Archaeoglobus fulgidus 7324 is a hyperthermophilic archaeon that was isolated from hot oil field water.
hyperthermophilic genome sequence Archaea|
|
| Domain Archaea |
| Phylum Methanobacteriota |
| Class Archaeoglobi |
| Order Archaeoglobales |
| Family Archaeoglobaceae |
| Genus Archaeoglobus |
| Species Archaeoglobus fulgidus |
| Full scientific name Archaeoglobus fulgidus Stetter 1988 |
| BacDive ID | Other strains from Archaeoglobus fulgidus (2) | Type strain |
|---|---|---|
| 18096 | A. fulgidus VC-16, DSM 4304, ATCC 49558, JCM 9628, NBRC ... (type strain) | |
| 18095 | A. fulgidus Z, DSM 4139 |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 24483 | ARCHAEOGLOBUS MEDIUM (DSMZ Medium 399) | Medium recipe at MediaDive | Name: ARCHAEOGLOBUS MEDIUM (DSMZ Medium 399) Composition: MgCl2 x 6 H2O 3.95257 g/l NaHCO3 2.96443 g/l Na-L-lactate 1.48221 g/l Na2S x 9 H2O 0.494071 g/l Yeast extract 0.494071 g/l KCl 0.335968 g/l NH4Cl 0.247036 g/l K2HPO4 0.13834 g/l MgSO4 x 7 H2O 0.0296443 g/l Nitrilotriacetic acid 0.0148221 g/l NaCl 0.00988142 g/l MnSO4 x H2O 0.00494071 g/l Fe(NH4)2(SO4)2 x 7 H2O 0.00197628 g/l CoSO4 x 7 H2O 0.00177866 g/l ZnSO4 x 7 H2O 0.00177866 g/l FeSO4 x 7 H2O 0.000988142 g/l CaCl2 x 2 H2O 0.000988142 g/l Sodium resazurin 0.000494071 g/l NiCl2 x 6 H2O 0.000296443 g/l AlK(SO4)2 x 12 H2O 0.000197628 g/l CuSO4 x 5 H2O 9.88142e-05 g/l Na2MoO4 x 2 H2O 9.88142e-05 g/l H3BO3 9.88142e-05 g/l Na2WO4 x 2 H2O 3.95257e-06 g/l Na2SeO3 x 5 H2O 2.96443e-06 g/l Distilled water |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 24483 | positive | growth | 80 | hyperthermophilic |
| Cat1 | Cat2 | Cat3 | |
|---|---|---|---|
| #Engineered | #Industrial | #Oil reservoir | |
| #Environmental | #Aquatic | - | |
| #Condition | #Thermophilic (>45°C) | - |
| @ref | Sample type | Geographic location | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|---|
| 24483 | hot oil field water | North Sea | Norway | NOR | Europe |
| @ref | Biosafety level | Biosafety level comment | |
|---|---|---|---|
| 24483 | 1 | Risk group (German classification) |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | yes | 93.60 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 87.20 | no |
| 125439 | gram_stain | BacteriaNetⓘ | variable | 87.00 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | anaerobe | 91.60 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | no | 65.94 | no |
| 125438 | anaerobic | anaerobicⓘ | yes | 85.82 | no |
| 125438 | aerobic | aerobicⓘ | no | 82.34 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 85.08 | no |
| 125438 | thermophilic | thermophileⓘ | yes | 69.25 | no |
| 125438 | flagellated | motile2+ⓘ | no | 86.71 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Genetics | Complete genome sequence analysis of Archaeoglobus fulgidus strain 7324 (DSM 8774), a hyperthermophilic archaeal sulfate reducer from a North Sea oil field. | Birkeland NK, Schonheit P, Poghosyan L, Fiebig A, Klenk HP | Stand Genomic Sci | 10.1186/s40793-017-0296-5 | 2017 |
| Culture collection no. | |
|---|---|
| DSM 8774 | |
| 7324 |
| #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 ) |
| #24483 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 8774 |
| #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) . |
| #91001 | Reimer, L.C., Lissin, A.,Schober, I., Witte,J.F., Podstawka, A., Lüken, H., Bunk, B.,Overmann, J.: StrainInfo: A central database for resolving microbial strain identifiers . ( DOI 10.60712/SI-ID48540.1 ) |
| #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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