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Metagenomic and metaproteomic insights into bacterial communities in leaf-cutter ant fungus gardens
<p>The submitted protein sequences were compiled from two of our previous studies, 1) 'Metagenomic and metaproteomic insights into bacterial communities in leaf-cutter ant fungus gardens' (doi.org/10.1038/ismej.2012.10) and 2) 'Leucoagaricus gongylophorus Produces Diverse Enzymes for the Degradation of Recalcitrant Plant Polymers in Leaf-Cutter Ant Fungus Gardens' (doi.org/10.1128/AEM.03833-12).</p>
Assemblies of 269 Metagenomic Tara Pacific Sequencing Samples - part 2
<p>This data is the result of the metagenomic assembly of 269 sequencing samples reflecting a first subset of the Tara Pacific metagenomes. Assemblies are used in </p> <p>- Preprint: <a href="https://doi.org/10.1101/2022.04.11.487905">Endogenous viral elements reveal associations between a non-retroviral RNA virus and symbiotic dinoflagellate genomes</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.7839794">Part 1</a></p>
Assemblies of 269 Metagenomic Tara Pacific Sequencing Samples - part 1
<p>This data is the result of the metagenomic assembly of 269 sequencing samples reflecting a first subset of the Tara Pacific metagenomes. Assemblies are used in </p> <p>- Preprint: <a href="https://doi.org/10.1101/2022.04.11.487905">Endogenous viral elements reveal associations between a non-retroviral RNA virus and symbiotic dinoflagellate genomes</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.7840044">Part 2</a></p>
Palleja et al. 2018 Metagenome Assemblies for Veseli et al. 2023
<p>A collection of anvi'o contigs databases for 57 human fecal metagenome assemblies generated for 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 the study by Palleja et al titled "Recovery of gut microbiota of healthy adults following antibiotic exposure" (https://doi.org/10.1038/s41564-018-0257-9). See `PALLEJA_ET_AL_SAMPLES_INFO.txt` file for 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>
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>
New Soil Metagenome-Assembled Genomes Catalogue Boosts Genetic Resources
<p><strong>Soil harbors a vast expanse of unidentified microbes, termed as microbial dark matter, presenting an untapped reservoir of microbial biodiversity and genetic resources, but has yet to be fully explored. In this study, we conducted the first large-scale excavation of soil microbial dark matter by reconstructing 40,039 metagenome-assembled genome bins (the SMAG catalog) from 3,304 soil metagenomes. We identified 16,530 of 21,077 species-level genome bins (SGBs) as unknown SGBs (uSGBs), which greatly expand archaeal and bacterial diversity across the tree of life. We also illustrate the pivotal role of uSGBs in augmenting soil microbiome's functional landscape and intra-species genome diversity, providing large proportions of the 43,169 biosynthetic gene clusters and 8,545 CRISPR-Cas genes. Additionally, we determined that uSGBs contributed 84.6% of novel viral-host associations identified from the SMAG catalog. Our results propose the SMAG catalog, a novel and expansive genomic resource that brings the soil microbial biodiversity and novel genetic resources to light.</strong></p>
Evaluation of an adapted semi-automated DNA extraction for human salivary shotgun metagenomics
<p>This deposit contains :</p> <p>- a RMarkdown filte containing the codes for the mcirobial analysis of saliva samples</p> <p>- the html report with codes, results and figures</p> <p>- a RData containing microbial datasets (MSp species abundance table, genus, family and phylum abundance tables, matrix of genes correlations, taxonomy)</p> <p>- a RData containing associated metadata </p>
Metagenomes: metadata, taxonomic abundances, PlasX scores, circularity, and contig sequences
<p>This repository contains files that describe the ~36 million contigs that were assembled from 1,782 metagenomes</p> <p>metagenomes_contigs.tar.gz</p> <ul> <li>Fasta files of contig sequences. One file per metagenome</li> </ul> <p>metagenomes_metadata.txt</p> <ul> <li>Information about the 1,782 metagenomes</li> </ul> <p>metagenomes_taxonomic_abundances.txt.gz</p> <ul> <li>Taxonomic abundances in metagenomes, inferred by kraken2 and bracken</li> </ul> <p>metagenomes_contigs_summary.txt.gz</p> <ul> <li>PlasX scores</li> <li>Metagenomic coverage and detection</li> <li>Circularity</li> </ul>
Introduction to Ancient Metagenomics Textbook (Edition 2023): Authentication and Decontamination
<p>Data and conda software environment file for the chapter 'Authentication and Decontamination' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Metagenomic assembly and bin3C clustering result for a healthy human faecal microbiome transplant donor
<p>Metagenomic WGS assembly and Hi-C deconvolution of a healthy human faecal microbiome transplant donor.</p> <p>Metagenomic assembly was produced using Spades (v3.13.1).</p> <p>Extracted MAGs were produced using bin3C (v0.3.3) and QC'd using CheckM (v1.0.18).</p>
Metagenomics assemblies and high-quality MAGs for "Long-read metagenomics to retrieve high-quality metagenome-assembled genomes from canine feces"
<p>This dataset includes the different metagenomics assemblies analyzed and its summary (_info.txt file):</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/100_assembly.fasta">100_assembly.fasta</a> is the Flye 2.7 metagenomics assembly merging HMW and non-HMW datasets</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/75_assembly.fasta">75_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 75% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/50_assembly.fasta">50_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 50% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/HMW_assembly.fasta?versionId=749ff6fd-2642-4ad1-971a-7f3404baa595">HMW_assembly.fasta</a> is the Flye 2.7 metagenomics assembly for HMW dataset.</p> <p>Moreover, it also includes the eight frameshift-corrected high-quality MAGs analyzed in the manuscript. </p>
Metagenome assemblies and metagenome-assembled genomes from the Daphnia magna microbiota
<p>Metagenome assemblies generated from raw reads not mapping to the Daphnia magna genome for six samples assembled individually (G4, G14, S1-S4) and a coassembly of all six samples (a_assembly) using metaSPAdes in SPAdes v3.14. Assemblies can be found in metagenome_assemblies.zip.</p> <p>Metagenome-assembled genomes generated using VAMB v3.0.2 (vamb_bins.zip) and ProxiMeta (proximeta_bins.zip). These MAGs were taxonomically identified using GTDB-Tk v1.3 and quality checked using CheckM v1.1. Outputs from GTDB-Tk and CheckM can be found in the .tsv and .tab files, respectively.</p>
SPIKEPIPE: A metagenomic pipeline for the accurate quantification of eukaryotic species occurrences and intraspecific abundance change using DNA barcodes or mitogenomes
<p>The accurate quantification of eukaryotic species abundances from bulk samples remains a key challenge for community ecology and environmental biomonitoring. We resolve this challenge by combining shotgun sequencing, mapping to reference DNA barcodes or to mitogenomes, and three correction factors: (a) a percent‐coverage threshold to filter out false positives, (b) an internal‐standard DNA spike‐in to correct for stochasticity during sequencing, and (c) technical replicates to correct for stochasticity across sequencing runs. The SPIKEPIPE pipeline achieves a strikingly high accuracy of intraspecific abundance estimates (in terms of DNA mass) from samples of known composition (mapping to barcodes R<sup>2</sup> = .93, mitogenomes R<sup>2</sup> = .95) and a high repeatability across environmental‐sample replicates (barcodes R<sup>2</sup> = .94, mitogenomes R<sup>2</sup> = .93). As proof of concept, we sequence arthropod samples from the High Arctic, systematically collected over 17 years, detecting changes in species richness, species‐specific abundances, and phenology. SPIKEPIPE provides cost‐efficient and reliable quantification of eukaryotic communities.</p>
Unbiased metagenomic sequencing complements specific routine diagnostic methods and increases chances to detect rare viral strains
<p>Raw Illumina MiSeq data in zipped FASTQ format.</p> <p>Files are named by sample type and time point (weeks after transplantation).</p>
Simulated benchmark metagenome used to demonstrate and evaluate MGLEX software
<p>This is a mock dataset of 120 000 artificial contigs of 1 kb length derived by simulating reads from 295 unique genomes and 44 species with each two or three strain genomes using the ART read simulator (Huang et al., 2012) and a lognormal abundance distribution. Genomes were chosen according to the CAMI2015 (www.cami-challenge.org) medium complexity toy dataset. The dataset contains four replicate samples with varied abundances and corresponding sequence feature files in MGLEX v0.1.1 format to use for genome reconstruction. Our aim was to create a benchmark dataset under controlled settings, minimizing potential biases introduced by specific software. This package also includes MGLEX benchmark scripts.</p>
Replidec - Use naive Bayes classifier to identify virus lifecycle from metagenomics data
<p>Replidec: Replication Cycle Decipher for Phages</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Accessing Ancient Metagenomic Data
<p>Data and conda software environment file for the chapter 'Accessing Ancient Metagenomic<br> Data' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Datasets for Metagenome-wide characterization of shared antimicrobial resistance genes in sympatric people and lemurs in rural Madagascar
<p>These datasets accompany the analyses conducted under the study title "<span>Metagenome-wide characterization of shared antimicrobial resistance genes in sympatric people and lemurs in rural Madagascar</span>"</p> <p>Accompanying R scripts with commentary to run these data can be found in the Github repository under release v1.0.0: https://github.com/bmtalbot/Humans_and_Lemurs_2017</p>
Public metagenome datasets annotated using SingleM, using a supplemented reference package.
<p>The SingleM package used for supplementing is available at 10.5281/zenodo.10360136</p>
263 MAG annotations for three nested metagenomic studies describe crop-shrub-microbe interactions in an agroecology system in the Sahel
<p>The Sahel region of West Africa is a vulnerable eco-region, where climate change induced drought and a rapidly growing population pose serious threats to food security and contribute to soil degradation. Local and biologically based systems are necessary to maintain crop yields and soil health, and intercropping with native woody shrubs Guiera senegalensis has been discovered as a solution. We have previously shown that soil microbial communities are significantly altered by the presence of shrubs, and that these organisms may have plant growth promoting properties. Here, we augment those data with metagenomic and metatranscriptomic data across three nested experiments: a landscape scale experiment across a rainfall and soil type gradient, a long-term experimental site, and a growth chamber simulated drought experiment. We recovered 263 95% ANI dereplicated metagenome-assembled genomes (MAGs) of medium and high quality to evaluate their relative enrichment and what their encoded metabolisms reveal about mechanisms of microbiome millet support. These data contribute to our understanding of the role of the microbial community crop drought resilience in the Sahel and in semi-arid cropping systems globally. Here we present the DRAM annotations of each MAG, all associated metadata, viral genes and vOTUs from the Optimized Shrub Intercropping Study (OSS), and eukaryotic contigs from the OSS</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.