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Chemical composition, soil water content and 16S rRNA and ITS gene copy numbers of soil aggregates and bulk soil samples
<p>This repository contains all data to reproduce the analyses presented in "Distinct microbial communities are linked to organic matter properties in millimetre-sized soil aggregates", Simon et al 2024, <em>The ISME Journal </em>(DOI: 10.1093/ismejo/wrae156).</p>
PanRes - Collection of antimicrobial resistance genes
<p><strong>PanRes database of antimicrobial resistance genes</strong></p><p>Many different collections of antimicrobial resistance genes (ARGs) have been collected and used for various purposes. In order to develop a workflow for mass screening of public metagenomes, we recently gathered up and filtered in a number of these gene collections to produce PanRes.</p><p>For details, please see the methods section in the following publication:</p><p><strong> "ARGfinder - a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets" (Unpublished, submitted)</strong></p><p>Briefly, the PanRes gene collection is gathered from a combination of other resistance gene collections into one, so each unique sequence has an "pan_" identifier (PanRes_genes). A separate table (PanRes_data) provides an overview of all the genes, their origin database and which genes cluster together in high-identity clusters.<br><br>A number of previously published collections of ARGs were used in the creation of PanRes (See references):</p><p><strong>ResFinder</strong> (downloaded 2023-01-20, (Bortolaia et al. 2020)),<br><strong>ResFinderFG</strong> (version 2.0, (Gschwind et al. 2023))<br><strong>CARD</strong> (version 3.2.5, (Alcock et al. 2023))<br><strong>MegaRes</strong> (version 3.0.0, (Bonin et al. 2023))<br><strong>AMRFinderPlus</strong> (version 3.11/2022-12-19.1, (Feldgarden et al. 2021))<br><strong>ARGANNOT</strong> (V6_July2019, (Gupta et al. 2014))<br><strong>The 'CsabaPal' collection</strong> (Provided by Csaba Pál and Zoltán Farkas in November 2022, Daruka et al. 2023))<br><strong>BacMet</strong> (version 1.1, (Pal et al. 2014))</p>
DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea
<p><strong><em>This version is to stay up to date with the improvements and increase in 16S rRNA gene sequences (SSU) added to the GTDB release 220. Please read this post for the stats on the updates. </em></strong><strong><em>https://gtdb.ecogenomic.org/stats/r220 </em></strong><strong><em>.</em></strong><strong><em> </em></strong></p> <p><strong><em>There has been no change to the RDP-RefSeq reference database please use previous versions.</em></strong></p> <p><strong><em>If anyone has concerns with MAG extracted 16S rRNA gene contamination concerns, then I suggest that they contact the curators of GTDB themselves because it is outside of my role with these resources designed for DADA2 usage only. </em></strong></p> <p><strong><em>Another concern that was raised was the orientation of the DB sequences, to get past this problem please use the tryRC = TRUE argument in the assignTaxonomy command within DADA2, this will search your ASVs in the reverse complement as well. </em></strong></p> <p>The bacterial and archaeal 16S rRNA gene sequence databases were collated from various sources and formatted to use the "assignTaxonomy" command within the DADA2 pipeline. The data was converted to suite DADA2 format by Alishum Ali.</p> <ol> <li>Genome Taxonomy Database (GTDB): The new version of our dada2 formatted GTDB reference sequences now contains 58102 bacteria and 3672 archaea full 16S rRNA gene sequences. If you wonder why there are fewer species with 16S rRNA, that is because some metagenomics-assembled genomes (MAGs) lack the 16S gene and thus cannot be extracted. The database was downloaded from <a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release95/">https://data.ace.uq.edu.au/public/gtdb/data/releases/</a> on 24/10/2024. Please read the release notes and file descriptions. </li> </ol> <p>The formatting to DADA2 was done using simple awk bash scripts. The script takes as input a fasta file and a tab-delimited taxonomy file (slightly edited to remove special characters) and then it outputs a fasta file with all 7 taxonomy ranks separated by ";" as required for DADA2 compatibility. Additionally, we have concatenated the unique sequence GTDB ID to the species entry (but replaced the "." with an " _". We see this as an important QC step to highlight the issues/confidence associated with short-read taxonomy assignment at the finer rank levels.</p> <p>Also, this update includes two other files that you can use with the assignTaxonomy and addSpecies commands in DADA2.</p>
PWAS Hub: exploring gene-based associations of complex diseases with sex dependency - backing data
<p>The contents of the PWAS database is presented on <a title="The PWAS hub" href="https://pwas.huji.ac.il/?ver=2" target="_blank" rel="noopener">pwas.huji.ac.il</a>. The frontend and backend were build on top of a dynamical databse system. Please consult the direct API for PWAS if you wish to query the database directly: <a title="The PWAS API" href="https://pwas.huji.ac.il/API?ver=2" target="_blank" rel="noopener">pwas.huji.ac.il/API</a></p> <p>This is a PostgreSQL dump file that was created using <code>pg_dump</code>, the backup/restore procedure for PostgreSQL. To restore this into PostgreSQL do</p> <p>[a] create a database</p> <p><code>createdb DATABASE</code></p> <p>[b] on the terminal run</p> <p><code>pg_restore -vcC -h HOST -p PORT -d DATABASE < pwas_dump.20220628.psql</code></p> <p>The HOST and PORT are determined by your installation and DATABASE is given by you in step [a] abobe.</p> <p> </p> <p>To access the PWAS tables, look for table names that begin with <code>pwasAPI_</code></p> <p>A possible query to the database may look like this:</p> <p><code>SELECT * FROM "pwasAPI_genediseasestatpwas" WHERE uniprot_id = 'P09914' AND disease = 'C44';</code></p> <p>This query lists the data that associate uniprot id <strong>P09914</strong> (gene symbol IFIT1) and disease ICD-10 <strong>C44</strong> (Other malignant neoplasms of skin)</p>
Arabidopsis thaliana circadian mRNA-seq gene expression processed tables from Romanowski et al., TPJ 2020.
<p>This dataset is an add-on for Romanowski et al., TPJ 2020 (https://doi.org/10.1111/tpj.14776) containing processed files for the circadian RNAseq data in tab delimited txt format.</p> <p><br> Here, you can the raw counts file, the normalized CPM values, and the full JTK result (without recalculated circadian phases, just the original ones). All genes with a read density > 0.05 in at least one timepoint were considered expressed. The read density is calculated as the amount of reads divided by the effective length of a gene (total reads / length). Genes rd file is also included.</p> <p>Some useful notes:<br> 1) Counts were assigned using ASpli and the AtRTDv2 annotation (34,212 genes).<br> 2) After filtering by rd we had a total of 18,503 expressed genes.<br> 3) 13,256 genes passed the QL F-tests.<br> 4) 9,127 genes were rhythmic according to JTK_cycle. </p> <p>For detailed protocols, please see Romanowski et al., TPJ 2020 (https://doi.org/10.1111/tpj.14776)</p> <p>The RNA-seq raw data supporting the conclusions of this article have been deposited in ArrayExpress (Kolesnikov et al., 2015) at EMBL-EBI (www.ebi.ac.uk/arrayexpress), under accession numbers E-MTAB-7933.</p> <p>All relevant custom r scripts are available at https://github.com/aromanowski/Circadian_rhythms_and_alternative_splicing</p>
Relief from nitrogen starvation entails quick unexpected down-regulation of glycolytic/lipid metabolism genes in enological Saccharomyces cerevisiae
<p>Data and code supporting the manuscript "Relief from nitrogen starvation entails quick unexpected down-regulation of glycolytic/lipid metabolism genes in enological Saccharomyces cerevisiae" by Tesnière et al. (2019) PLoS ONE 14(4): e0215870. https://doi.org/10.1371/journal.pone.0215870</p> <p>README.pdf or README.md files contain information about the files in this archive.</p>
DADA2 formatted eHOMD 16S rRNA gene sequences databse
<p>eHOMD Refseq database (V15.22) formated to be used with dada2 <em>i.e.</em>, dada2::assignTaxonomy(seqs, "eHOMD_RefSeq_dada2_V15.22.fasta.gz" ) and dada2::addSpecies(taxa, "eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz", verbose=TRUE)</p> <p>Alternatively, you could use the metabaRpipe R package to directly update the taxonomy of a phyloseq object see: https://github.com/fconstancias/metabaRpipe#2-addingreplacing-taxonomical-table-in-a-phyloseq-object</p> <p>Example below:<br> source("https://raw.githubusercontent.com/fconstancias/metabaRpipe-source/master/Rscripts/functions.R")</p> <p>readRDS("dada2/phyloseq.RDS") %>%<br> phyloseq_dada2_tax(physeq = .,<br> threshold = 60,<br> db ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_V15.22.fasta.gz",<br> db_species ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz",<br> nthreads = 2,<br> full_return = FALSE) -> physeq_eHOMD_tax</p> <p> </p> <p> </p>
Data set for publication: Determination of Virulence-Associated Genes and Antimicrobial Resistance Profiles in Brucella Isolates Recovered from Humans and Animals in Iran Using NGS Technology
<p>This dataset includes information on resistance profiling, as well as antimicrobial resistance (AMR) genes and virulence-related factors that were identified in <em>Brucella</em> isolates recovered from humans and animals in different regions of Iran using classical phenotyping and next-generation sequencing (NGS) technology.</p>
Supplemental data files: Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans
<p>This dataset holds all non-GEO-hosted supplemental data files for manuscript "Beyond the reference: gene expression variation and transcriptional response to RNAi in <em>C. elegans</em>". Please see the linked preprint/publication for full details.</p> <p>The PDF _guide_to_datafiles.pdf gives details on the format and content of each of the included files.</p>
A Curated Gene and Biological System Annotation of Adverse Outcome Pathways Related to Human Health
<p>Adverse Outcome Pathways (AOPs) are multi-scale models of biological mechanisms connecting molecular initiating events to adverse outcomes through measurable key events. AOPs can guide the use and development of new approach methodologies (NAMs) aimed at reducing animal experimentation in chemical safety assessment. Here, we present a comprehensive molecular annotation of AOPs relevant to human health to embed the AOP framework into molecular data interpretation, which supports the development and application of novel AOP-based approaches in biomedical research.</p> <p>Please cite the following publication alongside this Zenodo entry when using the data:</p> <p>Saarimäki, L.A., Fratello, M., Pavel, A. <em>et al.</em> A curated gene and biological system annotation of adverse outcome pathways related to human health. <em>Sci Data</em> <strong>10</strong>, 409 (2023). https://doi.org/10.1038/s41597-023-02321-w</p>
Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.
<p><strong>ABSTRACT </strong></p> <p>It contains the data of drug targets (gene names), uniprot identifiers, secondary linked data sources (e.g., PharmGKB), market drug name, chembl identifier, and pubchem compound identifier obtained from DGIdb.</p> <p><strong>Instructions: </strong></p> <p>Data were cleaned and duplicates were removed. Data were all categorical features.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "DRG_DEPOT" summer 2023 team project. It is used for constructing R2G dataset, which will map drugs to their drug targets (gene -> protein = drug target)</p> <p><strong>Acknowledgements</strong></p> <p>Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, Griffith M, Griffith OL, Wagner AH. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021 Jan 8;49(D1):D1144-D1151. doi: 10.1093/nar/gkaa1084. PMID: 33237278; PMCID: PMC7778926.</p> <p>https://www.dgidb.org/</p> <p><strong>U-BRITE last update date:</strong> 06/09/2023</p>
TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about HNSC, a type of cancer that originates in the squamous cells lining the mucosal surfaces of the head and neck region, including the oral cavity, throat, and larynx. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about KIRC, the most common and aggressive subtype of kidney cancer, originating from the cells lining the tubules of the kidney and characterized by its clear appearance under the microscope. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Glioblastoma Multiforme (GBM) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about GBM, an aggressive and highly malignant brain tumor that arises from glial cells, characterized by rapid growth and infiltrative behavior. The gene expression profile was measured experimentally using the Affymetrix HT Human Genome U133a microarray platform by the Broad Institute of MIT and Harvard University cancer genomic characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about CESC, a type of cancer that affects the cells lining the cervix and can have squamous cell or adenocarcinoma histological subtypes. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
CGGA mRNAseq_325 Gene Expression
<p><strong>Abstract:</strong></p> <p>The Chinese Glioma Datasets (CGGA) are comprehensive and valuable collections of data related to glioma, a type of brain tumor, originating from Chinese patients. The CGGA is a data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples. This dataset's gene expression profile was measured experimentally using Agilent-014850 Whole Human Genome Microarray. The Sample IDs serve as unique identifiers for each sample. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>z-normalization was performed on all the samples </p> <p><strong>Acknowledgments:</strong></p> <p>Zhao, Z., Zhang, KN., Wang, QW., et al. Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients (2021). Genomics, Proteomics & Bioinformatics 19(1):1-12.</p> <p>Fang, S., Liang, J., Qian, T., et al. (2017). Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area. AMERICAN JOURNAL OF NEURORADIOLOGY. 38(10): 1990-1997.</p> <p>Wang, Y., Wang, Y., Fan, X., et al. (2017). Putamen involvement and survival outcomes in patients with insular low-grade gliomas. JOURNAL OF NEUROSURGERY. 126(6): 1788-1794.</p> <p><strong>U-BRITE last update: </strong>07/27/2023</p>
Multifaceted quality assessment of gene repertoire annotation with OMArk
<p>Dataset associated to the OMArk paper.</p><p>Contain eight archives:</p><p>Supplementary_Tables</p><p>The Supplementary Table files referred to in the paper</p><p>OMAmerDB:</p><p>The OMAmer database constructed using the whole dataset of the OMA database (November 2022 Release) and used in the paper. An OMAmer database is necessary to run OMArk.</p><p>Simulation:<br>Proteomes with artificially introduced errors, contaminants or depleted completeness, used to assess OMArk's performance. The archive contains the generated proteomes (Simulated_Data) and their OMArk quality assessments (omark). They also contains the OMAmer results (OMAmerResults) that were used to run OMArk and BUSCO completeness assessments (BUSCO).</p><p>*Note that for storage efficiency, only the non-redundant part of the data (added errors, added contamination, random fraction of proteomes) are stored there. The full modified proteome can be regenerated from these data and the source proteomes.</p><p>Reference Proteomes:</p><p>The UniProt Reference Proteomes (Proteomes) (2021_04) and their proteome quality assesment results according to OMArk. The archive contains the source proteome FASTA (Source folder), OMAmer results for these proteomes (omamer folder) , OMArk results (omark folder), and BUSCO completeness assesments (BUSCO folder). It also contains a subfolder that contains part of the Contamination detection experiment (Contamination folder).</p><p>Ensembl_Metazoa_AssemblyChange.<br><br>Contains Ensembl Metazoa proteomes with version change between version 52 and 54 as well as their quality assesment resuls for both version. The archive contains the source proteomes FASTA (Source folder), a Splice file that group together all proteins coded by the same gene (Splice folder), omamer results for the proteomes (omamer folder) and the omark results (omark folder)</p><p>MissingGenesBLAST<br><br>Contains sequences of HOGs considered as missing in the Human proteome, that was used to look for sequences in the human genome.</p><p>Ensembl_NCBI_Results</p><p>Contains OMArk and BUSCO results for Ensembl and NCBI proteomes. These results were then used to evaluate OMArk biais due to source of proteomes in the OMA database.</p><p>Notebooks<br>Jupyter Notebooks that were used to perform the analysis described in the paper<br><br> </p>
Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013
Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is jus
Inventory of High-resolution phylogenetic profiles of the planktonic microbial communities (via 16S and 18S rRNA gene amplicons) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, 2017 - ongoing
Planktonic microbial communities mediate many vital biogeochemical processes in wetland ecosystems, yet compared to other aquatic ecosystems, like oceans, lakes, rivers, or estuaries, they remain relatively underexplored. Our study site, the Florida Everglades (USA)—a vast iconic wetland consisting of a slow-moving system of shallow rivers connecting freshwater marshes with coastal mangrove forests and seagrass meadows—is a highly threatened model ecosystem for studying salinity and nutrient gradients, as well as the effects of sea level rise and saltwater intrusion. This dataset provides the first high-resolution phylogenetic profiles of planktonic bacterial and eukaryotic microbial communities (using 16S and 18S rRNA gene amplicons) from these environments. The dataset contains 16S and 18S rRNA data from 2017, and contains 16S rRNA data for monthly (2019) and quarterly water samples (2020-ongoing). The 2017 data are published in Laas et al. 2022. A detailed list of sequence data and their accession numbers in GenBank is provided and will be updated as more data are published. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA525456 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA525456) and BioProject PRJNA1018945 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1018945). This data package is associated with the following publication: Laas, P., Ugarelli, K., Travieso, R., Stumpf, S., Gaiser, E. E., Kominoski, J. S., & Stingl, U. (2022). Water column microbial communities vary along salinity gradients in the Florida Coastal Everglades wetlands. Microorganisms, 10(2), 215. https://doi.org/10.3390/microorganisms10020215 Instead of citing this package, which is an inventory, please cite the original GenBank data or journal article, as appropriate. Citation guidance for the journal article is available on the respective publisher's website.
16S rRNA gene sequence accessions from discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (2013-2023, ongoing)
An important component of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project is monitoring spatial and temporal patterns in the biological composition of perennially ice-covered lakes in Antarctica’s McMurdo Dry Valleys. This data package contributes to this core research area by providing a curated table linking 16S rRNA gene sequence accession numbers archived in NCBI to MCM LTER limnological sampling campaigns conducted at specific depths along the water column of Lakes Fryxell, Hoare, Bonney, and Miers. These data enable integration of microbial community data with co-collected biological, chemical, and physical measurements.
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.