Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
54
datasets available to search
ShareScore release 0.9.0
Dataset results
54 results for “gut metagenome”
Gut Metagenome Assemblies for Veseli et al. 2023
<p>A collection of anvi'o contigs databases for 408 human fecal metagenome assemblies from the study by Veseli et al. titled "High metabolic independence is a determinant of microbial resilience in the face of gut stress". These are publicly-available gut metagenomes originally obtained from several studies of the gut microbiome. See `METAGENOMES_INFO.txt` file for references and sample SRA accessions.</p> <p>The metagenomes were assembled individually using IDBA-UD as part of the anvi'o metagenomics workflow in anvi'o v7.1-dev. As part of this workflow, they were annotated with KEGG KOfams using `anvi-run-kegg-kofams` and a KEGG snapshot from December 12, 2020 (modules database hash value `45b7cc2e4fdc`). See manuscript and its reproducible workflow for details.</p>
iMGMC - integrated Mouse Gut Metagenomic Catalog
<p><em>Creation of an new mouse gut gene catalog with special features:</em></p> <ul> <li>more diverse samples from different studies (12 Vendors incl. wild mice and various gut locations)</li> <li>clustering-free approach: all-in-one assembly, keeping track of each ORF to contigs to bins</li> <li>higher taxonomic resolution and more accuracy by using contigs for annotation</li> <li>16S rRNA gene integration via linkage to bins</li> <li>expansion by 20,927 MAGs from sample-wise assembly of 871 mouse gut metagenomic samples, representing 1,296 species</li> </ul> <p>Code used: <a href="https://github.com/tillrobin/iMGMC">https://github.com/tillrobin/iMGMC</a></p> <p>The vast complexity of host-associated microbial ecosystems requires host-specific reference catalogs to survey the functions and diversity of these communities. We generated a comprehensive resource, the integrated mouse gut metagenome catalog (iMGMC), comprising 4.6 million unique genes and 660 metagenome-assembled genomes (MAGs) with many of them (485 MAGs, 73%) linked to reconstructed full-length 16S rRNA gene sequences. iMGMC enables unprecedented coverage and taxonomic resolution of the mouse gut microbiota, i.e. more than 92% of MAGs lack species-level representatives in public repositories (<95% ANI match). The integration of MAGs and 16S rRNA gene data allows a more accurate prediction of functional profiles of communities than based on 16S rRNA amplicons alone. Accompanying iMGMC we provide a set of MAGs representing 1,296 gut bacteria obtained through complementary assembly strategies. We envision that integrated resources such as iMGMC together with MAG collections will enhance the resolution of numerous existing and future sequencing-based studies.</p> <p>Genecatalog:</p> <p>Description Size Filename<br> Catalog ORF sequences 1 GB iMGMC-GeneID.fasta.gz<br> Full assembly contigs 1.3 GB iMGMC-ConitgID.fasta.gz<br> Mapping File (GeneID->ContigID->BinID) 30 MB iMGMC-map-Gene-Contig-Bin.tab.gz<br> Taxonomic annotations 40 MB iMGMC_map_taxonomy.tar.gz<br> Functional annotations 36 MB iMGMC_map_functionality.tar.gz<br> 16S rRNA sequences 2 MB iMGMC-16SrRNAgenes.fasta</p> <p>Metagenome-assembled genomes (MAGs) :</p> <p>Description Size Filename<br> integrated MAGs 0.5 GB iMGMC_MAGs.tar.gz<br> representave mMAGs (n=1296) 1 GB iMGMC-mMAGs-dereplicated_genomes.tar.gz<br> representave hqMAGs (n=830) 0.7 GB iMGMC-hqMAGs-dereplicated_genomes.tar.gz<br> all mMAGs (n=20,927) 15 GB iMGMC-mMAGs.tar.gz<br> Annotations by CheckM, dRep-Clustering, GTDB-Tk 2 MB MAG-annotation_CheckM_dRep_GTDB-Tk.tar.gz<br> Functional annotations (hqMAGs by eggNOG mapper v2) 187 MB hqMAGs.emapper.annotations.gz</p> <p> </p>
Reconstruction of prokaryotic genomes from ten termite gut metagenomes using two distinct workflows: SnakeMAGs and ATLAS.
<p><strong><em>SnakeMAGs</em></strong> (Nachida Tadrent, Franck Dedeine, Vincent Hervé (Submitted). <em>SnakeMAGs</em>: a simple, efficient, flexible and scalable workflow to reconstruct prokaryotic genomes from metagenomes<em>.</em> <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 (<em>Kieser </em>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.</p> <p>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, <em>i.e. </em>the MAGs reconstructed from each metagenome and their taxonomic classification.</p>
Metagenomics uncovers dietary adaptations for chitin digestion in the gut microbiota of convergent myrmecophagous mammals
<p><strong>Metagenomics uncovers dietary adaptations for chitin digestion in the gut microbiota of convergent myrmecophagous mammals</strong></p> <p>Sophie Teullet<sup>a,#</sup>, Marie-Ka Tilak<sup>a</sup>, Amandine Magdeleine<sup>a</sup>, Roxane Schaub<sup>b,c</sup>, Nora M. Weyer<sup>d</sup>, Wendy Panaino<sup>d,e</sup>, Andrea Fuller<sup>d</sup>, William. J. Loughry<sup>f</sup>, Nico L. Avenant<sup>g</sup>, Benoit de Thoisy<sup>h,i</sup>, Guillaume Borrel<sup>j</sup> and Frédéric Delsuc<sup>a,#</sup></p> <p><sup>a</sup>Institut des Sciences de l’Evolution de Montpellier (ISEM), Univ Montpellier, CNRS, IRD, Montpellier, France</p> <p><sup>b</sup>CIC AG/Inserm 1424, Centre Hospitalier de Cayenne Andrée Rosemon, Cayenne, French Guiana</p> <p><sup>c</sup>Tropical Biome and immunopathology, Université de Guyane, Labex CEBA, DFR Santé, Cayenne, French Guiana</p> <p><sup>d</sup>Brain Function Research Group, School of Physiology, University of the Witwatersrand, Johannesburg, South Africa</p> <p><sup>e</sup>Centre for African Ecology, School of Animals, Plant, and Environmental Sciences, University of the Witwatersrand, Johannesburg, South Africa</p> <p><sup>f</sup>Department of Biology, Valdosta State University, Valdosta, GA, USA</p> <p><sup>g</sup>National Museum and Centre for Environmental Management, University of the Free State, Bloemfontein, South Africa</p> <p><sup>h</sup>Institut Pasteur de la Guyane, Cayenne, French Guiana, France</p> <p><sup>i</sup>Kwata NGO, Cayenne, French Guiana, France</p> <p><sup>j</sup>Institut Pasteur, Université Paris Cité, UMR CNRS 6047, Evolutionary Biology of the Microbial Cell, Paris, France</p> <p><sup>#</sup>Corresponding authors: sophie.teullet@umontpellier.fr; frederic.delsuc@umontpellier.fr</p> <p> </p> <p><em><strong>Abstract</strong></em></p> <p>In mammals, myrmecophagy (ant and termite consumption) represents a striking example of dietary convergence. This trait evolved independently at least five times in placentals with myrmecophagous species comprising aardvarks, anteaters, some armadillos, pangolins, and aardwolves. The gut microbiome plays an important role in dietary adaptation, and previous analyses of 16S rRNA metabarcoding data have revealed convergence in the composition of the gut microbiota among some myrmecophagous species. However, the functions performed by these gut bacterial symbionts and their potential role in the digestion of prey chitinous exoskeletons remain open questions. Using long- and short-read sequencing of fecal samples, we generated 29 gut metagenomes from nine myrmecophagous and closely related insectivorous species sampled in French Guiana, South Africa, and the USA. From these, we reconstructed 314 high-quality bacterial genome bins of which 132 carried chitinase genes, highlighting their potential role in insect prey digestion. These chitinolytic bacteria belonged mainly to the family Lachnospiraceae, and some were likely convergently recruited in the different myrmecophagous species as they were detected in several host orders (i.e., <em>Enterococcus faecalis</em>, <em>Blautia</em> sp), suggesting that they could be directly involved in the adaptation to myrmecophagy. Others were found to be more host-specific, possibly reflecting phylogenetic constraints and environmental influences. Overall, our results highlight the potential role of the gut microbiome in chitin digestion in myrmecophagous mammals and provide the basis for future comparative studies performed at the mammalian scale to further unravel the mechanisms underlying the convergent adaptation to myrmecophagy.</p> <p> </p> <p><em><strong>Main figures and corresponding datasets</strong></em></p> <p><strong>Figure_1_dataset.zip</strong> contains:</p> <ul> <li><strong>FIGURE 1.</strong> Phylogenetic position of the 314 high-quality selected bins reconstructed from 29 gut metagenomes of the nine focal myrmecophagous species within a reference prokaryotic phylogeny. A: Phylogeny of the 314 selected bins (red branches) with 2496 prokaryote reference genomes. Circles respectively indicate (from inner to outer circles): the bacterial phyla and kingdom to which these genome bins were assigned based on the Genome Taxonomy Database release 7 (Parks <em>et al</em>, 2021). Clades, where a subtree was defined, are highlighted in blue for the Firmicutes (Fig. 1B), green for the Bacteroidetes, and pink for the Proteobacteria (Figs. S2 A and B, respectively). B: Subtree within Fimircutes showing myrmecophagous-specific clades (blue highlights; dark blue corresponds to the three clades mentioned in the results, light blue to the other clades). The outer circle indicates the bacterial family to which these genome bins were assigned based on the Genome Taxonomy Database. Bins’ names of the myrmecophagous-specific clades are indicated at leaves of the phylogenetic tree together with the genus to which they were assigned to.</li> <li><strong>phylophlan_LR_SR_ToL_FINAL_concatenated.aln</strong>: Alignment of the concatenated markers assembled by PhyloPhlAn v3.0.58.</li> <li><strong>phylophlan_LR_SR_ToL_FINAL.tre</strong>: Phylogenetic tree reconstructed by PhyloPhlAn v3.0.58 for the 314 high quality selected genome bins and the 2496 prokaryote reference genomes.</li> </ul> <p><strong>Figure_2_dataset.zip </strong>contains:</p> <ul> <li><strong>FIGURE 2</strong>. Phylogeny of the 394 GH18 sequences identified in 132 high-quality selected bins reconstructed from 29 gut metagenomes of the nine focal myrmecophagous species and relatives. Red branches indicate the 237 sequences having an active chitinolytic site (DXXDXDXE). Circles respectively indicate (from inner to outer circles): the bacterial family and phyla of the bin the sequence was retrieved from. Colored sequence names indicate the host species. Colored circles at certain nodes indicate enzymes to which sequences are similar when blasting them against the NCBI non-redundant protein database. Sequence names are indicated at leaves of the tree and begin with the genus to which the bin they were identified in was assigned to. </li> <li><strong>GH18_sequences_from_selected_bins_alignment.fasta</strong>: Alignment of the 394 GH18 sequences identified in 132 high quality selected bins computed with MAFFT v7.450.</li> <li><strong>GH18__sequences_from_selected_bins_tree.newick</strong>: Phylogenetic tree of the 394 GH18 sequences inferred with RAxML v8.2.11 within Geneious Prime 2022.0.2.</li> </ul> <p><strong>Figure_3_dataset.zip</strong> contains:</p> <ul> <li><strong>FIGURE 3</strong>. Detection of the 314 high-quality bacterial genomes (lines) in the 29 gut metagenomes (columns) of the nine focal species. Each square indicates the detection of a genome bin in a sample as estimated by anvi’o v7 (Eren <em>et al</em>, 2021). Names of bins are indicated on the left with red indicating chitinolytic bins (Table S2). The names begin with the genus to which the bin was assigned to. Asterisks (*) indicate bins detected in at least one soil sample (detection > 0.25) (Fig. S4, Table S2, and detection table available via Zenodo). Phylogenetic relationships of host species distinguished by different color strips are represented at the bottom of the graph. Columns on the right indicate (from left to right): the number of GH18 sequences identified in each bin (from 0 to 17), the bin’s taxonomic phylum, class, order, and family. The phylogeny of the 314 selected bins inferred with PhyloPhlAn v3.0.58 (Asnicar <em>et al</em>, 2020) is also represented on the right of the graph (see Fig. S1). Silhouettes were downloaded from phylopic.org.</li> <li><strong>detection_bins_across_gut_metagenomes.txt</strong>: Detection table as tab-delimited file containing the detection values inferred by anvi'o v7 for the 314 high quality selected bins across the 29 gut metagenomes from the nine focal myrmecophagous species. </li> </ul> <p><strong>Figure_4_dataset.zip</strong> contains:</p> <ul> <li><strong>FIGURE 4</strong>. Distribution of chitinolytic selected bins (red links) among the nine focal myrmecophagous species and relatives. Phylogenies of the 314 high-quality selected bins (Fig. S1) and of the nine host species (downloaded from timetree.org) are represented respectively on the left and the right of the graph. Links illustrate, for each bin, in which host species the bin was detected (detection threshold > 0.25). Red links indicate bins in which at least one GH18 sequence with an active chitinolytic site (DXXDXDXE) was found (chitinolytic bins). The size of the circles at the tips of the host phylogeny is proportional to the number of samples (n = 1 for <em>D. kap</em>; n = 2 for <em>D. nov</em>, <em>C. uni</em> and <em>M. tri</em>; n = 3 for <em>T. tet </em>and <em>O. af</em>e; n = 4 for <em>D. sp. nov </em>FG; n = 6 for <em>P. cri </em>and <em>S. tem</em>). Bins’ names are indicated at the tip of the bins’ phylogeny and main bacterial phyla are indicated by colored vertical bars. This graph was done with the cophylo R package within the phytools suite (Revell, 2012). Silhouettes were downloaded from phylopic.org.</li> <li><strong>presence_absence_MAGs_in_metagenomes.txt</strong>: Presence/absence matrix of the 314 selected genome bins across the 29 gut metagenomes.</li> <li><strong>host_species_phylo_reduced_fig4.newick</strong>: Host phylogenetic timetree.</li> </ul> <p><strong>Table_1_sample_infos.xls: </strong>Detailed sample information for the 33 fecal samples collected. <em>N.B</em>.: Diet was determined based on field observations (i.e., dissections) and the literature.</p> <p><strong> </strong></p> <p><em><strong>Supplementary results</strong></em></p> <p><strong>Supplementary_results_Teullet_etal_2023.zip </strong>includes a comparison of genome statistics of the selected bins reconstructed from the long-read vs the short-read datasets, a phylogeny of the set of selected bins before dereplication (n = 407) and a comparison of the distribution of shared and specific genome bins carrying GH18 among host orders.</p> <p> </p> <p><em><strong>Supplementary material</strong></em></p> <p><strong>Supplementary_material_Teullet_etal_2023.zip </strong>contains</p> <ul> <li>Supplementary figures (S1-S4) and tables (S1-S4).</li> <li><strong>phylophlan_314_bins_phylogeny_FINAL_concatenated.aln and phylophlan_314_bins_phylogeny_FINAL.tre</strong>: Alignment of the concatenated markers and the final tree (respectively) reconstructed by PhyloPhlAn v3.0.58 for the 314 high-quality selected and dereplicated genome bins.</li> <li><strong>phylophlan_407_selected_bins_nodRep_concatenated.aln and phylophlan_407_selected_bins_phylogeny_FINAL.tre</strong>: Alignment of the concatenated markers and the final tree (respectively) reconstructed by PhyloPhlAn v3.0.58 for the 407 high-quality selected genome bins before dereplication.</li> <li><strong>abundance_bins_across_gut_metagenomes.txt</strong>: A tab-delimited file corresponding to the absolute abundance values inferred by anvi'o v7 for the 314 high-quality selected bins across the 29 gut metagenomes from the nine focal myrmecophagous species. </li> <li><strong>detection_bins_across_soil_samples.txt</strong>: A tab-delimited file corresponding to the detection values inferred by anvi'o v7 for the 140 high-quality selected bins reconstructed from the aardvark, ground pangolin and southern aardwolf gut metagenomes across the eight soil samples collected on sample sites in South Africa.</li> </ul> <p> </p> <p><strong><em>Assemblies</em></strong></p> <p><strong>Long-read_metagenomic_assemblies_polished.zip</strong> contains the 31 long-read metagenomes assembled with metaFlye strain v2.9 and polished with short reads using Pilon v1.4, which were used for binning.</p> <p><strong>Long-read_metagenomic_assemblies_not_polished.zip</strong> contains the 33 long-read metagenomes assembled with metaFlye strain v2.9 before polishing.</p> <p><strong>Short-read_metagenomic_assemblies.zip</strong> contains the 31 short-read metagenomes assembled with metaSPAdes and MEGAHIT.</p> <p><em>N.B</em>:</p> <ol> <li>Two samples (DASY M1746 and DASY VLD168) were not sequenced using Illumina short reads. Only long reads were generated and assembled for these two samples and are made available here. As these assemblies could not be polished, these samples were not included in downstream analyses.</li> <li>Two samples (CAB M3141 and MYR M5293) were highly contaminated by host reads and not used in downstream analyses. As they were still assembled with the other samples, the corresponding metagenomes are made available here.</li> </ol> <p> </p> <p><strong><em>Binning: genome bins and dereplication results</em></strong></p> <p><strong>High-quality_selected_bins_dereplicated.zip</strong> contains the 314 high quality selected bins (>90% completion, <5% redundancy) reconstructed from long- and short-read metagenomes with metaBAT2 and dereplicated with dRep at 98% ANI.</p> <p><strong>metaBAT2_short-read_assemblies_bins.zip </strong>contains all bins reconstructed from the short-read assemblies with metaBAT2 (i.e., output of metaBAT2).</p> <p><strong>metaBAT2_long-read_assemblies_bins.zip</strong> contains all bins reconstructed from the long-read polished assemblies with metaBAT2 (i.e., output of metaBAT2).</p> <p><strong>Output_dRep_98ANI_407_bins_long-short-reads.zip</strong> contains the output of the dereplication analysis done on the set of 407 high-quality selected genome bins reconstructed from long- (n = 201) and short-read (n = 206; labeled "spad") metagenomes. It was performed with dRep using default parameters. After this step, the final dataset included 314 high-quality non-redundant genome bins. This folder includes:</p> <ul> <li><strong>LR_SR_407_bins_dRep_98ANI_Primary_clustering_dendrogram.pdf</strong>: The primary clustering of selected genome bins using the Mash algorithm with an ANI threshold of 90%.</li> <li><strong>LR_SR_407_bins_dRep_98ANI_Secondary_clustering_dendrograms.pdf</strong>: The secondary clustering of selected genome bins using the fastANI algorithm with an ANI threshold of 98%.</li> <li><strong>LR_SR_407_bins_dRep_98ANI_Cluster_scoring.pdf</strong>: The clustering score attributed to each genome bin during dereplication. Asteriks (*) indicate genomes chosen to be the representative genomes of their cluster.</li> </ul> <ul> </ul>
Comprehensive discovery of CRISPR-targeted terminally redundant sequences in the human gut metagenome: viruses, plasmids, and more
<p>S1 Data</p> <p>Dataset including the discovered CRISPR spacers, direct repeats, protospacers, co-occurrence-based spacer clustering results, predicted protein sequences, built HMMs, database comparison results, phylogenetic analysis results, predicted targeting hosts, and CRISPR-targeted TR sequences.</p>
Results of a Galaxy metagenomic analysis of bee gut microbiome data from PRJNA977416
<p>This dataset contains the outputs of a metagenomic Galaxy workflow run on the raw data of the project PRJNA977416, including the CSV file of associated metadata and the workflow.ga used for the analysis.</p> <p>Firstly, it has information on taxonomic assignment with :</p> <ul> <li>the reports of all samples for Kraken2, Bracken, and MetaPhlan taxonomic profilers. </li> <li>two tabular files obtained with Taxpasta, which merge samples and standardize taxonomic abundances.</li> <li>for the Bracken standardised abundance, a file with the measures of alpha diversity calculated </li> <li>two HTML files giving access to the Krona diagram for this taxonomic composition.</li> </ul> <p>Secondly, it contains functional informations with :</p> <ul> <li>a tabular file with the relative abundance of all GO terms for all samples</li> <li>a directory detailing pathways and genes families detected.</li> </ul>
German beaver gut metagenome
<p>This dataset is part of research study:</p> <p> </p> <p>Pratama R, Schneider D, Böer T and Daniel R (2019) First Insights Into Bacterial Gastrointestinal Tract Communities of the Eurasian Beaver (<em>Castor fiber</em>). <em>Front. Microbiol.</em> 10:1646. doi: 10.3389/fmicb.2019.01646</p>
Supplementary Material for publication "Bifidobacteria Define Gut Microbiome Profiles of Golden Lion Tamarin (Leontopithecus rosalia} and Marmoset Callithrix sp. Metagenomic Shotgun Pools
<p>Supplementary Tables and Figure for the publication "Bifidobacteria Define Gut Microbiome Profiles of Golden Lion Tamarin <em>Leontopithecus rosalia</em> and Marmoset <em>Callithrix</em> sp. Metagenomic Shotgun Pools"</p>
Comparative seagulls of gut microbiota by using metagenomics and 16S rDNA sequencing
<p><span>Shotgun</span><span> metagenomic and 16S rDNA sequencing are commonly used methods to identify the taxonomic composition of microbial communities. </span><span>We compared the metagenome and 16S rDNA amplicon results to demonstrate the features of this animal. </span><span>In general, </span><span>relatively </span><span>consistent patterns and reliability could be identified by both sequencing methods, but the results varied </span><span>following </span><span>the refinement of taxonomic levels. </span><span>Metagenomic </span><span>sequencing was more suitable for the discovery and detection of pathogenic bacteria of gut microbiota in seagulls.</span><span> Although there were large differences in the numbers and abundance of </span><span>bacterial </span><span>species of</span><span> the</span><span> two methods in terms of taxonomic levels, the patterns and reliability results of </span><span>the </span><span>samples were consistent.</span></p>
Comparative seagulls of gut microbiota by using metagenomics and 16S rDNA sequencing
Open the record for dataset details and reuse information.
Metagenomic analysis of gut microbiome illuminates the mechanisms and evolution of lignocellulose degradation in mangrove herbivorous crabs
<p><strong>Background:</strong></p> <p>Sesarmid crabs dominate mangrove habitat as the major primary consumers, which facilitates the trophic link and nutrient recycling in the ecosystem. Therefore, the adaptations and mechanisms of sesarmid crabs to herbivory is not only crucial to terrestrialization and its evolutionary success, but also to the healthy functioning of mangrove forest ecosystems. Although endogenous cellulases expressions were reported in crab species, it remains unknown if the endogenous enzymes alone can complete the whole lignocellulolytic pathway, or they also depend on the contribution from their intestinal microbiome. We attempt to investigate the role of gut symbiotic microbes of mangrove-feeding sesarmid crabs in plant digestion using a comparative metagenomic approach.</p> <p><strong>Results:</strong></p> <p>Metagenomics analyses on 43 crab gut samples from 23 species of mangrove crabs revealed a wide coverage of 127 CAZy families and nine KOs targeting lignocellulose and their derivatives in all species analyzed, including predominantly carnivorous species, suggesting the crab species gut microbiome have lignocellulolytic capacity regardless of dietary preference. Microbial cellulase, hemicellulase and pectinase genes in herbivorous and detritivorous crabs were differentially more abundant when compared to omnivorous and carnivorous crabs, indicating the importance of gut symbionts in lignocellulose degradation in mangrove crabs and the enrichment of lignocellulolytic microbes in response to diet with higher lignocellulose content. The herbivorous and detritivorous crabs showed highly similar CAZyme composition compared to dissimilarities observed in taxonomic profiles observed in both groups, suggesting a stronger selection force to gut microbiota by its functional capacity than by taxonomy. The gut microbiota in herbivorous sesarmid crabs were also enriched with nitrogen reduction and fixation genes, implying possible roles of the gut microbiota in supplementing nitrogen that is deficient in plant diet.</p> <p><strong>Conclusions:</strong></p> <p>Endosymbiotic cellulolytic microbes play an important role in lignocellulose degradation in most crab species but their abundance is strongly correlated with dietary preference, and they are highly enriched in herbivorous sesarmids, thus enhancing their capacity for digestion of mangrove leaves. Dietary preference is a stronger driver in determining the microbial CAZyme composition and taxonomic profile in mangrove crab microbiome, resulting in functional redundancy of endosymbiotic microbes. Our results showed that crabs implement a mixed mode of digestion utilizing both endogenous and microbial enzymes in lignocellulose degradation, as observed in most of the more advanced herbivorous invertebrate species.</p>
Twenty-five metagenome assembled genomes recovered from the gut microbiome of the domestic ferret, Mustela putorius
<p>This dataset is composed of 25 unique metagenome assembled genomes (MAGs) recovered from the gut microbiome of three domestic ferrets (<em>Mustela putorius</em>). Details on both MAG and host ferret metadata, as well as information on sample collection, DNA sequencing, and bioinformatic processing can be found in the American Society for Microbiology Resource Announcement by Amundson et al. (in prep). </p>
HiFi Metagenomic Sequencing Enables Assembly of Accurate and Complete Genomes from Human Gut Microbiota.
<p>We reported 102 complete metagenome assembled genomes (cMAGs) from five human fecal HiFi sequencing samples.</p> <p>102_cMAGs_fna.tar.gz: Fasta sequence files of 102 cMAGs.</p> <p>gc_skew_figures.tar.gz: GC-skew pattern figures of 102 cMAGs. (SVG format)</p> <p>coverage_plots.tar.gz: Genome coverage plot of 102 cMAGs.</p>
Early-life human gut metagenome-assembled genomes and proteins catalogs
<p>The description of the files:</p> <p>(1) The 32,277 genomes include six parts: ELGG_part_1.zip, ELGG_part_2.zip, ELGG_part_3.zip, ELGG_part_4.zip, ELGG_part_5.zip, ELGG_part_6.zip.</p> <p>(2) The 2,172 representative species: ELGG_representatives_2172.zip.</p> <p>(3) The ELGP catalog clustered at 95% amino acid identity: ELGP_95.faa.gz.</p> <p> </p>
Data supporting publication "Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome"
<p>Data supporting publication "Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome"</p>
Simulated Human gut metagenomic samples to benchmark mOTUs v2
<p>We simulated ten human gut metagenomic samples to assess the taxonomic quantification accuracy of the mOTUs tool (<a href="http://motu-tool.org/">link</a>). In this directory you can find the metagenomic samples, the gold standard (used to produce them) and the profiles obtained with four metagenomic profiler tools.</p> <p>Check README.txt for more information.</p>
Metagenome-assembled genomes(MAGs) generated from CRC human gut (PRJEB27928).
<p>MAGs generated from CRC human gut (PRJEB27928) with Maxbin2, VAMB, Metabat2, SemiBin(single-sample binning) and VAMB, SemiBin(multi-sample binning).</p> <p>Single-sample binning: Maxbin2.tar.gz, Metabat2.tar.gz, VAMB.tar.gz and SemiBin(_pretrain).tar.gz. </p> <p>Multi-sample binning: VAMB_multi.tar.gz and SemiBin_multi.tar.gz.</p>
Metagenome-assembled genomes(MAGs) generated from dog gut (PRJEB20308).
<p>MAGs generated from dog gut (PRJEB20308) with Maxbin2, VAMB, Metabat2, SemiBin(single-sample binning) and VAMB, SemiBin(multi-sample binning).</p> <p>Single-sample binning: Maxbin2.tar.gz, Metabat2.tar.gz, VAMB.tar.gz and SemiBin(_pretrain).tar.gz. </p> <p>Multi-sample binning: VAMB_multi.tar.gz and SemiBin_multi.tar.gz.</p>
Metagenomics of Parkinson's disease implicates the gut microbiome in multiple disease mechanisms
<p><strong>Abstract:</strong> Parkinson's disease (PD) may start in the gut and spread to the brain. To investigate the role of gut microbiome, we conducted a large-scale study, at high taxonomic resolution, using uniform standardized methods from start to end. We enrolled 490 PD and 234 control individuals, conducted deep shotgun sequencing of fecal DNA, followed by metagenome-wide association studies requiring significance by two methods (ANCOM-BC and MaAsLin2) to declare disease association at species and genus level, followed by network analysis to identify polymicrobial clusters, and functional profiling based on microbial genes and pathways. Here we show that over 30% of species, genes and pathways tested have altered abundances in PD, depicting a widespread dysbiosis. PD-associated species form polymicrobial clusters that grow or shrink together, and some compete. PD microbiome is disease permissive, evidenced by overabundance of pathogens and immunogenic components, dysregulated neuroactive signaling, preponderance of molecules that induce alpha-synuclein pathology, and over-production of toxicants; with the reduction in anti-inflammatory and neuroprotective factors limiting the capacity to recover. We validate, in human PD, findings that were observed in experimental models; reconcile and resolve human PD microbiome literature, and provide a broad foundation with a wealth of concrete testable hypotheses to discern the role of the gut microbiome in PD. </p> <p><strong>Zenodo contents:</strong> In this Zenodo archive we provide (1) post sequence QC and post taxonomic and functional profiling "Source Data" used to generate tables and figures in the manuscript and (2) "Supplementary Code" that contains the workflow and code used to perform bioinformatic processing of shotgun sequences and statistical analyses of microbial profiles and subject metadata. The code provided here is the same "Supplementary Code" that is provided in the supplement of the manuscript. Individual level raw shotgun sequences and metadata are available on NCBI Sequence Read Archive (SRA) under BioProject ID <a href="https://www.ncbi.nlm.nih.gov/bioproject/834801">PRJNA834801</a>.</p>
Fasta format protein sequences from assembled kyphosid fish gut metagenomes
<p>Predicted proteins sequences from kyposid fish gut metagenomic samples F5, F6, F7, and F8, obtained as described in the following study:</p> <p>Podell S, Oliver A, Kelly LW, Sparagon W, Plominsky, A, Nelson RS, Laurens LML, Augyte, S, Sims NA, Nelson CE, Allen EE. Herbivorous fish microbiome adaptations to sulfated dietary polysaccharides (2023)<br> manuscript submitted.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.