Complete genome sequence of <i>Paracoccus denitrificans</i> ATCC 19367 and its denitrification characteristics
Bibliographic record
Abstract
Studies show that Paracoccus denitrificans can denitrify nitrogen sources under aerobic conditions. However, the lack of data on its genome sequence has restricted molecular studies and practical applications. In this study, the complete genome of P. denitrificans ATCC 19367 was sequenced and its nitrogen metabolism properties were characterized. The size of the whole genome is 5 242 327 bp, with two chromosomes and one plasmid. The average G + C content is 66.8%, and it contains 5308 protein-coding genes, 54 tRNA genes, and nine rRNA operons. Among the protein-coding genes, 71.35% could be assigned to the Gene Ontology (GO) pathway, 86.66% to the Clusters of Orthologous Groups (COG) pathway, and 50.57% to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Comparative genome analysis between P. denitrificans ATCC 19367 and P. denitrificans PD1222 revealed that there are 428 genes specific to ATCC 19367 and 4738 core genes. Furthermore, the expression of genes related to denitrification, biofilm formation, and nitrogen metabolism (nar, nir, and nor) by P. denitrificans ATCC 19367 under aerobic conditions was affected by incubation time and shaking speed. This study elucidates the genomic background of P. denitrificans ATCC 19367 and suggests the possibility of controlling nitrogen pollution in the environment by using this bacterium.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".