Complete and validated genomes from a metagenome
Bibliographic record
Abstract
0.1 Abstract The assembly and binning of metagenomically-assembled genomes (MAGs) using Illumina sequencing has improved the genomic characterization of unculturable communities. However, short-read-only metagenomic assemblies rarely result in completed genomes because of the difficulty assembling repetitive regions. Here, we present a strategy to complete and validate multiple MAGs from a bacterial community using a combination of short and ultra long reads (N50 > 25 kb). Our strategy is to perform an initial long read-only metagenomic assembly using metaFlye, followed by multiple rounds of polishing using both long and short reads. To validate the genomes, we verified that longs reads spanned the regions that were not supported by uniquely mapped paired-end Illumina sequences. We obtained multiple complete genomes from a naphthenic acid-degrading community, including one from the recently proposed Candidate Phyla Radiation. The majority of the population is represented by the assembled genomes; recruiting 63.77 % of Nanopore reads, and 64.38 % of Illumina reads. The pipeline we developed will enable researchers to validate genomes from metagenomic assemblies, increasing the quality of metagenomically assembled genomes through additional scrutiny.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".