Use of Low-Coverage, Large-Insert, Short-Read Data for Rapid and Accurate Generation of Enhanced-Quality Draft Pseudomonas Genome Sequences
Bibliographic record
Abstract
Next-generation genomic technology has both greatly accelerated the pace of genome research as well as increased our reliance on draft genome sequences. While groups such as the Genomics Standards Consortium have made strong efforts to promote genome standards there is a still a general lack of uniformity among published draft genomes, leading to challenges for downstream comparative analyses. This lack of uniformity is a particular problem when using standard draft genomes that frequently have large numbers of low-quality sequencing tracts. Here we present a proposal for an "enhanced-quality draft" genome that identifies at least 95% of the coding sequences, thereby effectively providing a full accounting of the genic component of the genome. Enhanced-quality draft genomes are easily attainable through a combination of small- and large-insert next-generation, paired-end sequencing. We illustrate the generation of an enhanced-quality draft genome by re-sequencing the plant pathogenic bacterium Pseudomonas syringae pv. phaseolicola 1448A (Pph 1448A), which has a published, closed genome sequence of 5.93 Mbp. We use a combination of Illumina paired-end and mate-pair sequencing, and surprisingly find that de novo assemblies with 100x paired-end coverage and mate-pair sequencing with as low as low as 2-5x coverage are substantially better than assemblies based on higher coverage. The rapid and low-cost generation of large numbers of enhanced-quality draft genome sequences will be of particular value for microbial diagnostics and biosecurity, which rely on precise discrimination of potentially dangerous clones from closely related benign strains.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".