Pseudomonas putida SQ1 is a mesophilic human pathogen that was isolated from littoral sediment.
mesophilic human pathogen genome sequence| @ref 20215 |
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| Domain Pseudomonadati |
| Phylum Pseudomonadota |
| Class Gammaproteobacteria |
| Order Pseudomonadales |
| Family Pseudomonadaceae |
| Genus Pseudomonas |
| Species Pseudomonas putida |
| Full scientific name Pseudomonas putida (Trevisan 1889) Migula 1895 (Approved Lists 1980) |
| Synonyms (2) |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 21480 | REACTIVATION WITH LIQUID MEDIUM 1 (DSMZ Medium 1a) | Medium recipe at MediaDive | Name: REACTIVATION WITH LIQUID MEDIUM 1 (DSMZ Medium 1a) Composition: Agar 15.0 g/l Peptone 5.0 g/l Meat extract 3.0 g/l Distilled water | ||
| 21480 | TRYPTICASE SOY BROTH AGAR (DSMZ Medium 535) | Medium recipe at MediaDive | Name: TRYPTICASE SOY BROTH AGAR (DSMZ Medium 535) Composition: Trypticase soy broth 30.0 g/l Agar 15.0 g/l Distilled water | ||
| 21480 | LB (Luria-Bertani) MEDIUM (DSMZ Medium 381) | Medium recipe at MediaDive | Name: LB (Luria-Bertani) MEDIUM (DSMZ Medium 381) Composition: Agar 20.0 g/l NaCl 10.0 g/l Tryptone 10.0 g/l Yeast extract 5.0 g/l Distilled water |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 21480 | positive | growth | 28 | mesophilic |
| @ref | Sample type | Geographic location | Country | Country ISO 3 Code | Continent | Latitude | Longitude | |
|---|---|---|---|---|---|---|---|---|
| 21480 | littoral sediment | Lake Constance | Germany | DEU | Europe | 47.6958 | 9.193 47.6958/9.193 |
| @ref | Pathogenicity human | Pathogenicity animal | Biosafety level | Biosafety level comment | |
|---|---|---|---|---|---|
| 21480 | 2 | Risk group (German classification) |
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | |
|---|---|---|---|---|---|---|---|
| 66792 | ASM80256v1 assembly for Pseudomonas putida SQ1 | contig | 303 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 98.00 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 83.10 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 98.30 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | obligate aerobe | 95.40 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | no | 98.50 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 99.07 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 89.60 | no |
| 125438 | aerobic | aerobicⓘ | yes | 94.18 | no |
| 125438 | thermophilic | thermophileⓘ | no | 98.49 | yes |
| 125438 | flagellated | motile2+ⓘ | yes | 90.61 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Permanent draft genome sequence of sulfoquinovose-degrading Pseudomonas putida strain SQ1. | Felux AK, Franchini P, Schleheck D. | Stand Genomic Sci | 10.1186/s40793-015-0033-x | 2015 | ||
| Identification of Microorganisms from Several Surfaces by MALDI-TOF MS: P. aeruginosa Is Leading in Biofilm Formation. | Asghari E, Kiel A, Kaltschmidt BP, Wortmann M, Schmidt N, Husgen B, Hutten A, Knabbe C, Kaltschmidt C, Kaltschmidt B. | Microorganisms | 10.3390/microorganisms9050992 | 2021 | ||
| Metabolism | Entner-Doudoroff pathway for sulfoquinovose degradation in Pseudomonas putida SQ1. | Felux AK, Spiteller D, Klebensberger J, Schleheck D. | Proc Natl Acad Sci U S A | 10.1073/pnas.1507049112 | 2015 |
| #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 ) |
| #21480 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 100120 |
| #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 . |
| #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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