Semi-Automatic In Silico Gap Closure Enabled De Novo Assembly of Two Dehalobacter Genomes from Metagenomic Data
Bibliographic record
Abstract
Typically, the assembly and closure of a complete bacterial genome requires substantial additional effort spent in a wet lab for gap resolution and genome polishing. Assembly is further confounded by subspecies polymorphism when starting from metagenome sequence data. In this paper, we describe an in silico gap-resolution strategy that can substantially improve assembly. This strategy resolves assembly gaps in scaffolds using pre-assembled contigs, followed by verification with read mapping. It is capable of resolving assembly gaps caused by repetitive elements and subspecies polymorphisms. Using this strategy, we realized the de novo assembly of the first two Dehalobacter genomes from the metagenomes of two anaerobic mixed microbial cultures capable of reductive dechlorination of chlorinated ethanes and chloroform. Only four additional PCR reactions were required even though the initial assembly with Newbler v. 2.5 produced 101 contigs within 9 scaffolds belonging to two Dehalobacter strains. By applying this strategy to the re-assembly of a recently published genome of Bacteroides, we demonstrate its potential utility for other sequencing projects, both metagenomic and genomic.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".