished genomes is exacerbated by sequence repeats and the inability of short reads to capture sufficient genomic information to resolve those problematic regions. In this regard, established and emerging long read technologies show great promise, but their current associated higher error rates typically require computational base correction and/or additional bioinformatics pre-processing before they can be of value. RESULTS: We present LINKS, the Long Interval Nucleotide K-mer Scaffolder algorithm, a method that makes use of the sequence properties of nanopore sequence data and other error-containing sequence data, to scaffold high-quality genome assemblies, without the need for read alignment or base correction. Here, we show how the contiguity of an ABySS Escherichia coli K-12 genome assembly can be increased greater than five-fold by the use of beta-released Oxford Nanopore Technologies Ltd. long reads and how LINKS leverages long-range information in Saccharomyces cerevisiae W303 nanopore reads to yield assemblies whose resulting contiguity and correctness are on par with or better than that of competing applications. We also present the re-scaffolding of the colossal white spruce (Picea glauca) draft assembly (PG29, 20 Gbp) and demonstrate how LINKS scales to larger genomes. CONCLUSIONS: This study highlights the present utility of nanopore reads for genome scaffolding in spite of their current limitations, which are expected to diminish as the nanopore sequencing technology advances. We expect LINKS to have broad utility in harnessing the potential of long reads in connecting high-quality sequences of small and large genome assembly drafts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it