Neobacillus jeddahensis JCE is a mesophilic prokaryote that was isolated from human feces, 24-year-old obese man.
mesophilic genome sequence 16S sequence| @ref 20215 |
|
|
| Domain Bacillati |
| Phylum Bacillota |
| Class Bacilli |
| Order Caryophanales |
| Family Cytobacillaceae |
| Genus Neobacillus |
| Species Neobacillus jeddahensis |
| Full scientific name Neobacillus jeddahensis (Bittar et al. 2017) Patel and Gupta 2020 |
| Synonyms (1) |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 22175 | NUTRIENT AGAR (DSMZ Medium 1) | Medium recipe at MediaDive | Name: NUTRIENT AGAR (DSMZ Medium 1) Composition: Agar 15.0 g/l Peptone 5.0 g/l Meat extract 3.0 g/l Distilled water |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 22175 | positive | growth | 30 | mesophilic |
| @ref | Spore formation | Confidence | |
|---|---|---|---|
| 125438 | 92.904 |
| Cat1 | Cat2 | Cat3 | |
|---|---|---|---|
| #Host | #Human | #Male | |
| #Host Body Product | #Gastrointestinal tract | #Feces (Stool) |
| @ref | Sample type | Geographic location | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|---|
| 22175 | human feces, 24-year-old obese man | Jeddah | Saudi Arabia | SAU | Asia |
Global distribution of 16S sequence HG931339 (>99% sequence identity) for Neobacillus jeddahensis subclade from Microbeatlas ![]()
| @ref | Biosafety level | Biosafety level comment | |
|---|---|---|---|
| 22175 | 1 | Risk group (German classification) |
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|---|
| 66792 | JCE assembly for Neobacillus jeddahensis JCE | contig | 1461580 | 48.73 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 22175 | Bacillus sp. JCE partial 16S rRNA gene, isolate JCE | HG931339 | 1517 | 1461580 |
| @ref | GC-content (mol%) | Method | |
|---|---|---|---|
| 22175 | 39.42 | sequence analysis |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | yes | 69.80 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 61.40 | no |
| 125439 | gram_stain | BacteriaNetⓘ | variable | 81.80 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | obligate aerobe | 84.10 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | yes | 76.11 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 95.73 | no |
| 125438 | aerobic | aerobicⓘ | yes | 55.52 | no |
| 125438 | spore-forming | spore-formingⓘ | yes | 92.90 | no |
| 125438 | thermophilic | thermophileⓘ | no | 89.31 | no |
| 125438 | flagellated | motile2+ⓘ | yes | 73.47 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Genetics | Neobacillus massiliamazoniensis sp. nov., a new bacterial species isolated from stool sample of an inhabitant of the Amazon region. | Mbaye B, Tidjani Alou M, Fadlane A, Fregiere L, Alibar S, Million M, Fenollar F, Lo CI. | New Microbes New Infect | 10.1016/j.nmni.2021.100900 | 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 ) |
| #22175 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 28281 |
| #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) . |
| #69479 | João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.: MicrobeAtlas 1.0 beta . |
| #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 ) |
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https://doi.org/10.13145/bacdive130866.20251217.10
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BacDive in 2025: the core database for prokaryotic strain data