Lactobacillus nasalidis YZ01 is a bacterium that was isolated from Probocis monkey, forestomach content.
genome sequence 16S sequence Bacteria| @ref 20215 |
|
|
| Domain Bacteria |
| Phylum Bacillota |
| Class Bacilli |
| Order Lactobacillales |
| Family Lactobacillaceae |
| Genus Lactobacillus |
| Species Lactobacillus nasalidis |
| Full scientific name Lactobacillus nasalidis Suzuki-Hashido et al. 2021 |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 67122 | MRS MEDIUM (DSMZ Medium 11) | Medium recipe at MediaDive | Name: MRS MEDIUM (DSMZ Medium 11) Composition: Glucose 20.0 g/l Casein peptone 10.0 g/l Meat extract 10.0 g/l Na-acetate 5.0 g/l Yeast extract 5.0 g/l (NH4)3 citrate 2.0 g/l K2HPO4 2.0 g/l Tween 80 1.0 g/l MgSO4 x 7 H2O 0.2 g/l MnSO4 x H2O 0.05 g/l Distilled water |
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|---|
| 67770 | ASM1658777v1 assembly for Lactobacillus nasalidis YZ01 | scaffold | 2797258 | 0 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 67770 | Lactobacillus nasalidis YZ01 gene for 16S ribosomal RNA, partial sequence | LC495593 | 1442 | 2797258 |
| @ref | GC-content (mol%) | Method | |
|---|---|---|---|
| 67770 | 51.6 | high performance liquid chromatography (HPLC) |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | oxygen_tolerance | BacteriaNetⓘ | obligate anaerobe | 98.05 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 84.96 | no |
| 125439 | motility | BacteriaNetⓘ | no | 81.59 | no |
| 125439 | spore_formation | BacteriaNetⓘ | no | 99.84 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | yes | 94.39 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 77.78 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 87.42 | no |
| 125438 | aerobic | aerobicⓘ | no | 96.90 | no |
| 125438 | thermophilic | thermophileⓘ | no | 94.27 | yes |
| 125438 | flagellated | motile2+ⓘ | no | 96.50 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Phylogeny | Lactobacillus nasalidis sp. nov., isolated from the forestomach of a captive proboscis monkey (Nasalis larvatus). | Suzuki-Hashido N, Tsuchida S, Hayakawa T, Sakamoto M, Azumano A, Seino S, Matsuda I, Ohkuma M, Ushida K | Int J Syst Evol Microbiol | 10.1099/ijsem.0.004787 | 2021 |
| #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 ) |
| #67122 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 110539 |
| #67770 | Japan Collection of Microorganism (JCM) ; Curators of the JCM; |
| #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 . |
| #126262 | A. Lissin, I. Schober, J. F. Witte, H. Lüken, A. Podstawka, J. Koblitz, B. Bunk, P. Dawyndt, P. Vandamme, P. de Vos, J. Overmann, L. C. Reimer: StrainInfo—the central database for linked microbial strain identifiers. ( DOI 10.1093/database/baaf059 ) |
You found an error in BacDive? Please tell us about it!
Note that changes will be reviewed and judged. If your changes are legitimate, changes will occur within the next BacDive update. Only proposed changes supported by the according reference will be reviewed. The BacDive team reserves the right to reject proposed changes.
Successfully sent
If you want to cite this particular strain cite the following doi:
https://doi.org/10.13145/bacdive160218.20260601.11
When using BacDive for research please cite the following paper
BacDive in 2025: the core database for prokaryotic strain data