Name: Peptoniphilus faecalis AGMB00490
Creator: BacDive
License: https://creativecommons.org/licenses/by/4.0/

Strain identifier

BacDive ID: 166552

Type strain:

Species: Peptoniphilus faecalis

Strain Designation: AGMB00490

Culture col. no.: KCTC 15944, NBRC 114159

NCBI tax ID(s): 2731255 (species)

Special Collections

KCTC

Section

When using BacDive for research please cite our paper

Peptoniphilus faecalis AGMB00490 is a bacterium of the family Peptoniphilaceae.

16S sequence
Bacteria
genome sequence

Information on the name and the taxonomic classification. Name and taxonomic classification

	Last LPSN update	09-12-2024 (DD-MM-YYYY)
	Domain	*Bacteria*
	Phylum	*Bacillota*
	Class	*Clostridia*
	Order	*Eubacteriales*
	Family	*Peptoniphilaceae*
	Genus	*Peptoniphilus*
	Species	**Peptoniphilus faecalis**
	Full Scientific Name (LPSN)	Peptoniphilus faecalis Ryu et al. 2021

Synonym

Peptoniphilus porci

Information on physiology and metabolism Physiology and metabolism

[Ref.: #125438]

Oxygen tolerance

anaerobe Confidence in %92.192

Information on isolation source, the sampling and environmental conditions Isolation, sampling and environmental information

[Ref.: #69479]

Global distribution of 16S sequence MT396160 (>99% sequence identity) for Peptoniphilus from Microbeatlas

Aquatic Samples	198
Soil Samples	114
Animal Samples	8752
Plant Samples	82
Total Samples	9146

Information on genomic background e.g. entries in nucleic sequence databass Sequence information

16S Sequence information:

	Sequence accession description	Seq. accession number	Sequence length (bp)	Sequence database	Associated NCBI tax ID
[Ref.: #20215]	Peptoniphilus faecalis strain AGMB00490 16S ribosomal RNA gene, partial sequence	MT396160	1498	GenBank ENA	2731255 tax ID

Genome sequence information:

	Sequence accession description	Seq. accession number	Assembly level	Sequence database	Associated NCBI tax ID
[Ref.: #66792]	Peptoniphilus faecalis AGMB00490	GCA_012976305	contig	GenBank ENA	2731255 tax ID	*

Data predicted using genome information as a basis Genome-based predictions

	Model	Trait	Prediction	Confidence in %	In training data
	Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Peptoniphilus faecalis AGMB00490 NCBI: GCA_012976305.1
[Ref.: #125438]	gram-positive	gram-positiveⓘ	yes	80.95	no
[Ref.: #125438]	anaerobic	anaerobicⓘ	yes	92.192	no
[Ref.: #125438]	aerobic	aerobicⓘ	no	96.353	no
[Ref.: #125438]	spore-forming	spore-formingⓘ	no	66.794	no
[Ref.: #125438]	thermophile	thermophilicⓘ	no	93.416	no
[Ref.: #125438]	motile2+	flagellatedⓘ	no	87.143	no

	Model	Trait	Prediction	Confidence in %
	Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Peptoniphilus faecalis AGMB00490 NCBI: GCA_012976305
[Ref.: #125439]	BacteriaNet	spore_formationⓘ	no	72
[Ref.: #125439]	BacteriaNet	motilityⓘ	yes	54.9
[Ref.: #125439]	BacteriaNet	gram_stainⓘ	positive	80.8
[Ref.: #125439]	BacteriaNet	oxygen_toleranceⓘ	obligate anaerobe	80.2

Availability in culture collections External links

[Ref.: #20215]

Culture collection no.

KCTC 15944, NBRC 114159

Literature:

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Peptoniphilus faecalis sp. nov., isolated from swine faeces.	Ryu SW, Kim JS, Oh BS, Yu SY, Lee JS, Park SH, Kang SW, Lee J, Lee MK, Rhee MS, Jung H, Hur TY, Kim HB, Kim JK, Lee JH, Lee JH	Int J Syst Evol Microbiol	10.1099/ijsem.0.004836	2021	*

References

#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 )

#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 .

* These data were automatically processed and therefore are not curated

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