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3,415 results for “Gut”
Full-length and split homologs of human proteins in the gut microbiome
<p>These files were generated as part of the manuscript "Human xenobiotic metabolism proteins have full-length and split homologs in the gut microbiome" (submitted).</p> <p>The .tar file contains .ipc files that are tables of full-length (full_humcover3.ipc) and split homologs (part_humcover3.ipc) of human proteins in the gut microbiome, organized by alignment coverage threshold. For example, the directory `HumanUPR_0.67_src_20000_70` contains results obtained at a 67% alignment coverage threshold for the bacterial protein, and 70% for the human protein. Note that our pipeline collapses full-length alignments to the same UHGP-90 protein family into a single entry per species, with the number of genomes reported in the column nGenomes. Split homologs are not collapsed because genomic context is used to define them, and this context may differ across individual genomes.</p> <p>These files are in Arrow <a href="https://arrow.apache.org/docs/python/ipc.html#ipc">IPC</a> format, which provides compression and fast I/O for large tables. We recommend reading them using <a href="https://pola.rs/">pola.rs</a> or the <a href="https://arrow.apache.org/docs/r/">R Arrow</a> package. In particular, because the full-length homolog table is large, you may wish to work with it without loading it into memory, which can be accomplished using <a href="https://docs.pola.rs/api/python/dev/reference/api/polars.scan_ipc.html">scan_ipc</a> in pola.rs or <a href="https://arrow.apache.org/docs/r/reference/open_dataset.html">open_dataset</a> in R Arrow.</p> <p>We also provide gzipped .csv format datasets of full-length (pgkb_FH_drugs.csv.gz) and split (pgkb_SH_drugs.csv.gz) homologs, at the default 67% alignment coverage threshold for bacterial and 70% for human proteins, organized by their <a href="https://www.pharmgkb.org/">PharmGKB</a> annotations. For each drug annotated in PharmGKB as being metabolized by a human protein with full-length or split homologs, we provide the human protein(s) responsible, its xenobiotic enzyme class, the bacterial protein homolog(s), length and percent identity of the alignment, and either the specific genome (g, split homologs only) or the number of genomes (nGenomes, full homologs only). Xenobiotic enzyme classes are defined as in Figure 4 of the manuscript, with the additional classes "nucl" (nucleobase-containing metabolic proteins not annotated to any other class), "redox" (oxidoreductases not annotated to any other class), and "other" (all remaining proteins).</p>
Supplementary Datasets for the publication "Increased Susceptibility of Rousettus aegyptiacus Bats to Respiratory SARS-CoV-2 Challenge Despite Its Distinct Tropism for Gut Epithelia in Bats"
<p>Increasing evidence suggests bats are the ancestral hosts of the majority of coronaviruses. In gen-eral, coronaviruses primarily target the gastrointestinal system, while some strains, especially Be-tacoronaviruses with the most relevant representatives SARS-CoV, MERS-CoV, and SARS-CoV-2, also cause severe respiratory disease in humans and other mammals. We previously reported the susceptibility of Rousettus aegyptiacus (Egyptian fruit bats) to intranasal SARS-CoV-2 infection. Here, we compared their permissiveness to an oral infection versus respiratory challenge (in-tranasal or orotracheal) by assessing virus shedding, host immune responses, tissue-specific pa-thology, and physiological parameters. While respiratory challenge with a moderate infection dose of 1 × 104 TCID50 caused a systemic infection with oral and nasal shedding of replica-tion-competent virus, the oral challenge only induced nasal shedding of low levels of viral RNA. Even after a challenge with a higher infection dose of 1 × 106 TCID50, no replication-competent vi-rus was detectable in any of the samples of the orally challenged bats. We postulate that SARS-CoV-2 is inactivated by HCl and digested by pepsin in the stomach of R. aegyptiacus, thereby decreasing the efficiency of an oral infection. Therefore, fecal shedding of RNA seems to depend on systemic dissemination upon respiratory infection. These findings may influence our general understanding of the pathophysiology of coronavirus infections in bats.</p>
Data from: "Alteration of the gut microbiota's composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters"
<p>This dataset contains all data collected and used for the publication : "Alteration of the gut microbiota’s composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters". Besides the Readme, it contains 11 files.</p> <p><br> Excel files with classification (i.e. genes according to their fold induction or repression) are provided. Data include different conditions with varying number of samples per group. Data are structured according to employed methods and then stratify the data obtained within the individual work packages.</p>
Aggregated Gut Viral Catalogue (AVrC)
<p>Despite the importance of the gut virome in human health and disease, identifying viral sequences from metagenomic datasets remains computationally challenging. Up to 99% of viral reads lack significant alignments to known viral genomes due to underrepresentation in reference databases. Recent machine learning tools can detect novel viral sequences based on features like k-mer composition or genomic signatures, but are limited to classifying assembled contigs into simplistic viral/non-viral categories. Several large-scale efforts have mined human gut metagenomes to establish viral catalogues, including the Gut Virome Database (33,242 viral OTUs), Cenote Human Virome Database (45,033 OTUs), and Gut Phage Database (142,809 OTUs). However, these catalogues have not been consistently compared for quality, diversity, and completeness. There is an unmet need to harmonize available gut viral sequences into a unified resource for comparing novel viruses against previous efforts. The Aggregated Gut Viral Catalog (AVrC) addresses this gap by harmonizing and aggregating previous mining efforts into a comprehensive resource to allow for the exploration of the Human gut viral diversity and the easier comparison of newly discovered viral sequences.</p>
Dietary fibers boost gut microbiome-produced B vitamin pool and alter host immune landscape
<p>This dataset contains fcs files of lymphocytes from the colonic lamina propria, lungs, and spleens of specific-pathogen-free (SPF), gnotobiotic (14-member synthetic microbiota, 14SM) or germ-free (GF) mice fed five distinct rodent diets (Standard chow 1, SC1; Standard chow 2, SC2; Fiber-supplemented diet, FS; Inulin-supplemented diet, IN; or Fiber-free diet, FF), analysed by mass cytometry. Three million cells per organ per animal were transferred into 15 mL conical tubes. For live/dead staining, cells were incubated with 5 μM cisplatin for 5 minutes. Cells were washed, and cell surface staining mix was added containing pre-conjugated antibodies for 30 minutes at room temperature. Samples were washed twice with FACS buffer, then fixed using the FoxP3 Fix/Perm kit (eBiosciences) for 45 minutes at 4°C, followed by permeabilization wash. Samples were then incubated with the intracellular staining mix for 30 minutes at room temperature. Cells were washed with FACS buffer twice, and pellets were resuspended in Cell-ID™ Intercalator-Ir (Fluidigm) in MaxPar fixation solution (Fluidigm, catalogue no. 201192B) and refrigerated overnight, or for up to five days. Prior to acquisition, samples were washed twice with 1X PBS, and then washed twice with deionized water. Cell pellets were further resuspended in deionized water at 0.5 × 10^6 cells/mL and topped up with 10% calibration beads (EQ Four Element Calibration Beads, Fluidigm). All samples were acquired on the Helios Mass Cytometer (Fluidigm). Effector immune populations and activated T cells in the gut accumulate in a microbiota-dependent manner. Shifts in the microbiome according to dietary fiber source and content result in altered concentrations of B vitamins available to the host, which is tied to distinct alterations in innate and adaptive immune populations. </p>
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>
Experimental Factors Influence Diversity Metrics of the Gut Microbiome in Laboratory Mice
<p>Abstract<br> Introduction</p> <p>Gut microbiome studies often overlook experimental factors that could influence gut microbiome diversity and could impact findings. Large-scale studies investigating these experimental factors are lacking. Thus, we aimed to determine which experimental factors influence the gut microbiome diversity in pre-clinical animal model studies.</p> <p><br> Methods</p> <p>We extracted DNA and sequenced the V4 region of the 16S rRNA gene of a total of 538 samples from various sections of the gastrointestinal tract of 303 young and aged male and female C57BL/6J mice of three different genotypes on five diets from three animal house facilities. As a proof-of-concept in a disease model, some mice were treated with sham or angiotensin II, a commonly studied agent used as a hypertension model. Some samples were sequenced twice as a matched-comparison group.</p> <p>Results</p> <p>Using over 17 million sequencing reads, we found that experimental factors such as animal house facility, genotype, diet, age, sex, sampling site, and technical factor (i.e., sequencing batch) affected both α- and β-diversity (weighted and unweighted UniFrac), and were associated with compositional changes in the microbiome at varying magnitude, with diet and sampling site having the largest effect. After adjustment by these factors, treatment with angiotensin II had no impact on α-diversity and was only significant in unweighted UniFrac (presence/absence of bacteria) analyses.</p> <p><br> Conclusion</p> <p>Our data identified several key experimental and technical factors that affect the gut microbiome in laboratory mice. Our findings support that not accounting or adjusting for these factors may lead to false-positive discoveries and non-biologically relevant findings in the gut microbiome field.</p>
With or without you: Gut microbiota does not predict aggregation behaviour in females of the European earwig
<p>Recent studies suggest that the gut microbiota could be one of the main driving forces behind the evolution of group living. However, these studies are mainly based on our knowledge of species in which non-social individuals are rare and abnormal, calling into question the adaptive value of the reported association between group-living and gut microbiota. In this study, we addressed this issue by testing this association in females of the European earwig, an insect showing frequent, naturaland wide inter-individual variation in the expression of group living. We video-tracked 320 field-sampled females to quantify their natural variation in aggregation and then tested whether the most and least gregarious females had different gut microbiota. We also compared the general activity, boldness, body size and body condition of these females and examined the association between each of these traits and the gut microbiota. Contrary to our predictions, we found no difference in gut microbiota between the most and least gregarious females, as well as no difference between these females in terms of general activity, boldness, body size and condition. We did show that the gut microbiota of females was overall linked to their body condition, even though it was also unrelated to the other measurements. Overall, these results demonstrate that a host's gut microbiota is not necessarily a major driver of aggregation behaviour in species with inter-individual variation in group living and call for future studies to investigate the determinants and role of gut microbiota in earwigs.</p>
Cell metadata for "The emergent landscape of the mouse gut endoderm at single-cell resolution"
<p>Cell metadata for the data published in "The emergent landscape of the mouse gut endoderm at single-cell resolution"</p> <p> </p>
Animal Gut Microbiome (AGM) Data from 91 Published Studies for 224 Animal Species
Diversity and heterogeneity often are conflated but are fundamentally different. An aphorism proposed by Shavit and Ellison (2021; J. Phil. 118: 525–548) for distinguishing them is that “a zoo is diverse whereas an ecosystem is heterogeneous.” That is, a zookeeper measuring diversity simply enumerates the different types of animals; interactions are not expected to occur between animals separated by fences or other barriers. In contrast, measures of heterogeneity ought to include both interspecific interactions and relationships between species and their heterogeneous habitats. Here, we use cross-scale, dual scaling-law analyses of heterogeneity and diversity of animal gut microbiomes (AGMs) to address three objectives: (i) estimate the spatial heterogeneity and diversity of animal-gut microbiomes; (ii) analyze influences of phylogeny and diets on scaling of diversity and heterogeneity; (iii) explore mechanistic differences between diversity and heterogeneity in AGMs. From 4903 AGM samples collected from 318 animal species covering all six classes of vertebrates and four major classes of invertebrates, we estimated that ≈640,000 operational taxonomic units (OTUs or “species”) make up the pool of microbial species that could inhabit animal guts, among which ≈8000 are relatively common and ≈800 are dominant. The gut of any single animal, however, includes only 0.01–0.5% of the total species pool. We extended Ma’s diversity-area relationship for scaling diversity and extend Taylor’s Power Law and Luna et al.’s (2020; Diversity 12: 86) interaction diversity for scaling heterogeneity. At the community scale, phylogeny significantly influenced heterogeneity, but diets did not. Phylogeny and diets had limited influence on diversity at both community and landscape scales. Although two common measures of diversity—beta diversity and unevenness—commonly are synonymized with heterogeneity, our data lead us to conclude that diversity and heterogeneity measure two very different
Mummichog (Fundulus heteroclitus) counts and gut content analysis from lift trap transect collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
The lift traps were used to capture mummichogs accessing the high marsh platform. Mummichogs were collected to study the effect of marsh-edge geomorphology on mummichog distribution and foraging. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2016 and at Clubhead Creek in 2005 and from 2009 till 2016. Years 2017-2020 are enrichment recovery years.
Mummichog (Fundulus heteroclitus) gut content analysis from Breder trap transect collections in tidal creeks associated with long term fertilization experiments, Rowley, MA.
At PIE, mummichog (Fundulus heteroclitus) use the spring-cycle high tides to access the flooded high marsh platform and eat invertebrate prey, coupling the high marsh and aquatic creek food webs by gathering energy produced on the high marsh and making it available to the aquatic food web. Changes in the geomorphology of saltmarsh creek edges greatly influence the survival, biomass, and resource use of mummichog populations. Here we use gut content analysis assess the diet of mummichog on the high marsh platform during a flooding spring-cycle tide in July 2018 across 3 PIE creeks known to present different geomorphologic patterns in their low marsh zones. These data allow us to quantify the amount of terrestrial invertebrate prey mummichog consume on a single flooding tide and determine the impact altered low marsh geomorphology has on the trophic relationships in PIE food webs. These mummichog were captured in Breder traps; information about the consumer communities captured in these traps was recorded separately (LTE-TIDE-BrederTrap-Demographics). These data were included in part of the study “Habitat decoupling via saltmarsh creek geomorphology alters connection between spatially-coupled food webs” (Lesser et al. 2020) and were a portion of an MBL REU project.
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>
Data for "Gut microbial genes are associated with neurocognition and brain development in healthy children"
<p><strong>Datasets accompanying<em> Gut microbial genes are associated with neurocognition and brain development in healthy children</em>, submitted to Nature Microbiology.</strong></p> <p><strong>Contents:</strong></p> <ul> <li> fecal_samples_master.csv <ul> <li>Metadata for all fecal samples processed by the Klepac-Ceraj Lab at Wellesley College</li> </ul> </li> <li>filemakerdb.csv <ul> <li>Initial export and parsing (long form) of deidentified patient metadata from internal filemnaker pro database</li> </ul> </li> <li>gbm.txt <ul> <li>Info about potentially neuroactive gene sets</li> <li>This was acquired as Supplementary Dataset 1 from <a href="https://doi.org/10.1038/s41564-018-0337-x">https://doi.org/10.1038/s41564-018-0337-x</a></li> </ul> </li> <li>batchXXX_analysis_noknead.tar.gz <ul> <li>Sequencing batches 001-012 (see fecal_samples_master.csv for metadata about samples contained in each batch)</li> <li>Each tarball contains: <ul> <li><strong>cluster.yaml</strong>: configuration file for snakemake pipeline (<a href="https://github.com/Klepac-Ceraj-Lab/snakemake_workflows">repo link</a>)</li> <li><strong>config.yaml</strong>: run configuration for snakemake pipeline</li> <li><strong>.snakemake/</strong>: metadata about snakemake pipeline runs on engaging cluster at MIT</li> <li><strong>output/</strong>: outputs from metaphlan2 and humann2 analysis runs. Note: kneaddata sequence files were not included, but will be uploaded to SRA (link to come)</li> </ul> </li> </ul> </li> <li>All <a href="https://www.uniprot.org/">uniprot</a> searches were performed 2019-09-19 <ul> <li>uniprot-abxr.tsv <ul> <li>search term: "keyword:\"Antibiotic resistance [KW-0046]\" AND reviewed:yes"</li> </ul> </li> <li>uniprot-carbohydrate.tsv <ul> <li>search term: "keyword:\"Carbohydrate metabolism [KW-0119]\" AND reviewed:yes"</li> </ul> </li> <li>uniprot-fa.tsv <ul> <li>search term: (keyword:\"Fatty acid biosynthesis [KW-0275]\" OR keyword:\"Fatty acid metabolism [KW-0276]\") AND reviewed:yes"</li> </ul> </li> </ul> </li> </ul>
Helminths, polyparasitism, and the gut microbiome in the Philippines
<p>Uploaded here are the raw FASTA files associated with this publication. Labelled P1-10, and 11-220.</p> <p><a href="https://zenodo.org/api/files/e3103a63-45d0-4b24-9786-d4f3a0f6534b/OTU%20tables%2C%20annotations%20and%20code%20book.zip">OTU tables, annotations and code book.zip</a> Annotations and OTU files for International Journal of Parasitology paper "Helminths, Polyparasitism, and the Gut Microbiome in the Philippines". Data set from human helminth infections from Palapag, the Philippines. Code book contains information on headings for the annotation file. OTUs generated by AGRF. Originally uploaded here; (<a href="http://dx.doi.org/10.17632/59j46prhvf.1">https://doi.org/10.17632/59j46prhvf.1</a>) </p> <p> </p> <p> </p>
Psoriasis is associated with elevated gut IL-1α and intestinal microbiome alterations
<p>Background: Psoriasis is a chronic inflammatory condition that predominantly affects the skin and is associated with extracutaneous disorders, such as inflammatory bowel disease and arthritis. Changes in gut immunology and microbiota are important drivers of proinflammatory disorders and could play a role in the pathogenesis of psoriasis. Therefore, we explored whether psoriasis in a Central Asian cohort is associated with alterations in select immunological markers and/or microbiota of the gut. Methods: We undertook a case-control study of stool samples collected from outpatients, aged 30-45 years, of a dermatology clinic in Kazakhstan presenting with plaque, guttate or palmoplantar psoriasis (n=20), and age-sex matched subjects without psoriasis (n=20). Stool supernatant was subjected to multiplex ELISA to assess the concentration of 47 cytokines and immunoglobulins and to 16S rRNA gene sequencing to characterize microbial diversity in both psoriasis participants and controls. Results: The psoriasis group tended to have higher concentrations of most analytes in stool (29/47=61.7%) and gut IL-1α was significantly elevated (4.19-fold, p=0.007) compared to controls. Levels of gut IL-1α in the psoriasis participants remained significantly unaltered up to three months after the first sampling (p=0.430). Psoriasis was associated with alterations in gut Firmicutes, including elevated Faecalibacterium and decreased Oscillibacter and Roseburia abundance, but no association was observed between gut microbial diversity or Firmicutes/Bacteroidetes ratios and disease status.<br> Conclusions: Psoriasis may be associated with gut inflammation and dysbiosis. Studies are warranted to explore the use of gut microbiome-focused therapies in the management of psoriasis in this under-studied population.</p>
A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog - Supporting Data
<p><strong>Description </strong></p> <p>The following files are available : </p> <ul> <li>Simulated non-redundant Gene Catalog (SGC) composed of 128267 genes;</li> <li>Gene abundance profiles across 40 samples: raw read counts, gene length normalized base counts, depth file computed by the jgi_summarize_bam_contig_depth script provided by MetaBAT;</li> <li>Gold Standard (GS) and Gold Standard Single Assignment (GS_SA) binning results;</li> <li>Binning results obtained on the SGC with nine binning methods: MSPminer, MGS-canopy, DAS Tool, MaxBin2, MetaBAT2, SolidBin, CONCOCT, COCACOLA and MyCC.</li> </ul> <p><strong>License</strong></p> <p>These files are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
Kin selection explains the evolution of cooperation in the gut microbiota, by Simonet & McNally, 2020, Dataset S1 and codes for statistical analysis and figures production
<p>Dataset S1 contains all raw and processed material referred to in the published article "Kin selection explains the evolution of cooperation in the gut microbiota". R codes files provide all codes to replicate the analysis. Please refer to the README file for a description of all code files. The manifest files are those obtained by accessing the HMP portal on April 2020 under Project > HMP, Body Site > feces, Studies>WGS-PP1, File Type > WGS raw sequences set, File format > FASTQ.</p> <p>We also provide access to these data and codes at our GitHub (https://github.com/CamilleAnna/HamiltonRuleMicrobiome gitRepos.git) which can be cloned to directly re-run this analysis. </p> <p><strong>Legends for Dataset S1:</strong></p> <ul> <li>Sheet 1: Metagenomic samples used and access links.</li> <li>Sheet 2: Reference on bacterial cooperation retrieved from Web of Science search: TI¯((microb* OR bacter* OR microorganis* OR micro-organis*) AND (coop* OR social*)</li> <li>Sheet 3: Retained bacteria cooperation keywords</li> <li>Sheet 4: GOs identified by annotating all MIDAS database genomes (5944 genomes) with PANNZER2.</li> <li>Sheet 5: Full list of potential bacterial cooperation GO terms and description of manual curation decisions.</li> <li>Sheet 6: Final list of bacterial cooperation GO used for the analysis</li> <li>Sheet 7: Genomic diversity of the bacterial population within and across host. Computed from MIDAS snp_diversity.py pipeline.</li> <li>Sheet 8: final dataset for statistical analysis.</li> <li>Sheet 9: per-gene annotation of cooperation.</li> </ul>
Supplementary Data: Global fits of GUT-scale SUSY models with GAMBIT (arXiv:1705.07935)
<p>Supplementary Data</p> <p><em>Global fits of GUT-scale SUSY models with GAMBIT</em><br> <em>arXiv:1705.07935</em></p> <p>The files in this record contain data for the CMSSM, NUHM1 and NUHM2 models considered in the GAMBIT "Round 1" GUT-scale SUSY paper.</p> <p>For each model, there are</p> <ul> <li>A number of YAML files, each corresponding to a different set of sampling parameters and/or priors</li> <li>A set of YAML files used for postprocessing: CMSSM_intermediate.yaml, CMSSM.yaml, NUHM1.yaml and NUHM2.yaml</li> <li>A final hdf5 file, containing the combined results of all sampling runs</li> <li>An example pip file, for producing plots from the hdf5 file using pippi</li> <li>SLHA1 and SLHA2 files for the best-fit point in each subregion of the fit. These can be found inside the tarball best_fits_SLHA.tar.gz.</li> </ul> <p>The record also contains</p> <ul> <li>StandardModel_SLHA2_scan.yaml and StandardModel_SLHA2_postprocessing.yaml, two universal YAML fragments included from other yaml files</li> <li>gambit_preamble.py, a collection of python functions used for in-line data processing in the pip files</li> </ul> <p>The different YAML files corresponding to different samplers and/or priors follow the naming scheme [model]_[scanner]_[prior]_[slice]_[special].yaml, where</p> <ul> <li>model = CMSSM, NUHM1, NUHM2</li> <li>scanner = Diver, MN</li> <li>prior = log, flat</li> <li>slice = pmu, nmu (positive or negative mu)</li> <li>special = sqcoann, slcoann, [blank] (squark co-annihilation, slepton co-annihilation, or bulk)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>For each model, the final hdf5 results file included here was generated in the following way:</p> <ul> <li>carry out initial runs using YAML files following the naming scheme above</li> <li>combine the resulting hdf5 output files into a single file, using gambit/Printers/scripts/combine_hdf5.py</li> <li>postprocess the samples to remove all points more than 5 sigma from the current best fit, using [model]_strip.yaml</li> <li>postprocess the samples to include a new likelihood term for LHC Run II searches, and to recompute the FlavBit likelihoods (these were buggy in a pre-release version of GAMBIT). For the CMSSM, this happened in two steps, due to persistent flavour bugs, using CMSSM_intermediate.yaml and CMSSM.yaml. For the NUHM1 and NUHM2, this was done in a single step each, using NUHM1.yaml and NUHM2.yaml.</li> </ul> </li> <li> <p>It is not necessary to repeat the steps listed in point 1 when running new scans; the LHC Run II likelihoods can be included in the original YAML file, so that no postprocessing step is required.</p> </li> <li> <p>The YAML files that we give here are updated compared to the ones that we used when generating the hdf5 file, in order to match the set of available options in the release version of GAMBIT 1.0.0. The included physics and numerics are however identical.</p> </li> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.0.0, and the pip files are tested with pippi 2.0, commit 2ab061a8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file for each model is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don't expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis (supporting data)
<div> <div>This archive contains the supporting data and code for <a href="https://doi.org/10.1093/ismeco/ycaf089" target="_blank" rel="noopener">Meilander et al., 2024: <em>Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis</em></a>.</div> <div> </div> <div><strong>Clicking the links below will open the corresponding files using QIIME 2 View (<a href="https://view.qiime2.org" target="_blank" rel="noopener">https://view.qiime2.org</a>). </strong></div> <div> </div> <div> <div> <div>Summaries of master data files:</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/asv-table.qzv/content" target="_blank" rel="noopener">Summary of master feature table (<code>asv-table.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/sample-metadata.qzv/content">Tabulated view of sample metadata (<code>sample-metadata.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/asv-seqs-ms10.qzv?download=1" target="_blank" rel="noopener">Summary of ASV sequences observed in at least 10 samples: (<code>asv-seqs-ms10.qzv</code>)</a></div> <div> </div> <div>PCoA plots:</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/braycurtis.qzv/content" target="_blank" rel="noopener">Bray-Curtis Emperor plot (<code>braycurtis.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/jaccard.qzv/content" target="_blank" rel="noopener">Jaccard Emperor plot (<code>jaccard.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/unweighted_unifrac.qzv/content" target="_blank" rel="noopener">Unweighted UniFrac Emperor plot (<code>unweighted_unifrac.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/weighted_unifrac.qzv/content" target="_blank" rel="noopener">Weighted UniFrac Emperor plot (<code>weighted_unifrac.qzv</code>)</a></div> <div> </div> <div>Taxonomy barplots:</div> <div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/taxa-bar-plots-bucket2-gtdb-r214.1-weighted-stool-taxonomy.qzv?download=1">Taxonomy bar plot for Bucket 2 only (<code>taxa-bar-plots-bucket2-gtdb-r214.1-weighted-stool-taxonomy.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/taxa-bar-plots-bucket3-gtdb-r214.1-weighted-stool-taxonomy.qzv?download=1" target="_blank" rel="noopener">Taxonomy bar plot for Bucket 3 only (<code>taxa-bar-plots-bucket3-gtdb-r214.1-weighted-stool-taxonomy.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/taxa-bar-plots-gtdb-r214.1-weighted-stool-taxonomy.qzv/content" target="_blank" rel="noopener">Taxonomy bar plot for all samples (<code>taxa-bar-plots-gtdb-r214.1-weighted-stool-taxonomy.qzv</code></a>)</div> </div> <div> </div> <div>q2-fmt "raincloud plots":</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/hec-raincloud.qzv/content">Raincloud plot (<code>hec-raincloud.qzv</code>)</a></div> <div> </div> </div> </div> <div> </div> <div>The linked <code>.qzv</code> files are also contained in the <code>gut-to-soil-qiime2.zip</code> zip file, along with all relevant data artifacts (<code>.qza</code> files).</div> <div>The <code>.qzv</code> files are also maintained outside of the <code>.zip</code> file to facilitate their viewing with QIIME 2 View.</div> </div> <div> </div> <div> <div>Code for generating figures 1 and 2 (and corresponding supplemental figures):</div> <div><code>gut-to-soil-manuscript-figures-main.zip</code> (also see: <a href="https://github.com/caporaso-lab/gut-to-soil-manuscript-figures" target="_blank" rel="noopener">https://github.com/caporaso-lab/gut-to-soil-manuscript-figures</a>)</div> <div> </div> <div>Code for generating ridgeline plots (Figure S6):</div> <div><code>gut-to-soil-ridgeline-plots-main.zip</code> (also see: <a href="https://github.com/caporaso-lab/gut-to-soil-ridgeline-plots" target="_blank" rel="noopener">https://github.com/caporaso-lab/gut-to-soil-ridgeline-plots</a>)</div> </div> <div> <div> </div> </div>
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.