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915 results for “metagenomics”
Metagenomic analysis suggests low in situ replication rates for dust-associated bacteria over the Red Sea
<p>Gff and fasta files of dust-associated MAGs that were analyzed on GRiD for estimation of in situ replication rates</p>
Fig. 3 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 3. Prevalence of pathogens associated with D. nuttalli. If there was only one study included in a certain pathogen, the positive rate would be calculated by the positive number of ticks divided by the total number of detected ticks, and without the 95% confidence interval. If there were more studies, the positive rate and 95% confidence interval would be calculated by meta-analysis.
Fig. 2 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 2. Study design and data sources of the meta-analysis. A comprehensive meta-analysis was performed to evaluate D. nuttalli's potential threats based on detected pathogens and geographical distribution positions. The database of D. nuttalli was constructed from four sources, including field surveys, literature review, a reference book, and an online biodiversity database (Global Biodiversity Information Facility, GBIF, https://www.gbif.org).
Fig. 1 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 1. Relative pathogen abundance of four D. nuttalli samples and the phylogenomic analysis of four Rickettsia genomes. (A) Pathogen abundance at the family level. (B) Pathogen abundance at the genus level. (C) The phylogenetic tree of four Rickettsia assemblies. The phylogenetic tree of four Rickettsia assemblies (Rickettsia conorii subsp. raoultii str XinjiangF1, Rickettsia conorii subsp. raoultii str XinjiangF2, Rickettsia conorii subsp. raoultii str XinjiangF3, and Rickettsia conorii subsp. raoultii str XinjiangM1) was built with 28 other publicly available established or proposed Rickettsiales species. The tree was inferred by IQ-TREE based on 277 single-copy orthologs identified by OrthoFinder. Anaplasma phagocytophilum and Ehrlichia ruminantium were two outgroup species.
Fig. 5 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 5. Global potential distribution of D. nuttalli. The red area indicates greater possibilities of suitability for D. nuttalli, while the blue area is less likely to be suitable for D. nuttalli.
Fig. 4 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 4. Geographical distribution of D. nuttalli. D. nuttalli lived mainly between 23◦–53◦ latitude and 76◦–133◦ longitude in the Northern Hemisphere. Triangles represent the locations in prefecture-level regions, while circles represent the distribution locations in county-level regions. The green circles represent points from GBIF, the yellow circles are points from literature, the purple circles represent the points from the field survey and the blue points are points from a reference book. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
MNBC: a multithreaded Minimizer-based Naïve Bayes Classifier for improved metagenomic sequence classification
<p>These files provide supplementary data underlying the article <a title="https://doi.org/10.1093/bioinformatics/btae601" href="https://doi.org/10.1093/bioinformatics/btae601" target="_blank" rel="noopener noreferrer nofollow">doi.org/10.1093/bioinformatics/btae601</a> (see Figure 1 in the article):</p> <ul> <li>37345_filtered_training_and_test_genomes_list.txt: Refseq assembly sequence filenames of the 37345 filtered training and test genomes</li> <li>taxonomy_37345_filtered_training_and_test_genomes.txt: Taxonomy file for all 37345 filtered training and test genomes</li> <li>Uniform_reference_database_31991_training_genomes_assemblyID_list.txt: Refseq assembly accessions of the 31991 training genomes in the uniform reference database</li> <li>uniform_reference_database.tar.gz_1 to uniform_reference_database.tar.gz_10: Merge them into a single file using the <em>cat</em> command. The folder produced by decompressing this file is the uniform reference database.</li> <li>taxonomy_uniform_reference_database.txt: Taxonomy file for the uniform reference database (i.e. the 31991 training genomes)</li> <li>5354_test_genomes_assemblyID_list.txt: Refseq assembly accessions of the 5354 test genomes</li> <li>testReads_NextSeq_C0.05.fasta.gz: 6562565 150bp-long positive reads randomly generated from the 5354 test genomes, simulating reads sequenced by NextSeq (0.05 coverge)</li> <li>testReads_MiSeq_C0.05.fasta.gz: 3282728 300bp-long positive reads randomly generated from the 5354 test genomes, simulating reads sequenced by MiSeq (0.05 coverge)</li> <li>testReads_Nanopore_C0.05.fasta.gz: 181912 positive reads of normally distributed 1kb-10kb lengths randomly generated from the 5354 test genomes, simulating reads sequenced by Nanopore (0.05 coverge)</li> <li>negaReads_NextSeq_C0.05.fasta.gz: 10143 150bp-long negative reads randomly generated from Chromosome 1 of the Arabidopsis Thaliana reference genome, simulating reads sequenced by NextSeq (0.05 coverge)</li> <li>negaReads_MiSeq_C0.05.fasta.gz: 5072 300bp-long negative reads randomly generated from Chromosome 1 of the Arabidopsis Thaliana reference genome, simulating reads sequenced by MiSeq (0.05 coverge)</li> <li>negaReads_Nanopore_C0.05.fasta.gz: 277 negative reads of normally distributed 1kb-10kb lengths randomly generated from Chromosome 1 of the Arabidopsis Thaliana reference genome, simulating reads sequenced by Nanopore (0.05 coverge)</li> <li>CAMI2_reference_database_16864_genomes_list.txt: Refseq assembly sequence filenames of the 16864 genomes and chromosomes in the reference database for CAMI2</li> <li>taxonomy_CAMI2_reference_database.txt: Taxonomy file for the CAMI2 reference database</li> </ul> <p><strong>Tip</strong>: To directly use the taxonomy file "taxonomy_uniform_reference_database.txt", please use version v1.1 or earlier of the MNBC tool. If using later versions it needs regenerating with the "MNBC taxonomy" program.</p>
Práctica Metagenómica: Tutorial de microbiomas usando QIIME2 / Practical Metagenomics: Microbiome tutorial with QIIME 2
<p>Data for the e-learning tutorial Práctica Metagenómica: Tutorial de microbiomas usando QIIME2 </p> <p>Datos para el tutorial e-learning <a href="https://f1000research.com/documents/10-798">Practical Metagenomics: Microbiome tutorial with QIIME 2</a></p> <p> </p>
Metagenomic investigation of the faecal microbiomes of great tits and blue tits - supplementary sequences
<h2>Overview:</h2> <p>The vertebrate gut microbiome plays crucial roles in host health and disease. However, there is limited data on the microbiomes of wild birds, most of which is restricted to barcode sequences. We therefore explored the use of shotgun metagenomics on the faecal microbiomes of two wild bird species widely used as model organisms in ecological studies: the great tit (<em>Parus major</em>) and the Eurasian blue tit (<em>Cyanistes caeruleus</em>). Mitochondrial genomes from the host and eukaryotic pathogens that were assembled from these metagenomes and are made available as a catalogue in this archive.</p> <h2>Methods:</h2> <p>Metagenomic reads were trimmed, and quality controlled using FastP configured to a minimum phred score of 20 and minimum length of 50 bp. Individual sample assemblies were performed on each metagenomic sample using MEGAHIT v1.2.9. The BLAST 2.16 suite of programs was downloaded from ftp.ncbi.nlm.nih.gov/blast and used to perform homology searches of the assemblies, using the makeblastdb utility to generate libraries, the blastn and tblastx utilities to search contigs with query sequences under high stringency (e value ≥1*e-200) and the blastdbcmd utility to retrieve hits from databases.</p> <h2>Files:</h2> <ul> <li>The <strong>Isospora_mitochondrial.fasta </strong>contains the fasta sequences for mitochondrial genomes of the bird pathogen Isospora sp, assembled from two faecal samples from the Great Tit (<em>Parus major</em>).</li> <li>The <strong>Cyanistes.caeruleus_mitochondrial.fasta</strong> contains the fasta sequences for mitochondrial genomes of the host bird, assembled from two faecal samples from the blue Tit (<em>Cyanistes caeruleus</em>).</li> <li>The <strong>Parus.major_mitochondrial.fasta</strong> contains the fasta sequences for mitochondrial genomes of the host bird, assembled from two faecal samples from the blue Tit (<em>Cyanistes caeruleus</em>).</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>
Metagenomics data of the bacterial community in Bemisia tabaci from Burkina Faso
<p>Microbial symbionts are widespread in insects and some of them have been associated to adaptive changes. Primary symbionts (P-symbionts) have a nutritional role that allows their hosts to feed on unbalanced diets (plant sap, wood, blood). Most of them have undergone genome reduction, but their genomes still retain genes involved in pathways that are necessary to synthesize the nutrients that their hosts need. However, in some P-symbionts, essential pathways are incomplete and secondary symbionts (S-symbionts) are required to complete parts of their degenerated functions. The P-symbiont of the phloem sap-feeder <i>Bemisia tabaci</i>, <i>Portiera aleyrodidarium,</i> lacks genes involved in the synthesis of vitamins, cofactors, and also of some essential amino-acids. Seven S-symbionts have been detected in the <i>B. </i>tabaci species complex. Phenotypic and genomic analyses have revealed various effects, from reproductive manipulation to fitness benefits, notably some of them have complementary metabolic capabilities to <i>Portiera</i>, suggesting that their presence may be obligatory. In order to get the full picture of the symbiotic community of this pest, we investigated, through metabarcoding approaches, the symbiont content of individuals from Burkina Faso, a West African country where <i>B. tabaci</i> induces severe crop damage. While no new putative <i>B. tabaci </i>S-symbiont was identified, <i>Hemipteriphilus, </i>a symbiont only described in <i>B. tabaci</i> populations from Asia<i>,</i> was detected for the first time on this continent. Phylogenetic analyses however reveal that it is a different strain than the reference found in Asia. Specific diagnostic PCRs showed a high prevalence of these S-symbionts and especially of <i>Hemipteriphilus </i>in different genetic groups. These results suggest that <i>Hemipteriphilus</i> may affect the biology of <i>B. tabaci</i> and provide fitness advantage in some <i>B. tabaci</i> populations.</p>
Catalog of metagenome-assembled bacterial genomes from Antarctic endolithic communities
<p>The dataset consists of 2 rar archives and 2 files ( tab-separated values ). Here is a brief summary of their contents:</p> <ul> <li><strong>MAGs_taxonomy: </strong>GTDB classification for each MAG.</li> <li><strong>MAGs_genome_info: </strong>genome size, completeness, contamination, length, N50.</li> <li><strong>MAGs - candidate species: </strong>high quality (HQ) and medium quality (MQ) bacterial metagenome assembled genomes.</li> <li><strong>MAGs_Annotation: </strong>EggNOG annotation files. For each MAG, the following files are included: <ul> <li>eggnog.emapper.annotations: the final EggNOG annotation;</li> <li>eggnog.emapper.hmm_hits: list of significant hits to eggNOG Orthologous Groups</li> <li>eggnog.emapper.seed_orthologs: best match of each query within the best Orthologous Group (OG) reported in the eggnog.emapper.hmm_hits file<strong>.</strong></li> </ul> </li> </ul>
Shotgun metagenomic sequencing dataset of a synthetic mock community containing 20 genomes spiked-in at even and staggered concentrations.
<p>Shotgun metagenomics (SM) sequencing is a popular method used in microbial ecology to obtain insights on microbial community structure and function potential in a given biological system without the need to cultivate microorganisms. The dataset described in this article describes technical triplicates of shotgun metagenomic sequence libraries generated from two purified and titrated mixes of 20 distinct reference bacterial genomes for which key characteristics such as genome size, sequence and spiked-in concentrations are known. In one of the genomic DNA mix, each genome is spiked-in at similar concentrations (representing an even microbial community) and in the other, genomes are spiked-in at different concentrations with some genomes highly abundant and other in low quantity, mimicking an uneven microbial community DNA extract. In order to be interpretable, SM sequencing data needs to be properly analyzed by complex analytical bioinformatic pipelines. Environments investigated with this method can range from simple to very complex. Typically, microbial communities contain microbes that are ubiquitous and some others much rarer. Analysis of rare microbes in a complex microbial community are challenging to perform as their sequencing signals get submerged by the microbial genomes that are more abundant. In this context, it is critical to have access to sequencing data of simple mock communities of mixes of well characterized genomes in order to develop and validate bioinformatic methods that aim to accurately analyze microbial communities.</p>
Metagenome assembled genome database of a human cohort and fecal reactors
<p><strong>HumanCohort_annotations.tsv.zip:</strong> This is the custom MAG database (n=2447 MAGs) and corresponding annotations that were used in Borton 2022: "Targeted curation of the gut microbial gene content modulating human cardiovascular disease". The citation will be updated upon publication of the manuscript. Metagenome assembled genomes were generated from fecal metagenomes derived from a 54 person cohort and anoxic methylated amine enrichments. </p> <p><strong>HumanCohortmetabolism_summary.xlsx.zip: </strong> This is the annotation summary for 2447 MAGs in the cohort database. </p> <p><strong>Quality_Abundance_CohortMAGs.xlsx: </strong>This is a genome inventory of the 2447 MAGs in the cohort database including genome statistics and relative abundance. </p> <p><strong>orig_1D_NMR_fids.zip: </strong>NMR data derived from anoxic methylated amine enrichments. </p>
Databases for MyCodentifier: A tool for routine identification of nontuberculous mycobacteria using MGIT enriched shotgun metagenomics.
<p>Databases used for MyCodentifier a Nextflow pipeline to identify Mycobacterium tuberculosis complex (MTBC) and Nontuberculous mycobacteria (NTM) species from Next-generation sequencing (NGS) data.<br> <br> <strong>Short description:</strong><br> The pipeline is constructed using nextflow as workflow manager running in a docker container. It is able to identify species of MTBC/NTM from positive Mycobacterial Growth Indicator Tube (MGIT) cultures. To do so it uses an hsp65 database for fast identification coupled with a Metagenomic method using centrifuge to identify on genome level. For TB it also is able to identify subspecies. Results are presented in automated pdf and html reports.</p> <table> <caption><strong>Databases</strong></caption> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Short Description</strong></td> </tr> <tr> <td>20220726_ref.tar.gz</td> <td>7 major mycobacterial genomes as centrifuge classification database, used for reference-based mapping and genotype resistance prediction</td> </tr> <tr> <td>20220726_wgs_centrifuge_db_Radboudumc_MB.tar.gz</td> <td>centrifuge classification database using Tortoli <em>et al</em> 2017 Mycobacterium strains + additional strains</td> </tr> <tr> <td>genomes.tar.gz</td> <td>7 major mycobacterial genomes, annotation and Genbank files. Files are paired with 20220726_ref.tar.gz</td> </tr> <tr> <td>snpEff.tar.gz</td> <td>7 major mycobacterial genomes annotation models for snpEff.</td> </tr> <tr> <td>Tortoli_etal_hsp65.tar.gz</td> <td>KMA database of hsp65 gene extractions of the Tortoli <em>et al</em> 2017 Mycobacterium strains.</td> </tr> <tr> <td> <p>Used in the study:<br> p_compressed+h+v.tar.gz (12/06/2016)</p> </td> <td> <p>Databases available via <a>ftp://ftp.ccb.jhu.edu/pub/infphilo/centrifuge/data</a> or <a href="https://ccb.jhu.edu/software/centrifuge/manual.shtml#custom-database">https://ccb.jhu.edu/software/centrifuge/manual.shtml#custom-database</a></p> </td> </tr> </tbody> </table> <p><strong>MyCodentifier Github:</strong></p> <p><a href="https://jordycoolen.github.io/MyCodentifier/">https://jordycoolen.github.io/MyCodentifier/</a></p> <p> </p> <p> </p>
Genetic Features of the Marine Polychaete Sirsoe methanicola from Metagenomic Data
<p>WebAUGUSTUS input and output data used in for eukaryotic gene prediction in <em>Sirsoe methanicola</em>:</p> <p><strong>WebAUGUSTUS input data:</strong></p> <ol> <li>capitella.fa - Nucleotide genomic sequence of <em>Capitella teleta</em> (NCBI accession: GCA_000328365.1)</li> <li>capitella-protein.faa - Protein sequences in the <em>Capitella teleta </em>genome (NCBI accession: GCA_000328365.1)</li> <li>big-contigs-wrapped.fa - Contigs >= 3,000 bp long assembled from the <em>S. methanicola </em>metagenomes (NCBI BioProject ID PRJNA689840) that did not bin into any bacterial MAGs</li> <li>small-contigs-wrapped.fa - Contigs< 3,000 bp long assembled from the <em>S. methanicola </em>metagenomes (NCBI BioProject ID PRJNA689840) that did not bin into any bacterial MAGs</li> </ol> <p><strong>WebAUGUSTUS output data:</strong></p> <ol> <li>augustus.bigcontigs.gff - WebAUGUSTUS GFF output file for contigs >=3,000 bp</li> <li>augustus.smallcontigs.gff - WebAUGUSTUS GFF output file for contigs <3,000 bp</li> <li>augustus.all.faa - All protein sequences predicted from the <em>S. methanicola </em>metagenomes using WebAUGUSTUS</li> </ol>
Investigation of machine learning algorithms for taxonomic classification of marine metagenomes
<p>Training, testing, and blind datasets used for machine learning algorithms for taxonomic classification of marine metagenomes:</p> <ol> <li><strong>K12.kmers.txt</strong> - 12bp k-mer vocabulary constructed by Jellyfish v1.1.11 from 47,894 genomes in GTDB release 202</li> <li><strong>MarRef_1.6.tsv</strong> - Metadata file downloaded from MarRef v1.6</li> <li><strong>MarRef.genustrain.fasta</strong> - Training set from MarRef v1.6 (seed=808) used for genus classification</li> <li><strong>MarRef.genustest.fasta</strong> - Testing set from MarRef v1.6 (seed=747) used for genus classification </li> <li><strong>MarRef.speciestrain.fasta</strong> - Training set from MarRef v1.6 (seed=808) used for species classification</li> <li><strong>MarRef.speciestest.fasta</strong> - Testing set from MarRef v1.6 (seed=747) used for species classification</li> <li><strong>MarRef.traintest.key.tsv</strong> - Table containing MarRef accession, GenBank accession, GenBank taxonomy ID, taxonomic information, and labels used for species and genus testing and training</li> <li><strong>anonymous_reads_*.fq</strong> - Blind datasets (1-10) in interleaved fastq format</li> <li><strong>reads_mapping_*.tsv</strong> - Key for blind datasets 1-10. Each sequence header is mapped to its corresponding MarRef accession and NCBI taxonomic ID.</li> </ol>
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
<p>Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at <a href="http://bench.cs.vt.edu/deeparg">http://bench.cs.vt.edu/deeparg</a>.</p>
Datasets for Evaluating Metagenomic Phage Detection Tools
<p>These datasets include sequences that can be used for evaluating computational tools that detect bacteriophage in metagenomes.</p> <p>Datasheets are supplied for each dataset, and a README describes how to extract each dataset. Once the directories are extracted, there is a README in each directory describing the individual dataset.</p>
Metagenome-Assembled Genome DRAM Annotations (EMERGE 97% dereplicated MAGs)
<p>This is the combined DRAM annotation outputs for the 1,864 97% dereplicated metagenome-assembled genomes from Stordalen Mire, Sweden. </p> <ul> <li>1864_97percentmags_annotations_combined.tsv.gz</li> <li>1864_97percentmags_metabolism_summary.xlsx</li> <li>product_0.html</li> <li>product_1.html</li> </ul> <p>METHODS:</p> <p>MAGs were annotated and distilled using DRAM (v1.4.0).</p> <p>FUNDING:<br> This research is a contribution of the EMERGE Biology Integration Institute ((https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br> This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/fics.proj.2017.49950/60006215 and 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (<a href="https://www.google.com/url?q=https://ror.org/04xm1d337&sa=D&source=docs&ust=1674859614742521&usg=AOvVaw2XgXYw9eI4JIXRMKn3S9Se">https://ror.org/04xm1d337</a>) and the Environmental Molecular Sciences Laboratory (<a href="https://www.google.com/url?q=https://ror.org/04rc0xn13&sa=D&source=docs&ust=1674859614742655&usg=AOvVaw3UXdoHIFmVjc-mXUhDXYQt">https://ror.org/04rc0xn13</a>), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).</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.