Desulforamulus putei TH-11 is an anaerobe, thermophilic prokaryote that was isolated from 2.7 km deep terrestrial subsurface.
anaerobe thermophilic genome sequence 16S sequence| @ref 20215 |
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| Domain Bacteria |
| Phylum Bacillota |
| Class Clostridia |
| Order Eubacteriales |
| Family Peptococcaceae |
| Genus Desulforamulus |
| Species Desulforamulus putei |
| Full scientific name Desulforamulus putei (Liu et al. 1997) Watanabe et al. 2021 |
| Synonyms (1) |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 4629 | DESULFOVIBRIO (POSTGATE) MEDIUM (DSMZ Medium 63) | Medium recipe at MediaDive | Name: DESULFOVIBRIO (POSTGATE) MEDIUM (DSMZ Medium 63) Composition: MgSO4 x 7 H2O 2.0 g/l Na-DL-lactate 2.0 g/l NH4Cl 1.0 g/l Yeast extract 1.0 g/l Na2SO4 1.0 g/l FeSO4 x 7 H2O 0.5 g/l K2HPO4 0.5 g/l CaCl2 x 2 H2O 0.1 g/l Na-thioglycolate 0.1 g/l Ascorbic acid 0.1 g/l Sodium resazurin 0.0005 g/l Distilled water |
Global distribution of 16S sequence AF053929 (>99% sequence identity) for Desulfotomaculum putei from Microbeatlas ![]()
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|---|
| 66792 | IMG-taxon 2582580748 annotated assembly for Desulforamulus putei DSM 12395 | scaffold | 1121429 | 64.42 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 4629 | Desulfotomaculum putei strain SMCC W459 16S ribosomal RNA gene, partial sequence | AF053929 | 1361 | 74701 |
| @ref | GC-content (mol%) | Method | |
|---|---|---|---|
| 4629 | 48.9 | sequence analysis |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 65.30 | no |
| 125439 | motility | BacteriaNetⓘ | no | 59.80 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 75.00 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | anaerobe | 97.60 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | yes | 53.32 | no |
| 125438 | anaerobic | anaerobicⓘ | yes | 88.92 | no |
| 125438 | aerobic | aerobicⓘ | no | 94.68 | no |
| 125438 | spore-forming | spore-formingⓘ | yes | 76.05 | no |
| 125438 | thermophilic | thermophileⓘ | yes | 56.27 | yes |
| 125438 | flagellated | motile2+ⓘ | yes | 71.78 | no |
| #4629 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 12395 |
| #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 ) |
| #60499 | Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 55205 |
| #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/bacdive11823.20251217.10
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BacDive in 2025: the core database for prokaryotic strain data