Mucilaginibacter celer HYN0043 is a prokaryote of the family Sphingobacteriaceae.
genome sequence| @ref 20215 |
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| Domain Pseudomonadati |
| Phylum Bacteroidota |
| Class Sphingobacteriia |
| Order Sphingobacteriales |
| Family Sphingobacteriaceae |
| Genus Mucilaginibacter |
| Species Mucilaginibacter celer |
| Full scientific name Mucilaginibacter celer Kim et al. 2020 |
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|---|
| 66792 | ASM357645v2 assembly for Mucilaginibacter celer HYN0043 | complete | 2305508 | 98.21 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 98.60 | no |
| 125439 | motility | BacteriaNetⓘ | no | 69.10 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 99.70 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | obligate aerobe | 98.20 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | no | 95.96 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 96.51 | no |
| 125438 | aerobic | aerobicⓘ | yes | 86.31 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 83.65 | no |
| 125438 | thermophilic | thermophileⓘ | no | 96.24 | no |
| 125438 | flagellated | motile2+ⓘ | no | 84.25 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Phylogeny | Mucilaginibacter celer sp. nov. and Aquirhabdus parva gen. nov., sp. nov., isolated from freshwater. | Kim M, Shin SK, Yi H | Int J Syst Evol Microbiol | 10.1099/ijsem.0.004437 | 2020 |
| #20215 | Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.: List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. IJSEM ( DOI 10.1099/ijsem.0.004332 ) |
| #66792 | Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg Overmann: Automatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information) . |
| #125438 | Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann: Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. 2024 ( DOI 10.1101/2024.08.12.607695 ) |
| #125439 | Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardy: deepG: Deep Learning for Genome Sequence Data. R package version 0.3.1 . |
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If you want to cite this particular strain cite the following doi:
https://doi.org/10.13145/bacdive167443.20251217.10
When using BacDive for research please cite the following paper
BacDive in 2025: the core database for prokaryotic strain data