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opencc-by-4.0Jan 2022View details →

Diel vertical migration promotes prokaryotic diversity in the Red Sea mesopelagic

ABSTRACT: The diel vertical migration (DVM) of fish provides an active transport of labile dissolved organic matter (DOM) to the deep ocean, fueling the metabolism of heterotrophic bacteria and archaea. We studied the impact of DVM on the mesopelagic prokaryotic diversity of the Red Sea focusing on the mesopelagic deep scattering layer (DSL) between 450-600 m. Despite the general consensus of homogeneous conditions in the twilight zone, we observed variability in physico-chemical variables and distinct seasonal indicator prokaryotes inhabiting the DSL, representing between 2% (summer) to over 10% (winter) of total sequences. The DSL samples diverged from the surrounding mesopelagic waters in multidimensional scaling analysis and were distributed according to depth (47% of variance explained). We identified the sources of diversity that contribute to the DSL using spring depth profiles. On average, 7% was related to probable sinking from the epipelagic, 34% was common among the other mesopelagic waters and 38% was attributable to the DVM, with 21% of species being unique to the DSL. We conclude that the mesopelagic physico-chemical properties shape a rather uniform prokaryotic community, but that the 200 m wide DSL contributes uniquely and in a high proportion to the diversity of the Red Sea mesopelagic. The raw 16S sequences used in this research article are available at <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB49545">https://www.ebi.ac.uk/ena/browser/view/PRJEB49545</a> as 67 paired fastq sequences with consecutive accession numbers: ERX7411972 – ERX7412038. The 2 files stored in this repository represent: a) the clean 16S sequences count and taxonomic affiliation (SILVA132 Database) and b) the metadata associated to each of the 67 samples (lat, long, temperature, salinity, nutrient concentrations, bacterial abundance, bacterial size, etc)

opencc-by-4.0May 2022View details →

AMPSphere : the worldwide survey of prokaryotic antimicrobial peptides

AMPSphere v.2022-03: the worldwide survey of prokaryotic antimicrobial peptides   INTRODUCTION AMPSphere is a comprehensive catalog of antimicrobial peptides predicted using Macrel (DOI: <a href="https://peerj.com/articles/10555/">10.7717/peerj.10555</a>) from 63,410 public metagenomes, <a href="http://progenomes.embl.de/">ProGenomes v2.2 database</a> (82,400 high-quality microbial genomes), and c.a. 4k non-whitelisted microbial genomes from NCBI. Currently, AMPSphere is available as a web resource at <a href="https://ampsphere.big-data-biology.org/">https://ampsphere.big-data-biology.org/</a>.   GENERATION Peptides were predicted using <a href="https://www.big-data-biology.org/software/macrel/">Macrel</a>. Singleton peptides were removed, except those with a direct hit to <a href="http://dramp.cpu-bioinfor.org/">DRAMP database</a>. Redundant peptides were coded using a reduced alphabet and hierarchically clustered using CD-HIT (version 4.6) at 100%, 85%, and 75% of amino acid identity (and 90% of overlap of the shorter peptide). The obtained clusters were numbered by decreasing size (number of peptides). Each level of clustering was called a SPHERE. Redundant nucleotide sequences for the gene variants of different AMPs also were included in this version of AMPSphere.   STATISTICS AMPSphere v.2022-03 contains 863,498 sequences (avg length: 36 amino acids, range 8-98). DRAMP database was used to find confirmed sequences with strict homology to reference. This approach showed that 2,488 peptides were previously confirmed in our dataset.   IDENTIFIERS Peptides are named in the form >AMP10.XXX_XXX where XXX_XXX is a unique numerical identifier (starting at zero). Numbers were assigned in order of increasing number of copies. So that the lower the number, the greater number of copies of that peptide were present in the input data. Annotations were also provided as separated fields in the fasta file, containing their: - SPHERE families at level III (corresponding to hierarchically obtained clusters using 100-85-75% of identity with a minimum overlap of 90% of the shorter gene). Example of header: <pre><code class="language-bash">>AMP10.000_000 | SPHERE-III.001_493 </code></pre> VERSION DETAILS Version 2022-03 includes: - quality assessment of documented AMPs, - metadata associated with the genes, - a better taxonomic identification of AMP sources using GTDB. WARNING: Due to a different procedure of AMP sorting, now some entries and families may have changed their accessions.   FILES WITHIN THIS VERSION README.md This file.   AMPsphere_v.2022-03.fna.xz Multi-fasta with AMPSphere gene sequences (nucleotide).   AMPsphere_v.2022-03.faa.gz Multi-fasta with AMPSphere peptide sequences (amino acid).   SPHERE_v.2022-03.levels_assessment.tsv.gz TSV table relating AMP name and the hierarchically obtained clusters per level. Columns: - AMP accession - evaluation vs. representative - SPHERE_fam level I - SPHERE_fam level II - SPHERE_fam level III Levels of each SPHERE family: I: contains clusters obtained with 100% of identity cut-off and 90% of overlap of the shorter sequence; II: contains clusters obtained with the unclustered sequences and the representatives from level I at 85% of identity and 90% of overlap of the sorter sequence; III: contains clusters obtained with the unclustered sequences and the representatives from level II at 75% of identity and 90% of overlap of the sorter sequence; `evaluation vs. representatives` shows the percent of identity the sequence has in an alignment against the cluster representative, and also the overlap in percent. Example:     * -- This means: this sequence is a cluster representative. OR something like this:     77.50%,1:40:1:40 -- This means: alignment identity against the                         representative of the cluster equals 77.5% and the                         alignment start and end position for the query (1 and                         40, respectively), and target (1 and 40, respectively).   AMPSphere_v.2022-03.quality_assessment.tsv.gz TSV table containing the results of each quality test (by sequence). Columns: - AMP ID - Antifam - RNAcode - Metaproteomes - Metatranscriptomes - Coordinates Results are one of 'Passed', 'Failed', or 'Not tested'. <a href="https://www.ebi.ac.uk/research/bateman/software/antifam-tool-identify-spurious-proteins">Antifam </a>results show if the sequence matches ('Fail') or does not match ('Pass') to Antifams, a set of well-known spurious ORFs. <a href="https://github.com/ViennaRNA/RNAcode">RNAcode</a> relies on gene diversity, therefore, families with less than 3 different gene sequences could not be tested and were marked as such. The direct match of 50% of our peptide to transcripts (in at least 2 different samples) or peptides from meta-omics studies sampled from different environments assigned the peptide as passing the metatranscriptomes and metaproteomes tests, respectively. Finally, the coordinates test check if the start of the small ORF happens with at least one stop codon upstream, this ensures that the gene is not a fragment from a larger protein.   AMPSphere_v.2022-03.general_geneinfo.tsv.gz TSV table relating AMP, gene name, the microbial source, sample, environment, and geographical location. Columns: - gmsc (gene code access)     - amp     - sample (biosample) - source (microbial origin, GTDB taxonomy) - specI (species cluster according to ProGenomes v.2 classification) - is_metagenomic (False if comming from a high-quality microbial genome) - geographic_location - latitude - longitude - general_envo_name - environment_material   CONTACT You can contact us via our discussion group: <a href="https://groups.google.com/g/ampsphere-users">https://groups.google.com/g/ampsphere-users</a> AMPsphere main developers: - <a href="mailto:celio@big-data-biology.com?subject=AMPSphere%20v.2022-03&body=Dear%20Celio%2C%20%0A%0ARegarding%20AMPSphere%20v.2022-03.">Célio Dias Santos Júnior</a> - <a href="mailto:yiqian@big-data-biology.org?subject=AMPSphere%20v2022-03&body=Dear%20Yiqian%2C%20%0A%0ARegarding%20AMPSphere%20v2022-03.%0A">Yiqian Duan</a> - <a href="mailto:hui@big-data-biology.org?subject=AMPSphere%20v2022-03&body=Dear%20Hui%2C%20%0A%0ARegarding%20AMPSphere%20v.2022-03.">Hui Chong</a> - <a href="mailto:luispedro@big-data-biology.com?subject=AMPSphere%20v.2022-03&body=Dear%20Luis%2C%20%0A%0ARegarding%20AMPSphere%20v.2022-03.">Luis Pedro Coelho</a> COPYRIGHT NOTICE AMPSphere v.2022-03 - the worldwide survey of prokaryotic antimicrobial peptides. This work is a joint effort of Big Data Biology group from the Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI) - Fudan University, Shanghai, China, and the Structural and Computational Biology Unit (Heidelberg) - European Molecular Biology Laboratory (EMBL). Copyright (C) 2019-2022 The Authors    AMPSphere IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES    OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.    IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,    DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR    OTHERWISE,ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE    USE OR OTHER DEALINGS IN THE SOFTWARE.    This database is free; you can redistribute it and/or modify it    as you wish, under the terms of the CC BY 4.0 license.    You are allowed to:    Share — copy and redistribute the material in any medium or format    Adapt — remix, transform, and build upon the material for any purpose,            even commercially.    You may also obtain a copy of the CC BY 4.0 license here:        https://creativecommons.org/licenses/by/4.0/ REFERENCES CITED - Macrel: Santos-Júnior CD, Pan S, Zhao X, Coelho LP. 2020. Macrel: antimicrobial peptide screening in genomes and metagenomes. PeerJ 8:e10555. https://doi.org/10.7717/peerj.10555 - ProGenomes: Mende DR, Letunic I, Maistrenko OM et al. 2020. proGenomes2: an improved database for accurate and consistent habitat, taxonomic and functional annotations of prokaryotic genomes.  Nucleic Acids Research 48(D1): D621–D625. https://doi.org/10.1093/nar/gkz1002 - DRAMP: Kang X, Dong F, Shi C et al. 2019. DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci Data 6, 148. https://doi.org/10.1038/s41597-019-0154-y - ANTIFAM: Eberhardt RY, Haft DH, Punta M, Martin M, O’Donovan C, BatemanA. 2012. AntiFam: a tool to help identify spurious ORFs in protein annotation. Database, Bas003. - RNAcode: Washietl S, Findeiss S, Müller SA, Kalkhof S, von Bergen M, Hofacker IL, Stadler PF, Goldman N. 2011. RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data. RNA 17(4):578-94.

opencc-zeroAug 2024View details →

Prokaryote: Prokaryote Phenome Data

Phenomic data about prokaryote taxa. Data from Blank CE (2009) Data from: Not so old Archaea - the antiquity of biogeochemical processes in the archaeal domain of life. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.71r61. Please cite the original publication and the dryad data package.Phenomic data about prokaryote taxa. Data from Blank CE (2009) Data from: Not so old Archaea - the antiquity of biogeochemical processes in the archaeal domain of life. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.71r61. Please cite the original publication and the dryad data package.

opencc-by-4.0Nov 2022View details →

Reconstruction of prokaryotic genomes from ten termite gut metagenomes using two distinct workflows: SnakeMAGs and ATLAS.

SnakeMAGs (Nachida Tadrent, Franck Dedeine, Vincent Hervé (Submitted). SnakeMAGs: a simple, efficient, flexible and scalable workflow to reconstruct prokaryotic genomes from metagenomes. <a href="https://doi.org/10.5281/zenodo.7303463">https://doi.org/10.5281/zenodo.7303463</a>; https://github.com/Nachida08/SnakeMAGs) is a workflow for building MAGs (Metagenome Assembled Genomes) from raw Illumina metagenomic reads. During the test phase of the development of this tool, a comparative analysis with another workflow called ATLAS v2.9.1 (Kieser et al, 2020) was performed. To compare these two workflows, we analyzed ten publicly available termite gut metagenomes (accession numbers: SRR10402454; SRR14739927; SRR8296321; SRR8296327; SRR8296329; SRR8296337; SRR8296343; DRR097505; SRR7466794; SRR7466795) from five different studies : Waidele et al, 2019; Tokuda et al, 2018; Romero Victorica et al, 2020; Moreira et al, 2021; and Calusinska et al, 2020. In this repository, we provide the configuration files that were used to launch each of the workflows (SnakeMAGs_config.yaml and ATLAS_config.yaml),  as well as the obtained results, i.e. the MAGs reconstructed from each metagenome and their taxonomic classification.

opencc-zeroJan 2022View details →

Planet Microbe Functional and Taxonomic annotation of Illumina WGS Prokaryotic Fraction for Semantic Web Analysis

Functional and Taxonomic annotations computed from a subset of Illumina Whole-Genome Sequencing samples from the prokaryotic fraction of the <a href="https://www.planetmicrobe.org/">Planet Microbe</a> database. Data was computed using the pipeline available from https://github.com/hurwitzlab/planet-microbe-functional-annotation/, and post processing scripts from https://github.com/hurwitzlab/planet-microbe-semantic-web-analysis. Files contain total annotation counts of Interpro, GO and NCBITaxon annotations, as well as additional sample metadata. See readme.txt file for more information.

edi44/100

Inventory of soil prokaryotic microbiome (via 16S based on rRNA gene amplicons) in freshwater and brackish water marshes following saltwater intrusion along Shark River Slough boundary, Everglades National Park (FCE LTER), Florida, USA, September 2018

Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased episodic or chronic saltwater exposure, leading to shifts in microbial communities and related ecological services. Soil prokaryotes play a fundamental role in regulating important biogeochemical processes in coastal wetland ecosystem. Yet, it is still difficult to predict how soil prokaryotic communities respond to the saltwater exposure because of poorly understood prokaryotic sensitivity within complex wetland soil microbial communities, as well as the high heterogeneity of wetland soils and saltwater exposure. To address this, a four-year experimental simulation of saltwater intrusion in a pristine freshwater site and a previously saltwater-impacted site was conducted. The saltwater addition started in October 2014 on a monthly basis and continued through October 2018. The dataset contains amplicon sequencing date of 16S rRNA gene obtained from saltwater-exposed soils and unmanipulated native soils in both sites (collected in September 2018). The 2018 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804545 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804545). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-Habbena, W. (2023). Year-around survey and manipulation experiments reveal differential sensitivities of soil prokaryotic and fungal communities to saltwater intrusion in Florida Everglades wetlands. Science of The Total Environment, 858, 159865. https://doi.org/10.1016/j.scitotenv.2022.159865 Instead of citing this package, which is an

openCC (other)Feb 2024View details →

edi44/100

Inventory of soil prokaryotic and fungal microbiome (via 16S rRNA gene amplicons and ITS sequencing) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, February 2019 - October 2020

Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased saltwater exposure, leading to change in soil microbial communities and many important biogeochemical processes. Given the high spatial and temporal heterogeneity in coastal wetlands, especially in tropical or subtropical climates characterized by seasonal temperature, precipitation, and tidal fluctuations, it remains unclear which environmental factors influence the compositions of soil microbial communities in wetlands affected by varying degrees of sea-water intrusion. To address this, a two-year survey was conducted on microbial community structure in submerged surface soils from 14 wetland sites across the Florida Everglades, representing three major ecosystem types, i.e. freshwater marshes, mangrove forests, and seagrass meadows. Bulk surface soil samples of each site were collected from February 2019 to October 2020 to cover dry and wet seasons. In addition to bulk soil samples, soil cores were collected from each site in August 2020 to assess vertical gradients of microbial communities. The dataset contains amplicon sequencing data of 16S rRNA gene (both bulk soil and soil cores) and ITS gene (only the bulk soil). The 2019 to 2020 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804243 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804243), PRJNA804246 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804246), and PRJNA804228 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804228). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-H

openCC (other)Feb 2024View details →

dryad40/100

Effects of tidal influence on the structure and function of prokaryotic communities in the sediments of a pristine Brazilian mangrove

Mangrove forests are ecosystems that constitute a large portion of the world's coastline and span tidal zones below, between, and above the waterline, while the ecosystem as a whole is defined by the health of these tidal microhabitats. However, we are only beginning to understand tidal zone microbial biodiversity and the role of these microbiomes in nutrient cycling. While extensive research has characterized microbiomes in pristine versus anthropogenically impacted mangroves these have, largely, overlooked differences in tidal microhabitats (sublittoral, intertidal, and supralittoral). Unfortunately, the small number of studies that have sought to characterize mangrove tidal zones have occurred in impacted biomes, making interpretation of the results difficult. Here, we characterized prokaryotic populations and their involvement in nutrient cycling across the tidal zones of a pristine mangrove within a Brazilian Environmental Protection Area of the Atlantic Forest. We hypothesized that the tidal zones in pristine mangroves are distinct microhabitats, which we defined as distinct regions that present spatial variations in the water regime and other environmental factors, and as such, these are composed of different prokaryotic communities with distinct functional profiles. Samples were collected in triplicate from zones below, between, and above the tidal waterline. Using 16S rRNA gene amplicon sequencing, we found distinct prokaryotic communities with significantly diverse nutrient cycling functions, as well as specific taxa with varying contribution to functional abundances between zones. Where previous research from anthropogenically impacted mangroves found the intertidal zone to have high prokaryotic diversity and functionally enriched in nitrogen cycling, we find that the intertidal zone from pristine mangroves have the lowest diversity and no functional enrichment, relative to the other tidal zones. The main bacterial phyla in all samples were Firmicutes, Proteobacteria and Chloroflexi while the main archaeal phyla were Crenarchaeota and Thaumarchaeota. Our results differ slightly from other studies where Proteobacteria is the main phyla in mangrove sediments and Firmicutes make up for only a small percentage of the communities. Salinity and organic matter were the most relevant environmental factors influencing these communities. Bacillaceae was the most abundant family at each tidal zone and showed potential to drive a large proportion of the cycling of carbon, nitrogen, phosphorus and sulfur. Our findings suggest that some aspects of mangrove tidal zonation may be compromised by human activity, especially in the intertidal zone.

opencc-zeroJul 2020View details →

opencc-by-4.0Sep 2020View details →

Phylogenomic analyses of 142 prokaryotic genera

This repository contains 142 tar archive files, each corresponding to a prokaryotic genus. Each archive contains the following files/directories: <ul> <li><code>accn.tax.tsv         </code>   a tab-delimited file containing the assembly accession (col 1) and an associated taxon name (col 2) for each selected genome (one per line)</li> <li><code>gff/                 </code>   a directory containing a gzip-compressed <a href="https://m.ensembl.org/info/website/upload/gff3.html">GFF3</a> file (as returned by the annotation tool <a href="https://github.com/tseemann/prokka">Prokka</a>) for each genome specified in <code>accn.tax.tsv</code></li> <li><code>cds.fna.gz           </code>   a gzip-compressed FASTA file containing all the coding sequences (at the codon level) from the GFF3 files in the directory <code>gff/</code></li> <li><code>msa/                 </code>   a directory containing multiple amino acid and codon sequence alignments (compressed FASTA files with extensions .afa.gz and .afc.gz, respectively) for each cluster of at least four homologous sequences (as determined by the pipeline <a href="https://github.com/sanger-pathogens/Roary">Roary</a> from the GFF3 files in <code>gff/</code>)</li> <li><code>supermatrix.fasta.gz </code>   a gzip-compressed FASTA file obtained by concatenating all the multiple codon sequence alignments in the directory <code>msa/</code></li> <li><code>tree.nwk             </code>   a <a href="https://evolution.genetics.washington.edu/phylip/newicktree.html">Newick</a>-formatted file containing a maximum-likelihood (ML) phylogenetic tree inferred from the file <code>supermatrix.fasta.gz</code> using <a href="http://www.iqtree.org/">IQ-TREE</a></li> <li><code>iqtree.txt           </code>   a txt file summarizing the ML estimates of the GTR+Γ evolutionary model parameters, as returned by IQ-TREE when inferring the phylogenetic tree <code>tree.nwk</code></li> </ul>   A summary of the 142 phylogenomic analyses can be found in the tab-delimited file <a href="https://zenodo.org/record/4034261/files/GTR.params.trees.tsv?download=1"><code>GTR.params.tree.tsv</code></a>. Each line corresponds to one genus and contains the 12 following fields:   <code>[1]   </code>   genus name,   <code>[2-5] </code>   frequencies of T, C, A, G, respectively,   <code>[6-10]</code>  C-T, A-T, G-T, A-C, C-G rate parameters, respectively (normalized such that A-G rate = 1),   <code>[11]  </code>   Γ shape parameter alpha,   <code>[12]  </code>   Newick-formatted phylogenetic tree. _____ Criscuolo A (2020) On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference. F1000Research, 9:1309. <a href="https://doi.org/10.12688/f1000research.26930.1">doi:10.12688/f1000research.26930.1</a>

opencc-zeroJan 2016View details →

Prokaryotic TFBS Dataset

A collection of prokaryotic transcription factor binding site motifs gleaned from the CollecTF, MtbRegList, RegTransBase, RegulonDB, DBTBS, and CoryneRegNet databases.  Motifs filtered for >= 10 sites, > 5 bits information content.

opencc-by-4.0Jun 2022View details →

Data from: Plant community stability is associated with a decoupling of prokaryote and fungal soil networks

Data from the manuscript Plant community stability is associated with a decoupling of prokaryote and fungal soil networks: https://doi.org/10.1101/2022.06.21.496867

Fig. 2 in A report of 12 unrecorded prokaryotic species isolated from gastrointestinal tracts and feces of various endangered animals in Korea

Fig. 2. Phylogenetic tree based on 16S rRNA gene sequence comparisons, showing the relationship between the isolated strains in this study and the notable species from phylum Firmicutes (a) order Lactobacillales (In particular Enterococcus, Lactobacillus and Vagococcus), phylum Actinobacteria (c) and phylum Proteobacteria (d) and. The trees were mainly reconstructed using the neighbor-joining algorithm (NJ), Maximum parsimony (MP) and maximum likelihood (ML) algorithms were applied for additional comparison. Filled diamonds indicate branches present in the phylogenetic trees generated using the three different methods. Numbers on the nodes (>70%) represent bootstrap values as percentages of 1000 replicates (NJ/MP/ML). Clostridium butyricum DSM 10702T (AQQF01000149), Bifidobacterium bifidum ATCC 29521T (KE993182) and Spirochaeta aurantia subsp. aurantia DSM 1902T (FR749896) were used as outgroups, respectively. Bar, 0.02 (a, c, d) and 0.01 (b) accumulated changes per nucleotide.

Fig. 1 in A report of 12 unrecorded prokaryotic species isolated from gastrointestinal tracts and feces of various endangered animals in Korea

Fig. 1. Transmission electron micrographs of the isolated strains. Strain-1, AE4-1; strain-2, B3; strain-3, M3; strain-4, VM3408; strain-5, VT2418; strain-6, VM2501; strain-7, VT2414; strain-8, VT2504.

Fig. 3 in A report of 18 unrecorded prokaryotic species isolated from the feces of an Oriental stork (Ciconia boyciana), and from the intestinal tracts of a cobitid fish (Kichulchoia multifasciata) and a Korean splendid dace (Coreoleuciscus splendidus)

Fig. 3. Phylogenetic tree based on 16S rRNA gene sequence comparisons, showing the relationship between the seven strains belonging to the phylum Firmicutes isolated in this study and the notable type species from each genus. The tree was reconstructed using neighbor joining (NJ), maximum likelihood (ML), and maximum parsimony (MP) algorithms. Filled diamonds indicate corresponding branches present in the phylogenetic tree generated using the three different tree construction methods. Numbers at the nodes represent bootstrap values of more than 70% are shown, based on 1000 replicates (NJ/MP/ML). Limnochorda pilosa HC45T (AP014924) was used as the outgroup. The bar indicates 0.02 accumulated substitutions per nucleotide.

Fig. 4 in A report of 18 unrecorded prokaryotic species isolated from the feces of an Oriental stork (Ciconia boyciana), and from the intestinal tracts of a cobitid fish (Kichulchoia multifasciata) and a Korean splendid dace (Coreoleuciscus splendidus)

Fig. 4. Phylogenetic tree based on 16S rRNA gene sequence comparisons, showing the relationship between the two strains belonging to the phylum Proteobacteria isolated in this study and the notable type species from each family. The tree was reconstructed using neighbor joining (NJ), maximum likelihood (ML), and maximum parsimony (MP) algorithms. Filled diamonds indicate corresponding branches present in the phylogenetic tree generated using the three different tree construction methods. Numbers at the nodes represent bootstrap values of more than 70% are shown, based on 1000 replicates (NJ/MP/ML). Bacteroides fragilis NCTC 9343T (NR_074784.2) was used as the outgroup. The bar indicates 0.05 accumulated substitutions per nucleotide.

Fig. 2 in A report of 18 unrecorded prokaryotic species isolated from the feces of an Oriental stork (Ciconia boyciana), and from the intestinal tracts of a cobitid fish (Kichulchoia multifasciata) and a Korean splendid dace (Coreoleuciscus splendidus)

Fig. 2. Phylogenetic tree based on 16S rRNA gene sequence comparisons, showing the relationship between the eight strains belonging to the phylum Actinobacteria isolated in this study and the notable type species from each genus. The tree was reconstructed using neighbor joining (NJ), maximum likelihood (ML), and maximum parsimony (MP) algorithms. Filled diamonds indicate corresponding branches present in the phylogenetic tree generated using the three different tree construction methods. Numbers at the nodes represent bootstrap values of more than 70% are shown, based on 1000 replicates (NJ/MP/ML). Akkermansia muciniphila ATCC BAA-835T (NR_074436.1) was used as the outgroup. The bar indicates 0.02 accumulated substitutions per nucleotide.

Fig. 1 in A report of 18 unrecorded prokaryotic species isolated from the feces of an Oriental stork (Ciconia boyciana), and from the intestinal tracts of a cobitid fish (Kichulchoia multifasciata) and a Korean splendid dace (Coreoleuciscus splendidus)

Fig. 1. Transmission electron micrographs of the strains isolated in this study. The arrows indicate flagella. Strains: 1, H11M7; 2, H11M9; 3, H23M10; 4, H21T1; 5, H13T1; 6, H43T7; 7, H21T20; 8, H23M25; 9, H23M9; 10, S13R1; 11, H13R26; 12, H11M5; 13, H11M15; 14, H11R12; 15, H21T7; 16, H11R21; 17, M13M1; 18, H23T21.

Fig. 5 in A report of 18 unrecorded prokaryotic species isolated from the feces of an Oriental stork (Ciconia boyciana), and from the intestinal tracts of a cobitid fish (Kichulchoia multifasciata) and a Korean splendid dace (Coreoleuciscus splendidus)

Fig. 5. Phylogenetic tree based on 16S rRNA gene sequence comparisons, showing the relationship between strain H23T21 and the notable type species from the family Sphingobacteriaceae. The tree was reconstructed using neighbor joining (NJ), maximum likelihood (ML), and maximum parsimony (MP) algorithms. Filled diamonds indicate corresponding branches present in the phylogenetic tree generated using the three different tree construction methods. Numbers at the nodes represent bootstrap values of more than 70% are shown, based on 1000 replicates (NJ/MP/ML). Filobacterium rodentium SMR-CT (LC055729) was used as the outgroup. The bar indicates 0.05 accumulated substitutions per nucleotide.

opencc-by-4.0Feb 2021View details →

Fig. 3 in Isolation and identification of 18 unrecorded prokaryotic species from the intestinal tracts of aquatic animals in Korea

Fig. 3. The phylogenetic tree based on 16S rRNA gene sequence showing the phylogenetic relatedness between the four isolated strains belonging to the phylum Actinobacteria and the type species from each genus. The tree was mainly reconstructed using the NJ, ML, and MP algorithms. The filled diamonds indicate the branches present in the phylogenetic trees reconstructed with three different tree reconstruction methods. Numbers on the nodes represent bootstrap values with 1000 replicates (NJ/ML/MP). B. fragilis NCTC 9434T was used as an outgroup. The bar indicates 0.05 accumulated substitutions per nucleotide.