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19,342 results for “Sequences”
Mitochondrial genome sequencing and analysis of the invasive Microstegium vimineum: a resource for systematics, invasion history, and management
<p>Table S1: Accession data for Microstegium samples included in this study.</p> <p>File S1: Alignment of Mitochondrial CDS for Poales mitochondrial sequences.</p> <p>File S2: SNP data for Microstegium vimineum mitochondrial variants.</p> <p>Figure S1: Transposable element content in the Microstegium vimineum mitogenome.</p> <p>Figure S2: Summary of Kraken2 output.</p> <p> </p>
Representative sequences for colorectal cancer additive log ratios
<p>Representative sequences and corresponding feature rank tables calculated in Debelius et al, The local tumor microbiome is associated with survival in late-stage colorectal cancer patients (doi: https://doi.org/10.1101/2022.09.16.22279353)</p> <p> </p> <p>file_s1_tissue_alr_sequences.fasta goes with tissue_only_ranks.tsv and corresponds to Figure 1 in the manuscript.</p> <p>file_s2_interaction_dr_sequences.fasta corresponds to figure 2 and links to interaction_models_interaction_ranks.tsv and interaction_models_tissue_ranks.tsv</p> <p> </p> <p>file_s3_tumor_rPCA_alr_sequences.fasta corresponds to Figure 3 and links to tumor_survival_index_ranks_file3.tsv.</p> <p> </p> <p>The rank files provide the rank and rank group; the fasta files provide the reference sequences for the data. </p>
Supplementary dataset to publication: "Genomic insight into Campylobacter jejuni isolated from commercial turkey flocks in Germany using whole-genome sequencing analysis"
<p><em>Campylobacter jejuni </em>is a zoonotic bacterium of public health significance. The present investigation was designed to assess the epidemiology and genetic heterogeneity of <em>Campylobacter jejuni</em> recovered from commercial turkey farms in Germany using whole-genome sequencing. The Illumina MiSeq<sup>®</sup> technology was used to sequence 66 <em>Campylobacter jejuni </em>isolates obtained between 2010 and 2011 from commercial meat turkey flocks located in ten German federal states. Phenotypic antimicrobial resistance was determined. Phylogeny, resistome, plasmidome and virulome profiles were analyzed using whole-genome sequencing data. Genetic resistancemarkers were identified with bioinformatics tools (AMRFinder, ResFinder, NCBI and ABRicate) and compared with the phenotypic antimicrobial resistance.</p>
Multiple Sequence Alignment of a diverse dataset with 1788 Mycobacterium tuberculosis isolates
<p><strong>Multiple Sequence Alignment of a diverse dataset with 1788 <em>Mycobacterium tuberculosis</em> isolates used for <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> benchmarking</strong></p> <p>The dataset comprises whole-genome sequence data published by <a href="https://doi.org/10.1016/S1473-3099(15)00062-6">Walker et al. 2015</a>. For the multiple sequence analysis, we proceeded as follows:</p> <ol> <li>Reads were downloaded from ENA BioProject <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA282721">PRJNA282721</a> (accessed on March 16<sup>th</sup>, 2023) and trimmed using Trimmomatic (<a href="https://pubmed.ncbi.nlm.nih.gov/24695404/">Bolger et al., 2014</a>) with <a href="https://github.com/B-UMMI/INNUca">INNUca</a> default settings;</li> <li>Quality-processed reads were individually mapped against the H37Rv reference genome (Genbank accession: <a href="https://www.ncbi.nlm.nih.gov/nuccore/NC_000962.3/">NC_000962.3</a>) using <a href="https://github.com/tseemann/snippy">Snippy</a> v4.5.1 and SNP-calling was performed on variant sites with the following criteria: a minimum proportion of reads differing from the reference of 70%, a minimum mapping quality of 30 and a minimum coverage for SNP calling of 10;</li> <li>A full alignment was extracted using Snippy’s core module (snippy-core), with masking of SNPs falling within known <em>M. tuberculosis</em> genomic regions with high GC content, repetitive elements and resistance-associated positions (corresponding to ~8% of the genome), as previously described for surveillance purposes (<a href="https://pubmed.ncbi.nlm.nih.gov/30948181/">Macedo et al., 2019</a>);</li> <li><em>M. tuberculosis </em>lineages were determined using tb-profiler v4.4.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31234910/">Phelan et al., 2019</a>), with samples from the <em>M. tuberculosis</em> complex other than <em>M. tuberculosis</em>, representing a mix of multiple lineages, or with less than 95% of mapped positions in the reference, being excluded;</li> <li>A filtered alignment comprising the maximum number of informative sites (88,562 nucleotide sites with at least one mutation in a given sequence) was extracted from the full alignment using the alignment_processing.py v1.1.0 (default settings) of <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a>, and then used as input for the benchmarking.</li> </ol> <p>In this repository, we provide two alignment files:</p> <ul> <li>Core_MTB_1787_strs.full.aln: this corresponds to the full multiple sequence alignment comprising 1787 samples and the reference (corresponding to the point 4 of the methodology).</li> <li>MTb_original_align_profile.fasta: this corresponds to the multiple sequence alignment comprising 1787 samples and the reference and only presenting the alignment informative sites (corresponding to the point 5 of the methodology)</li> </ul>
Lepidoptera genomics based on 88 chromosomal reference sequences informs population genetic parameters for conservation
<p>This repository contains (1) germline mutations called by the DeepVariant (v1.1.0) pipeline in VCF format; (2) rejected substitution scores calculated by the Genomic Evolutionary Rate Profiling (GERP++) software on each species and chromosome; and (3) the phylogenetic tree used as guide tree in the Cactus alignment.</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>
Data For: Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data
<p>Simulation output and Genome-wide scan for nIBD variants in UK10K data as reported in:</p> <p>Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data</p> <p>Johnson KE, Adams CJ, Voight BF. Methods Ecol Evol 2022 Nov;13(11): 2429–2442.</p> <p>Code available at: https://github.com/kelsj/EVICORD</p>
Dataset of backprojection and MT inversion results for 2023 Türkiye earthquake sequence
<p>This dataset contains the results of the back-projection analysis of the two Mw 7.7 and 7.6 earthquakes on February 6, 2023 in south-eastern Türkiye and the centroid moment tensor inversion results for one foreshock and 221 aftershocks. All methods and results are described in detail in the publication: Petersen et al., (2023): The 2023 SE Türkiye seismic sequence: Rupture of a complex fault network. The Seismic Record, 3 (2): 134-143. <a href="https://doi.org/10.1785/0320230008">https://doi.org/10.1785/0320230008</a></p>
Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study VCF files
<p>VCF files used and cited in the article: Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study</p>
The $E_2$ page of the 2-primary Adams spectral sequence in a large range
<p>This dataset contains a description of the cohomology of the 2-primary Steenrod algebra in a large range. Broadly, it contains</p> <ul> <li>complete additive data up to stem 256,</li> <li>complete multiplicative and secondary data up to stem 200 (including the Adams <span class="math-tex">\(d_2\)</span> differentials).</li> </ul> <p>This data was generated using `<a href="https://github.com/SpectralSequences/sseq/tree/master/ext">ext-rs</a>` running on the Wayne State Grid. The Grid is a high-performance computing cluster operated by Wayne State University Computing & Information Technology. See the accompanying document for a more detailed breakdown of the timeline and the resource usage.</p> <p>This repository is comprised of several files.</p> <ul> <li>`rank.csv`: The rank of the cohomology in each bidegree. This data is complete up to and including stem 256 and homological degree 132. The bidegrees with rank zero are omitted. The columns are as follows: <ul> <li>`stem`: The stem <span class="math-tex">\(n\)</span>.</li> <li>`Adams_filtration`: The homological degree <span class="math-tex">\(s\)</span>.</li> <li>`dimension`: The rank of the cohomology at the given stem and homological degree.</li> </ul> </li> </ul> <p>We use `rank.csv` to define a basis for the cohomology. The symbol <span class="math-tex">\(x_{n,s,i}\)</span> refers to the <span class="math-tex">\(i\)</span>th basis element in bidegree <span class="math-tex">\((n,s)\)</span>, where <span class="math-tex">\(n\)</span> is the stem and <span class="math-tex">\(s\)</span> is the homological degree.</p> <ul> <li>`d2.csv`: The Adams <span class="math-tex">\(d_2\)</span> differentials. This data is complete up to and including stem 200 and homological degree 128. If the target bidegree is zero-dimensional, the differentials are omitted. The columns are as follows: <ul> <li>`stem`: The stem <span class="math-tex">\(n\)</span>.</li> <li>`Adams_filtration`: The homological degree <span class="math-tex">\(s\)</span>.</li> <li>`index`: The index <span class="math-tex">\(i\)</span> in the basis of the bidegree <span class="math-tex">\((n,s)\)</span> given by `rank.csv`.</li> <li>`target`: The value of <span class="math-tex">\(d_2(x_{n,s,i})\)</span>.</li> </ul> </li> <li>`products.csv`: The multiplication table of the product structure with respect to our basis. This data is complete for all pairs of elements whose product is in stem at most 200 and homological degree at most 128. The pairs of elements that multiply to zero are omitted. The columns are as follows: <ul> <li>`stem_1`, `Adams_filtration_1`, `index_1`: The stem <span class="math-tex">\(n_1\)</span>, homological degree <span class="math-tex">\(s_1\)</span>, and index <span class="math-tex">\(i_1\)</span> of the first element.</li> <li>`stem_2`, `Adams_filtration_2`, `index_2`: The stem <span class="math-tex">\(n_2\)</span>, homological degree <span class="math-tex">\(s_2\)</span>, and index <span class="math-tex">\(i_2\)</span> of the second element.</li> <li>`product`: The vector representing the product <span class="math-tex">\(x_{n_1, s_1, i_1} \cdot x_{n_2, s_2, i_2}\)</span> in bidegree <span class="math-tex">\((n_1 + n_2, s_1 + s_2)\)</span>.</li> </ul> </li> <li>`differentials.txt.zst`: The differentials in our minimal resolution, in a human-readable plain text format. The output was generated using <a href="https://github.com/SpectralSequences/sseq/blob/v0.0.4/ext/examples/differentials.rs">this</a> example code. It is compressed using Zstandard for convenience, and decompresses to a 64 GiB file.</li> <li>`change_of_basis.csv`: The correspondence between our basis and the Bruner-Rognes (BR) basis defined in <a href="https://arxiv.org/abs/2109.13117">arXiv:2109.13117</a>. Each row expresses one of our basis elements in terms of the BR basis. The columns are as follows: <ul> <li>`stem`: The stem <span class="math-tex">\(n\)</span>.</li> <li>`Adams_filtration`: The homological degree <span class="math-tex">\(s\)</span>.</li> <li>`index`: The index <span class="math-tex">\(i\)</span> in the basis given by `rank.csv` in bidegree <span class="math-tex">\((n,s)\)</span>.</li> <li>`BR`: The vector in the BR basis corresponding to our element <span class="math-tex">\(x_{n,s,i}\)</span>.</li> </ul> </li> </ul> <p>All CSV files are in UTF-8 encoded plain text. Unless otherwise noted, the entries are ordered by stem, then homological degree, then index.</p> <p>v1.0.1: We fixed an error in the `products.csv` file. Due to a software error, some products involving elements in bidegrees <span class="math-tex">\((0,9)\)</span>, <span class="math-tex">\((9,5)\)</span>, <span class="math-tex">\((11,5)\)</span>, <span class="math-tex">\((11,6)\)</span>, <span class="math-tex">\((20,5)\)</span>, <span class="math-tex">\((25,9)\)</span>, <span class="math-tex">\((30,9)\)</span>, <span class="math-tex">\((31,9)\)</span>, and <span class="math-tex">\((33,5)\)</span> were missing. We emphasize that none of the data that was already present in the original file was incorrect.</p> <p>Moreover, we have checked that our multiplication table is compatible with Bruner and Rognes' data (<a href="https://doi.org/10.11582/2022.00015">doi:10.11582/2022.00015</a>), and satisfies the relations described by Lin in (<a href="https://doi.org/10.11582/2022.00015">doi:10.5281/zenodo.7786290</a>).</p>
Data for: Regularized sequence-context mutational trees capture variation in mutation rates across the human genome
<p>Additional data on output models from Bayer as reported in:</p> <p>Regularized sequence-context mutational trees capture variation in mutation rates across the human genome</p> <p>Adams CJ, Conery M, Auerbach BJ, Jensen ST, Mathieson I, Voight BF. BioRxiv https://doi.org/10.1101/2022.10.14.512160</p> <p>Accepted, PLoS Genetics. </p> <p>Code Available at: https://github.com/bvoightlab/Baymer</p>
Joint embedding of vertebrate brain single-cell RNA-Seq using sequence or structure
<p>Embeddings of single-cell RNA-Seq data from three adult vertebrate brain datasets into Orthogroup feature space or Structural cluster feature space. Orthogroups were generated using OrthoFinder v5.5.0; Structural clusters were assigned by using FoldSeek to cluster AlphaFold-v4 structural predictions.<br> <br> The three datasets used as the basis for these embeddings were:</p> <ul> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3768152">"Brain8"</a> from the <a href="https://www.frontiersin.org/articles/10.3389/fcell.2021.743421/full">Jiang et al. 2021</a> zebrafish cell atlas (files beginning with GSM3768152)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2906405">"Brain1"</a> from the <a href="https://www.sciencedirect.com/science/article/pii/S0092867418301168#sec4">Han et al. 2018</a> mouse cell atlas (files beginning with GSM2906405)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM6214268">"Xenopus_brain_COL65"</a> from the <a href="https://www.nature.com/articles/s41467-022-31949-2">Liao et al. 2022</a> Xenopus laevis adult cell atlas (files beginning with GSM6214268)</li> </ul> <p>For each dataset, we also generated a standardized cell type annotation file based on the author's originally provided cell type annotation data. The first column is the cell barcode for that species and the second column is the original study's cell type annotation for that cell.</p> <p>For the Xenopus brain data, we removed around ~18k cells that were not annotated in the original data to simplify data analyses - these are reflected in the files with the "subsampled" suffix. Subsampled versions of the data are also available for the joint embedding space (prefixed with "DrerMmusXlae").</p> <p>For the final datasets used in our analyses, we also provide features x cell matrices as .h5ad files for smaller file sizes and faster loading using Scanpy. </p> <p>For visualizing our UMAP plots of our top200 embedding space, we provide ".tsv" files with a variety of metrics and the x and y positions of each cell in the UMAP. See "DrerMmusXlae_adultbrain_FoldSeek_plotlydata.tsv" and "DrerMmusXlae_adultbrain_OrthoFinder_plotlydata.tsv"</p> <p>These data are part of the Arcadia Science Pub titled <a href="https://doi.org/10.57844/arcadia-vw5e-2670">"Comparing gene expression across species based on protein structure instead of sequence"</a>.</p>
Timed Sequence Task - Data
<p>Timed Sequence Task - data</p> <p>Raw and generated data from Timed Sequence Task. Data are generated by set of codes for standard operant boxes equipped with two fixed levers and a feeder (Med Associates, MED-307A-B1) and operated by Med-PC V software. </p> <p>Names of the tasks indicate the required sequences of lever presses (eg left - left - right - right). Individual inter-press intervals are specifically defined and are logged in great detail (ie. different types of incorrect presses are distinguished and recorded separately).</p> <p> </p> <p>Operant boxes code is available here: <a href="https://github.com/mjtecka/Timed-Sequence-Task-OB">Timed Sequence Task - OB</a>.</p> <p>Python scripts for the automated analysis of the resulting task logs are available on GitHub: <a href="https://github.com/mjtecka/Timed-Sequence-Task-Analysis">Timed Sequence Task Analysis</a></p> <p> </p> <p>Dataset contains following files</p> <p>raw_data/data_vpa_final.csv</p> <ul> <li>original, complete log of all tasks as exported from Med-PC V Software</li> <li>columns are described in separate file <a href="https://zenodo.org/api/files/3084f324-a303-47a4-8e58-e3506c990d37/Data%20VPA%20final%20-%20columns%20structure.txt">Data VPA final - columns structure.txt</a></li> </ul> <p>raw_data/mastersheet_complete_one_file.csv :</p> <ul> <li>further context for the raw log allowing to split the original log in multiple task logs; format [ mouse - day - task ] ; this can be done using scripts provided here: https://doi.org/10.5281/zenodo.7881058 </li> </ul> <p> </p> <p>generated data/data_by_task/[task-log].csv </p> <ul> <li>data from complete log filtered according to mastersheet into specific [task-log].csv files</li> </ul> <p>generated_data/subanalysis_results/[subanalysis-name]/[task-name].csv</p> <ul> <li>results of specific subanalyses divided by task names into separate files. <ul> <li>e.g. count_all_presses_test/LLPP-count_all_presses_test.csv = subanalysis count all presses for LLPP task</li> </ul> </li> <li>note that csv files in this directory have different structure, according to selected subanalysis</li> </ul> <p> </p> <p> </p>
Data supporting publication: Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes
<p>Genomes, mapping databases, and data supporting the publication "Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes".</p> <p><strong>Please see the README on GitHub for descriptions of files and tutorials on how to use them: <a href="https://github.com/MrOlm/ZenodoREADME/blob/main/README.md">https://github.com/MrOlm/ZenodoREADME/blob/main/README.md</a></strong></p>
Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes
<p>Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles:</p> <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> <p>The spatial reference system is EPSG 25830.</p>
Self-supervised learning of seismological data reveals undocumented eruptive sequences at the Mayotte submarine volcano - Supplementary Materials
<p>The following files are shared:<br> - The scripts used to train the model and generate the figures of the article<br> - The input images used to train the model as well as the final outputs (embedding matrix and the associated filenames matrix)<br> - The clusters organization with their associated images</p>
Reads-per-UMI tables across single-cell RNA sequencing protocols
<p>Data analyzed in <a href="https://www.biorxiv.org/content/10.1101/2023.08.02.551637v1">Lause, Ziegenhain et al. (2023)</a>.</p> <p>Code to obtain these tables from public data sources is available on <a href="https://github.com/berenslab/read-normalization">github</a>.</p> <p> </p> <p>Each row in the table is a UMI-tag detected in a certain cell (column RG) attached to a molecule from a specific gene (column GE) with a certain barcode (column UB). Column N gives the number of times the UMI was detected for that gene and cell.</p> <p>Data sources and protocols are given with the respective file names below.</p> <p><strong>Johnsson2022_Smartseq3_PE.hd1.txt.gz</strong>: Mouse fibroblasts profiled with <strong>Smart-seq3</strong> paired-end; accession E-MTAB-10148, sample plate2,<br> <a href="https://doi.org/10.1038/s41588-022-01014-1">Paper</a><br> <br> <strong>Hagemann-Jensen2020_Smartseq3_SE.hd1.txt.gz: </strong>Mouse fibroblasts profiled with <strong>Smart-seq3</strong> single-end; accession E-MTAB-8735, sample Smartseq3.Fibroblasts.smFISH<br> <a href="https://doi.org/10.1038/s41587-020-0497-0">Paper</a><br> <br> <strong>Hagemann-Jensen2022_Smartseq3xpress.hd1.txt.gz: </strong>HEK293 cells profiled with <strong>Smart-seq3Xpress</strong>; accession E-MTAB-11467.<br> <a href="https://www.biorxiv.org/content/10.1101/2021.07.10.451889v1">Paper</a><br> <br> <strong>Ziegenhain2017.hd1.txt.gz: </strong>Mouse embryonic stem cells profiled by <strong>CEL-seq2, Drop-seq, MARS-seq, </strong>and<strong> SCRB-seq</strong>; GEO accession GSE75790<br> <a href="https://doi.org/10.1016/j.molcel.2017.01.023">Paper</a></p>
NLL-SSST-coherence high-precision earthquake location catalog for the 2023 Ojai, California earthquake sequence
<p><strong>Hypocenter catalog files and visualizations of high-precision, NLL-SSST-coherence earthquake locations for the 2023 M5.1 Ojai, California earthquake sequence and background seismicity (2128 events, 1980-01-01 to 2023-08-25).</strong></p> <p>NLL-SSST-coherence (<a href="https://doi.org/10.1029/2021JB023190">Lomax and Savvaidis, 2022</a>; <a href="https://doi.org/10.26443/seismica.v2i1.324">Lomax and Henry, 2023</a>) is an enhanced, absolute-timing earthquake location procedure which 1) iteratively generates spatially varying travel-time corrections to improve multi-scale location precision and 2) uses waveform similarity to improve fine-scale location precision.</p> <p>Relocations performed with phase arrival data available from <a href="http://service.scedc.caltech.edu">http://service.scedc.caltech.edu</a></p> <p>Visualizations include topography from <a href="https://opentopography.org">https://opentopography.org</a> and surface fault traces from <a href="https://usgs.maps.arcgis.com/apps/webappviewer/index.html?id=5a6038b3a1684561a9b0aadf88412fcf">https://usgs.maps.arcgis.com</a></p> <p> </p> <p>This repository contains:</p> <p><strong>Full catalog in CSV format</strong>:<br> CSV file data columns correspond to selected fields of the of NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Full catalog in NonLinLoc hyp format</strong>:<br> NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Key NLL-SSST-coherence configuration files</strong>: NLL-SSST-coherence_config/*</p> <p><strong>Selected Visualization images</strong></p> <p> </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>
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.