Stigmatella erecta M26 is a mesophilic prokaryote that was isolated from poplar bark.
mesophilic genome sequence 16S sequence| @ref 20215 |
|
|
| Domain Pseudomonadati |
| Phylum Pseudomonadota |
| Class Deltaproteobacteria |
| Order Myxococcales |
| Family Archangiaceae |
| Genus Stigmatella |
| Species Stigmatella erecta |
| Full scientific name Stigmatella erecta (Schroeter 1886) McCurdy 1971 (Approved Lists 1980) |
| Synonyms (1) |
| @ref | Gram stain | Confidence | |
|---|---|---|---|
| 125438 | negative | 91.476 |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 6637 | VY/2 AGAR (DSMZ Medium 9) | Medium recipe at MediaDive | Name: VY/2 AGAR (DSMZ Medium 9) Composition: Agar 15.0 g/l Baker's yeast 5.0 g/l CaCl2 x 2 H2O 1.36 g/l Vitamin B12 0.0005 g/l Distilled water |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 6637 | positive | growth | 30 | mesophilic |
| @ref | Sample type | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|
| 6637 | poplar bark | Canada | CAN | North America |
Global distribution of 16S sequence KF267737 (>99% sequence identity) for Stigmatella from Microbeatlas ![]()
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|---|
| 66792 | IMG-taxon 2693429888 annotated assembly for Stigmatella erecta DSM 16858 | contig | 83460 | 65.83 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 20218 | Stigmatella erecta strain ATCC 25191 16S ribosomal RNA gene, partial sequence | DQ768128 | 1502 | 83460 | ||
| 20218 | Stigmatella erecta strain DSM 16858 16S ribosomal RNA gene, partial sequence | KF267737 | 1502 | 83460 | ||
| 6637 | Stigmatella erecta partial 16S rRNA gene, type strain DSM 16858T | AJ970180 | 1534 | 83460 |
| @ref | GC-content (mol%) | Method | |
|---|---|---|---|
| 6637 | 68.7 | thermal denaturation, midpoint method (Tm) |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 68.90 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 50.50 | no |
| 125439 | gram_stain | BacteriaNetⓘ | positive | 72.50 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | facultative anaerobe | 73.20 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | no | 91.48 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 92.71 | no |
| 125438 | aerobic | aerobicⓘ | yes | 76.55 | no |
| 125438 | spore-forming | spore-formingⓘ | no | 75.33 | no |
| 125438 | thermophilic | thermophileⓘ | no | 95.35 | yes |
| 125438 | flagellated | motile2+ⓘ | yes | 65.38 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Genetics | Comparative genomic insight into the myxobacterial carbohydrate-degrading potential. | Saraf N, Sharma G. | Front Microbiol | 10.3389/fmicb.2025.1550287 | 2025 | |
| Approach to analyze the diversity of myxobacteria in soil by semi-nested PCR-denaturing gradient gel electrophoresis (DGGE) based on taxon-specific gene. | Li B, Yao Q, Zhu H. | PLoS One | 10.1371/journal.pone.0108877 | 2014 |
| #6637 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 16858 |
| #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 ) |
| #20218 | Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.: StrainInfo introduces electronic passports for microorganisms.. Syst Appl Microbiol. 37: 42 - 50 2014 ( DOI 10.1016/j.syapm.2013.11.002 , PubMed 24321274 ) |
| #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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