Find research datasets worth reusing

opencc-by-4.0Feb 2021View details →

Fig. 5 in Isolation and identification of 18 unrecorded prokaryotic species from the intestinal tracts of aquatic animals in Korea

Fig. 5. The phylogenetic tree based on 16S rRNA gene sequence showing the phylogenetic relatedness between the three isolated strains belonging to the phylum Firmicutes 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 using three different tree reconstruction methods. Numbers on the nodes represent bootstrap values with 1000 replicates(NJ/ML/MP). E. coli ATCC 11775T was used as an outgroup. The bar indicates 0.05 accumulated substitutions per nucleotide.

opencc-by-4.0Feb 2019View details →

Fig. 2 in Unrecorded prokaryotic species belonging to the class Actinobacteria in Korea

Fig. 2. Neighbor-joining phylogenetic tree, based on 16S rRNA gene sequences, showing the relationship between the strains isolated in this study and their relatives of the class Actinobacteria. Bootstrap values (>70%) are shown above nodes. Filled circles indicate the nodes recovered by three other treeing methods including Maximum-likelihood, Maximum-parsimony and Neighbor-joining. Bar, 0.01 substitutions per nucleotide position.

opencc-by-4.0Feb 2019View details →

Fig. 1 in Unrecorded prokaryotic species belonging to the class Actinobacteria in Korea

Fig. 1. Transmission electron micrographs or scanning electron micrographs of cells of the strains isolated in this study. Strains: 1, CAU 1470; 2, CAU 1475; 3, NA_1; 4, HC_48; 5, D7-24; 6, GH1-18; 7, GH1-39; 8, Gsoil 1173; 9, 17J72-9; 10, IMCC34147; 11, 17J28-11; 12, 17J49-8; 13, 17J49-11; 14, Ibu_O_11; 15, Ibu_O_21; 16, MMS17-SY291; 17, LT2304; 18, JMn2; 19, JMn10; 20, WD9; 21, LM3301; 22, KYW1377; 23, Gsoil 335; 24, Gsoil 1175; 25, BE2-18; 26, Gsoil 1130; 27, 17J48-16; 28, Gsoil 006; 29, Gsoil 262; 30, MMS17-SY284; 31, MMS17-SY227; 32, MMS17-GJ001; 33, Gsoil 961; 34, Gsoil 1526; 35, Gsoil 554.

opencc-by-4.0Feb 2021View details →

Fig. 1 in Isolation and identification of 18 unrecorded prokaryotic species from the intestinal tracts of aquatic animals in Korea

Fig. 1. The energy-filtering transmission electron micrographs of the isolated strains. Strains: 1, K33R8; 2, K33T6; 3, G12R3; 4, R13S1; 5, R23R4; 6, R33M6-1; 7, R33M4; 8, K11M4; 9, O13M9; 10, 719; 11, 176; 12, 775; 13, 765; 14, 684; 15, A52; 16, 771; 17, A79; 18, 772.

opencc-by-4.0Jul 2023View details →

Tara Prokaryote Annotated 16s OTU Counts

"Tara Oceans systematically collected ~35,000 samples for morphological, genetic, and environmental analyses using standardized protocols across multiple depths at global scale, aiming to facilitate a holistic study on how environmental factors and biogeochemical cycles affect oceanic life." (1) "For each prokaryote-enriched sample (N=139), we extracted metagenomic merged Illumina reads (miTAGs) that contained signatures of the 16S rRNA gene (Logares et al. 2013). These fragments were mapped to a set of 16S reference sequences that were downloaded from the SILVA database (Release 115: Quast et al. 2013) and clustered into 97% operational taxonomic units. The OTU count table was summarized at different taxonomic levels." (2) (1): https://www-science-org.offcampus.lib.washington.edu/doi/full/10.1126/science.1261359 (2): http://ocean-microbiome.embl.de/companion.html

opencc-by-4.0May 2023View details →

Data and code for 'Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes'

This repository contains the code and files for reproducing the analyses and results reported in 'Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes' by Gavin M. Douglas and B. Jesse Shapiro (<a href="https://doi.org/10.1038/s41559-023-02268-6">https://doi.org/10.1038/s41559-023-02268-6</a>). File organization and descriptions: <ul> <li>code/ - Contains GitHub repository releases of code used in manuscript (the other folders contain datafiles only). This code is provided here as well as on GitHub to ensure long-term access. <ul> <li>handy_pop_gen-1.1.0/ - release v1.1.0 of the convenience repository (used for specific data processing and analysis steps referred to in the manuscript).</li> <li>pangenome_pseudogene_null-1.1.0/ - Main code repository for manuscript.</li> </ul> </li> </ul>   <ul> <li>broad_pangenome_analysis/ <ul> <li>element_info/element_counts.tsv.gz - Counts of (filtered) pseudogenes and intact genes called per genome accession.</li> <li>element_info/gene_sizes.tsv.gz - Gene sizes in base-pairs.</li> <li>element_info/pseudogene_sizes.tsv.gz - Filtered pseudogene sizes in base-pairs.</li> <li>element_info/element_percent_coverage/*tsv.gz - Tables containing the percent genome coverage of genes and pseudogenes, by accession and averaged over accessions per species separately.</li> <li>example_Mycoplasmopsis_bovis_panaroo_output.csv.gz - Panaroo output table for Mycoplasmopsis bovis, which was used for an example. Corresponds to the gene_presence_absence.csv file in the raw Panaroo output.</li> <li>focal_and_non.focal_full_to_short.tsv.gz - Mapfile of full to short (and unique) species ids used in analysis. Primarily to include species ids in cluster names without making them unnecessarily long.</li> <li>genome_info/accessions.tsv.gz - Genome accessions used for broad pangenome analysis (note that not all genome accessions could be downloaded [and were ignored], which is indicated in the "could_download" column).</li> <li>genome_info/genome_sizes.tsv.gz - Sizes of all genomes used for the broad pangenome analysis.</li> <li>metrics_additional_subsamples.tsv.gz - Contains columns also found in the pangenome_and_related_metrics.tsv.gz file below, but based on genome subsamplings of 3 and 20, rather than 9.</li> <li>model_output/pangenome_linear_models.rds - R Data Serialization files containing the output of R linear model objects (generated by lm and provided as an R list object). There are separate elements in the list for the mean number of genes, genomic fluidity, percentage singletons (si), and si/sp.</li> <li>model_output/linear_model_coef.tsv.gz - Coefficient summary table for all linear models.</li> <li>pangenome_and_related_metrics.tsv.gz - Metrics used for broad pangenome analysis across 670 prokaryotic species. Note that this table was filtered down to 668 species after excluding those with < 9 genomes.</li> <li>pangenome_and_related_metrics_filt.tsv.gz - Filtered table, as described above.</li> <li>taxonomy.tsv.gz - Taxonomy for all species used for this analysis, taken from GTDB. Row names are species names.</li> </ul> </li> </ul>   <ul> <li>indepth_10_species_analysis/ <ul> <li>cluster_breakdown_tables/ - Folder containing tables providing breakdown of how clusters are distributed by element type, pangenome partition, and species. Provided for easy plotting.</li> <li>cluster_COG_annot.tsv.gz - Mapping of cluster IDs to COG annotations.</li> <li>cluster_filt_lengths_and_additional.tsv.gz - Metadata on clusters, most pertinently the length of the representative sequence in the cluster (which was used to filter out some clusters, below the cut-off which pseudogenes could not be called).</li> <li>cluster_member_breakdown.tsv.gz - Table providing information on each element (called pseudogenes and intact genes) and provides information such as what cluster they are part of, what species and genome accession they are found in, etc.</li> <li>cluster_types.rds - R Data Serialization file containing R list providing breakdown of all clusters into categories (intact/pseudogene/mixed, where mixed means containing both pseudogene and intact elements).</li> <li>COG_enrichment_results/ultra.cloud-COG-gene-enrichments.tsv.gz - Output file with enrichment test summaries for COG IDs in significant COG categories, which was run for the ultra-cloud pangenome partition model only.</li> <li>element_glmm_input.tsv.gz - Table containing all information used for fitting generalized linear mixed models.</li> <li>focal_species.txt - Names of species used for the in-depth analysis.</li> <li>genome_info/ - Folder containing the genome accessions (and the corresponding genome sizes) for all ten analyzed species.</li> <li>glmm_output/ - Folder containing R Data Serialization files containing output R objects after fitting generalized linear mixed models (only ultra-rare files are present, due to file size constraints).</li> <li>per_genome_element.type_percent_coverages.rds - R Data Serialization file containing R list providing the percent coverage by intact genes vs pseudogenes per accession (nested by species)</li> </ul> </li> </ul>

opencc-by-4.0Oct 2023View details →

Multi-method gene clusters at species-level resolution for 125 prokaryotic pangenomes

This dataset contains 9 sets of species-level gene clusters and high-resolution species trees for 125 representative bacterial and archaeal species, encompassing a total of 6,851 nearly complete genomes. Each set represents a different approach to homology-, orthology-, and synteny-based gene clustering as implemented by 6 popular tools for comparative genomics and pangenome analysis (Roary, panX, OrthoFinder, MMseqs2/PanACoTa, CD-HIT, and eggNOG-mapper). For Escherichia coli, Cutibacterium acnes, Bacteroides uniformis, and Staphylococcus epidermidis, we provide additional sets that combine high-quality genomes with different proportions of medium- and low-quality metagenome-assembled genomes (MAGs). This dataset is a helpful resource for benchmarking gene clustering tools and pangenome analysis workflows, as well as for testing their robustness with respect to the presence of incomplete or contaminated genomic assemblies. Reference: Manzano-Morales S, Liu Y, González-Bodí S, Huerta-Cepas J, Iranzo J. 2022. Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses. bioRxiv doi: <a href="https://doi.org/10.1101/2022.09.25.509376">10.1101/2022.09.25.509376</a>

dryad40/100

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

Open the record for dataset details and reuse information.

publicMar 2021View details →

opencc-by-nd-4.0Dec 2015View details →

Data and metadata dealing with prokaryote and viral abundances from a variety of ecosystems

Data from 210 articles and five unpublished studies were used for the meta-analysis. Articles were gathered using on-line databases (ScienceDirect, Wiley Online Library, Springer Link, PubMed and Google), using the keywords "virus-to-prokaryote ratio", "virus-to-bacterium ratio", "VPR", "VBR" and "viral abundance". Interesting reports were also found within the references of publications dealing with viral ecology. Publications were chosen according to the data they contained with a priority on articles containing VPR values (184 out of the 210 publications and four out of five unpublished studies) and viral abundance data. Articles lacking information on VPR and viral numbers were discarded (e.g. reports with only prokaryotic abundance, but lacking data on viral numbers, etc.), as the focus was on the ratio between viruses and prokaryotes. When data of interest were not available in the analysed reports, authors were contacted for more detail. When VPR values were not cited as such given within a publication, they were calculated according to the given viral and prokaryotic abundances provided therein. The file corresponding to our database was created by KJ Parikka. The doi was obtained by S Jacquet with INRA services.

opencc-by-4.0Aug 2023View details →

Oxygen concentration profiles data and code from: Langet et al., 2023, Single-celled bioturbators: benthic foraminifera mediate oxygen penetration and prokaryotic diversity in intertidal sediment

This archive contains the data of measured oxygen profile and the scripts used to generate the oxygen surface fluxes for the articleLanglet, D., Mermillod-Blondin, F., Deldicq, N., Bauville, A., Duong, G., Konecny, L., Hugoni, M., Denis, L., and Bouchet, V. M. P.: Single-celled bioturbators: benthic foraminifera mediate oxygen penetration and prokaryotic diversity in intertidal sediment, Biogeosciences, 20, 4875–4891, https://doi.org/10.5194/bg-20-4875-2023, 2023.

opencc-by-4.0Feb 2024View details →

Advancing source tracking: systematic review and source-specific genome database curation of fecally shed prokaryotes

This repository contains several files describing the data discussed in Lindner et al's "Advancing source tracking: systematic review and source-specific genome database curation of fecally shed prokaryotes".  <ol> <li>"database.fna" = concatenation of the (draft or complete) genome sequences described in the paper (n=12,730 source associated prokaryotic genomes) which passed quality checks and are species-level representatives (i.e., dereplicated at 95% ANI).</li> <li>"gdef.txt" = a manifest describing which sequences belong to which genomes.</li> <li>"sources.txt" = a manifest describing which genomes belong to which sources.</li> </ol> The sources this database covers: <table> <tbody> <tr> <td>Source Category</td> <td>Species-level genome count</td> <td>Source-specific species-level  genome count</td> </tr> <tr> <td>Bird</td> <td>56</td> <td>40</td> </tr> <tr> <td>Cat</td> <td>86</td> <td>6</td> </tr> <tr> <td>Chicken</td> <td>1314</td> <td>887</td> </tr> <tr> <td>Cow</td> <td>39</td> <td>15</td> </tr> <tr> <td>Dog</td> <td>139</td> <td>56</td> </tr> <tr> <td>Pig</td> <td>2764</td> <td>2035</td> </tr> <tr> <td>Ruminant</td> <td>740</td> <td>714</td> </tr> <tr> <td>Human</td> <td>4484</td> <td>3350</td> </tr> <tr> <td>Wastewater</td> <td>3108</td> <td>3097</td> </tr> </tbody> </table>   See publication for further details.

opencc-by-4.0Jun 2024View details →

Supplementary Data for "Phylogeny and evolution of dissimilatory sulfite reduction in Prokaryotes"

Supplementary Data for "Phylogeny and evolution of dissimilatory sulfite reduction in Prokaryotes" Supplementary Data 1: The information of the genomes we used in our analyses. The “DsrAB” sheet included the information of DsrAB-containing genomes and denoted whether DsrC, DsrM, DsrK, DsrJ, DsrO, DsrP genes were present. The other sheets included the information of the representative genus-level genomes which comprised DsrAB. These genomes were used to reconstruct genus-level phylogenies to compare tree topologies within phylum. See the “Note” sheet for more details. Supplementary Data 2: The raw data of Ka/Ks values of each gene pair estimated by KaKs_calculator v2.0 with MA method. Supplementary Data 3: the information of the COGs of iron-reducers and SRMs, respectively. Supplementary Data 4: fasta files used for tree reconstruction, including Dsr-related proteins and concatenated sequences for phylogenomic trees. The folder names indicated their analytical purposes. Supplementary Data 5: tree files used in our analyses, including Dsr trees, genetic distance-based trees and dated phylogenetic trees. The folder names indicated their analytical purposes.

opencc-by-4.0Jun 2024View details →

In silico mock communities for evaluation of taxonomic profilers across prokaryotes and viruses

In silico mock communities generated with CAMISIM for benchmarking the performance of taxonomic profilers across prokaryotic (50 communities), eukaryotic (30 communities), and viral communities (10 communities) of the human microbiome. Metagenomes were generated using CAMISIM (Fritz et al., 2019), which simulates 2.1 Gb of Illumina 2 ×150 bp paired end reads with the default HiSeq 2500 error profile and a mean insert size of 200 bp. To assess profiling performance for a range of sequencing depths, the 50 in silico metagenomes were also rarefied with seqtk (-s100) to sequencing depths of 20, 5, 2, 1, 0.5, 0.25 and 0.1 million read pairs. Counts are provided for rarefied metagenomes. Prokaryotic communities For prokaryotic benchmarking, 10 body site-representative prokaryotic metagenomes were simulated for each of the following five body sites: adult gut, infant gut, oral, skin, and vagina. Genome accession ids for prokaryotic species found in each human body site were identified from published literature (Bäckhed et al., 2015; Proctor et al., 2019; Saheb Kashaf et al., 2021).  Adult Gut: pro_gut_adult.zip Infant Gut: pro_gut_infant.zip Oral: pro_oral.zip Skin: pro_skin_1.zip, pro_skin_2.zip, pro_skin_3.zip Vaginal: pro_vaginal.zip Downsized counts:  Eukaryotic communities 30 eukaryotic in silico metagenomes comprising up to 200 randomly sampled genomes from a set of 113 eukaryotic species (See Supplementary Table 2 from the paper) corresponding to the eukaryotic species within both CHAMP and MetaPhlAn 4 (Blanco-Míguez et al., 2023) databases. Eukaryotic data is deposited here: <a href="https://doi.org/10.5281/zenodo.12090449" target="_blank" rel="noopener">doi: 10.5281/zenodo.12090449</a> Viral communities 10 viral communities were simulated with 95% of the reads from bacteria and 5% of the reads originating from phages. Each community consisted of 200 randomly selected bacterial genomes from GTDB with species-level annotation and 200 viral genomes from the Gut Phage Database (GPD, Camarillo-Guerrero et al., 2021).  Counts: phage_communities_counts.zip FastQ, forward reads: camisimu_[1-10].fq.1.gz FastQ, reverse reads: camisimu_[1-10].fq.2.gz References Bäckhed, F., Roswall, J., Peng, Y., Feng, Q., Jia, H., Kovatcheva-Datchary, P., et al. (2015). Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life. Cell Host Microbe 17, 690–703. doi: 10.1016/J.CHOM.2015.04.004 Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L. J., Thompson, K. N., Zolfo, M., et al. (2023). Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nature Biotechnology 2023 41:11 41, 1633–1644. doi: 10.1038/s41587-023-01688-w Camarillo-Guerrero, L. F., Almeida, A., Rangel-Pineros, G., Finn, R. D., and Lawley, T. D. (2021). Massive expansion of human gut bacteriophage diversity. Cell 184, 1098. doi: 10.1016/J.CELL.2021.01.029 Fritz, A., Hofmann, P., Majda, S., Dahms, E., Dröge, J., Fiedler, J., et al. (2019). CAMISIM: Simulating metagenomes and microbial communities. Microbiome 7, 1–12. doi: 10.1186/S40168-019-0633-6/FIGURES/5 Proctor, L. (2019). Priorities for the next 10 years of human microbiome research. Nature 2021 569:7758 569, 623–625. doi: 10.1038/d41586-019-01654-0 Saheb Kashaf, S., Proctor, D. M., Deming, C., Saary, P., Hölzer, M., Mullikin, J., et al. (2021). Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions. Nature Microbiology 2021 7:1 7, 169–179. doi: 10.1038/s41564-021-01011-w

opencc-by-4.0Apr 2019View details →

Raw diffraction images of prokaryotic peptide transporter PepTSo2

Diffraction images of prokaryotic peptide transporter PepTSo2. In Nagamura et al. (<a href="https://doi.org/10.1107/S2053230X19003546">Acta Cryst. F, 2019</a>), the crystal form A (PDB code: <a href="http://www.rcsb.org/structure/6JKD">6JKD</a>, space group I4, 3.9 Å resolution) and the form B (PDB code: <a href="http://www.rcsb.org/structure/6JKC">6JKC</a>, space group P4212, 3.5 Å resolution) were reported. Small-wedge (5 or 10°/crystal) datasets collected from loop-harvested microcrystals using EIGER X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. PepT-So2_171220_BL32XU_{1..4}.tar.xz contains directories 01, 07-1, 07-2, 09, 10, 11. PepT-So2_180215_BL32XU.tar.xz contains directories 01, 02, 03, 04, 05, 14. Different directories are from different crystallization conditions. It seems they have different space groups even though they may share the same reduced cells. In the literature for form A data (I4, a=b=115, c=110 Å) from 07-2 and 11 of 171220_BL32XU were used while for form B data (P4212, a=b=119, c=104.3 Å) from 04 and 05 of 180215_BL32XU were used. Note that master.h5 files were modified; see <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md">https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md</a>.

opencc-zeroMar 2023View details →

Data from: Phosphorus limitation determines the quality of dissolved organic matter released by marine heterotrophic prokaryotes

We determined phosphorus (P) limitation effect on the quantity and quality of dissolved organic matter (DOM) released by heterotrophic prokaryotes (HP). We grew 2 single bacterial strains, Photobacterium angustum and Sphingopyxis alaskensis, and natural HP communities collected in fall and spring from the Mediterranean Sea, on glucose under 2 treatments: P-replete vs. P-limiting. DOM release by HP comprised up to 30 % of the initial carbon provided for growth. P availability influenced carbon allocation to different cellular processes (respiration vs. growth), but did not significantly affect the quantity of DOM released by HP. However, using fluorescence spectroscopy, we demonstrated an effect of P-limitation on DOM quality, with a predominance of humic-like compounds under P-limitation but protein-like compounds under P-repletion. Our results suggest that P-limitation could determine the fate of HP-derived DOM in the ocean, thus affecting the microbial carbon pump.

opencc-by-4.0Mar 2023View details →

Oceanic Prokaryotes Metagenome-Assembled Genomes reconstructed using metagenomic distances

Sets of reconstructed Metagenome-Assembled Genomes (MAGs) from Tara Oceans dataset. The reconstructed MAGs belong to Magneto paper: https://doi.org/10.1128/msystems.00432-22 The dataset is composed of 93 oceanic metagenomes sampled from non-polar oceanic regions. The Metagenomic Distance MAGs were reconstructed following a co-assembly protocol driven by nucleotidic composition similarity, as detailed in the publication. The Oceanic Region MAGs were reconstructed by co-assembly of samples belonging to the same Oceanic Regions. The file clusters.tsv sum up the metagenomic distance cluster and the oceanic region each sample belongs to.

opencc-zeroOct 2023View details →

Patterns and drivers of prokaryotic communities in thermokarst lake water across Northern Hemisphere

Aim: The formation of thermokarst lakes could make a large amount of carbon accessible to microbial degradation, potentially intensifying the permafrost carbon-climate feedback via carbon dioxide/methane emissions. Because of their diverse functional roles, prokaryotes play a key role in mediating biogeochemical cycles in thermokarst lakes. However, little is known about the large-scale patterns and drivers of these communities. Location: Permafrost regions in the Northern Hemisphere. Time Period: Present day. Major taxa studied: Prokaryotes. Methods: Based on a combination of large-scale measurements on the Tibetan Plateau and data syntheses in pan-Arctic regions, we constructed a comprehensive dataset of 16S rRNA sequences from 258 thermokarst lakes across Northern Hemisphere permafrost regions. We also used the local contributions to beta diversity (LCBD) to characterize the variance of prokaryotic species composition and screened underlying drivers by conducting a random forest modeling analysis. Results: Prokaryotes in thermokarst lake water were dominated by the orders Burkholderiales, Micrococcales, Flavobacteriales, and Frankiales. The relative abundance of dominant taxa was positively associated with dissolved organic matter (DOM) properties, especially for the chromophoric/aromatic compounds. Microbial structure differed between high-altitude and high-latitude thermokarst lakes, with the dominance of Flavobacterium in high-altitude lakes, and the enrichment of Polynucleobacter in high-latitude lakes. More importantly, climatic variables were among the main drivers shaping the large-scale variation of prokaryotic communities. Specifically, mean annual precipitation was the best predictor for prokaryotic beta diversity across the Northern Hemisphere, as well as in the high-altitude permafrost regions, while mean annual air temperature played a key role in the high-latitude thermokarst lakes. Main conclusions: Our findings demonstrate significant associations between dominant taxa and DOM properties, as well as the important role of climatic factors in affecting prokaryotic communities. These findings suggest that climatic change may alter DOM conditions and induce dynamics in prokaryotic communities of thermokarst lake water in the Northern Hemisphere.

publicOct 2023View details →

Patterns and drivers of prokaryotic communities in thermokarst lake water across Northern Hemisphere

Open the record for dataset details and reuse information.