MinION sequencing from sea ice cryoconites leads to de novo genome reconstruction from metagenomes
Bibliographic record
Abstract
Genome reconstruction from metagenomes enables detailed study of individual community members, their metabolisms, and their survival strategies. Obtaining high quality metagenome-assembled genomes (MAGs) is particularly valuable in extreme environments like sea ice cryoconites, where the native consortia are recalcitrant to culture and strong astrobiology analogues. We evaluated three separate approaches for MAG generation from Allen Bay, Nunavut sea ice cryoconites-HiSeq-only, MinION-only, and hybrid (HiSeq + MinION)-where field MinION sequencing yielded a reliable metagenome. The hybrid assembly produced longer contigs, more coding sequences, and more total MAGs, revealing a microbial community dominated by Bacteroidetes. The hybrid MAGs also had the highest completeness, lowest contamination, and highest N50. A putatively novel species of Octadecabacter is among the hybrid MAGs produced, containing the genus's only known instances of genomic potential for nitrate reduction, denitrification, sulfate reduction, and fermentation. This study shows that the inclusion of MinION reads in traditional short read datasets leads to higher quality metagenomes and MAGs for more accurate descriptions of novel microorganisms in this extreme, transient habitat and has produced the first hybrid MAGs from an extreme environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".