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372 results for “MAG”
Metagenome-assembled genomes from Stordalen Mire, Sweden (MAGs v2)
<p><strong>This release (MAGs v2) is a major new version of this metagenome-assembled genome (MAG) set.</strong> All previous releases on this page (which only differ in the metadata) are designated "MAGs v1." The current release (MAGs v2) uses<strong> </strong>CheckM2 v1.0.2 filtering (≥70% completeness, ≤10% contamination) to expand this dataset to include <strong>36,419 MAGs</strong>, with the following subcategories:</p> <ul> <li>Cronin_v1: Manually-curated subset of the "Field" category from MAGs v1.</li> <li>Cronin_v2: MAGs from raw bin filtering on the same assemblies used to generate Cronin_v1.</li> <li>Woodcroft_v2: MAGs from raw bin filtering on the same assemblies used to generate the MAGs reported in <a href="https://doi.org/10.1038/s41586-018-0338-1">Woodcroft & Singleton et al. (2018)</a>.</li> <li>SIPS: Updated genomes from samples originating from a stable isotope probing (SIP) incubation experiment by Moira Hough et al. ("SIP" in MAGs v1), re-analyzed due to read truncation and sample linkage issues in MAGs v1.</li> <li>JGI: Expanded set of genomes from the Joint Genome Institute's metagenome annotation pipeline.</li> </ul> <p> </p> <p>FILES:</p> <ul> <li><strong>Emerge_MAGs_v2.tar.gz</strong> - Archive containing the MAG files (.fna).</li> <li><strong>metadata_MAGs_v2_EMERGE.tsv</strong> - Table containing source sample names and accessions, GTDB taxonomy information, CheckM2 quality reports, NCBI GenomeBatch- and MIMAG(6.0)-formatted sample attributes and other metadata for the MAGs. </li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>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.</p> <p>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.</p> <p>Data collected at the Joint Genome Institute was generated under the following awards:</p> <ul> <li>The majority of sequencing at JGI was supported by BER Support Science Proposal 503530 (DOI: <a href="https://doi.org/10.46936/10.25585/60001148">10.46936/10.25585/60001148</a>), conducted by the U.S. Department of Energy Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</li> <li>Sequencing of SIP samples was performed under the Facilities Integrating Collaborations for User Science (FICUS) initiative (proposal 503547; award DOI: <a href="https://doi.org/10.46936/fics.proj.2017.49950/60006215">10.46936/fics.proj.2017.49950/60006215</a>) and used resources at the DOE Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>) and the Environmental Molecular Sciences Laboratory (<a href="https://ror.org/04rc0xn13">https://ror.org/04rc0xn13</a>), which are DOE Office of Science User Facilities. Both facilities are sponsored by the Office of Biological and Environmental Research and operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).</li> </ul>
Metagenomics of a pustular microbial mat from Shark Bay, Australia: Raw sequences and assembled MAGs
<p>This data accompanies the paper, "<a href="https://www.nature.com/articles/s43705-022-00128-1">Metagenomic, (bio)chemical, and microscopic analyses reveal the potential for the cycling of sulfated EPS in Shark Bay pustular mats</a>" which looks at the cycling of sulfated polysaccharides in peritidal pustular mats from Shark Bay, Australia. The microbial community was sequenced, assembled, and binned. The raw sequencing reads used in this analysis are the following:</p> <ul> <li>SB_forward_paired_copy.fastq.gz </li> <li>SB_reverse_paired_copy.fastq.gz </li> </ul> <p>The resulting metagenome-assembled genomes (MAGs) are presented in the following folder:</p> <ul> <li>MAGs.zip</li> </ul> <p> </p> <ul> </ul> <p> </p>
Large-scale attributed graph & hypergraph datasets: TWeibo, Amazon2M, Amazon, MAG-PM
<p>Here we provide additional large-scale datasets used in our work "A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation", along with the index files for constructing KNN graphs using ScaNN and Faiss.</p> <p>Usage:</p> <p>cd ANCKA/</p> <p>unzip ~/Download_path/ANCKA_data.zip -d data/</p>
Metagenome-assembled genomes from Stordalen Mire, Sweden (2019) (MAGs from long-read, short-read, & hybrid assemblies)
<p>METHODS:</p> <p>Soil samples (6 total) were collected at the Stordalen Mire site in 2019 from two depths (1-5 & 20-24 cm below ground) across three habitats (Palsa, Bog, and Fen). DNA was extracted based on the protocol described by <a href="http://dx.doi.org/10.17504/protocols.io.yxmvm244bg3p/v1">Li et al. (2024)</a>. For short reads, libraries were prepared at the Joint Genome Institute (JGI) with the KAPA Hyperprep kit, and sequenced with Illumina NovaSeq 6000. For long reads, libraries were prepared with the SMRTbell Express Template Prep Kit 2.0 (PacBio), then sequenced using PacBio Sequel IIe at JGI. PacBio data was processed at JGI to form filtered CCS (Circular Consensus Sequencing) reads. </p> <p>Assemblies were generated with short-only, long-only, and hybrid read sources: <strong>Short-only</strong> was assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">metaSPAdes</a> (v3.15.4) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Long-only</strong> was assembled with <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Hybrid</strong> assembly was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with default parameters. This involved a step-down procedure with long-read assembly through <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768), followed by short-read polishing by <a href="https://genome.cshlp.org/content/27/5/737">Racon</a> (v1.4.3), <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112963">Pilon</a> (v1.24) and then Racon again. Next, reads that didn't map to high-quality metaFlye contigs were hybrid assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">SPAdes (--meta option)</a> and binned out with <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5). For each bin, the reads within the bin were hybrid assembled using <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005595">Unicycler</a> (v0.4.8). The high-coverage metaFlye contigs and Unicycler contigs were then combined to form the assembly fasta file. Genome recovery was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with samples chosen for differential abundance binning by <a href="https://zenodo.org/records/10939393">Bin Chicken</a> (v0.4.2) using <a href="https://zenodo.org/records/7130825">SingleM metapackage S3.0.5</a>. This involved initial read mapping through <a href="https://zenodo.org/records/10531254">CoverM</a> (v0.6.1) using <a href="https://academic.oup.com/bioinformatics/article/34/18/3094/4994778">minimap2</a> (v2.18) and binning by <a href="https://peerj.com/articles/1165/">MetaBAT</a>, <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5), <a href="https://www.nature.com/articles/s41587-020-00777-4">VAMB</a> (v3.0.2), <a href="http://doi.org/10.1038/s41467-022-29843-y">SemiBin</a> (v1.3.1), <a href="https://zenodo.org/records/10460259">Rosella</a> (v0.4.2), <a href="https://www.nature.com/articles/nmeth.3103">CONCOCT</a> (v1.1.0) and <a href="https://academic.oup.com/bioinformatics/article/32/4/605/1744462">MaxBin2</a> (v2.2.7). Genomes were analyzed using <a href="https://www.nature.com/articles/s41592-023-01940-w">CheckM2</a> (v1.0.2) and clustered at 95% ANI using <a href="https://zenodo.org/records/10526086">Galah</a> (v0.4.0).</p> <p> </p> <p>FILES:</p> <ul> <li><strong>EMERGE_MAGs_2019_long-short-hybrid.tar.gz</strong> - Archive containing the MAG files (.fna).</li> <li><strong>metadata_MAGs_2019_EMERGE.tsv</strong> - Table containing source sample names and accessions, GTDB classifications, CheckM2 quality information, NCBI GenomeBatch- and MIMAG(6.0)-formatted attributes, and other metadata for the MAGs.</li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io/">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>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.</p> <p>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.</p> <p>Data from the Joint Genome Institute (JGI) was collected under BER Support Science Proposal 503530 (DOI: <a href="https://doi.org/10.46936/10.25585/60001148">10.46936/10.25585/60001148</a>), conducted by the U.S. Department of Energy Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p>
SubductCR17_MAG-redox_and_CFP_diversity
<p>The code and data connected to the "Biology Meets Subduction" Metagenomic analysis of the 2017 Costa Rica project.</p>
BepiColombo magnetic field data (MPO-MAG) from the two Venus flybys
<p> </p> <p>Vector magnetic field data from the first two Venus flybys (highres and 1-sec res).</p> <p>*Data will be properly archived on ESA's PSA, once the data has been finalized and/or cleaned*</p> <p>Reference frame: VSO</p> <p>Trajectory information is also included. </p>
Metagenomics assemblies and high-quality MAGs for "Long-read metagenomics to retrieve high-quality metagenome-assembled genomes from canine feces"
<p>This dataset includes the different metagenomics assemblies analyzed and its summary (_info.txt file):</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/100_assembly.fasta">100_assembly.fasta</a> is the Flye 2.7 metagenomics assembly merging HMW and non-HMW datasets</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/75_assembly.fasta">75_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 75% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/50_assembly.fasta">50_assembly.fasta</a> is the Flye 2.7 metagenomics assembly including 50% of random data of the merged dataset.</p> <p>- <a href="https://zenodo.org/api/files/3a502803-82f7-4b51-ac62-7c1dfcdcb680/HMW_assembly.fasta?versionId=749ff6fd-2642-4ad1-971a-7f3404baa595">HMW_assembly.fasta</a> is the Flye 2.7 metagenomics assembly for HMW dataset.</p> <p>Moreover, it also includes the eight frameshift-corrected high-quality MAGs analyzed in the manuscript. </p>
Targeted MAG recovery of novel Muirbacteria, Wallbacteria, Riflebacteria and Fusobacteria using SIngleM
<p>Metagenome-assembled genomes (MAGs) from four underrepresented phyla, recovered using targeted analysis of metagenomes included in the <a href="sandpiper.qut.edu.au/">Sandpiper </a>website, analysed using <a href="https://github.com/wwood/singlem">SingleM</a>.</p>
263 MAG annotations for three nested metagenomic studies describe crop-shrub-microbe interactions in an agroecology system in the Sahel
<p>The Sahel region of West Africa is a vulnerable eco-region, where climate change induced drought and a rapidly growing population pose serious threats to food security and contribute to soil degradation. Local and biologically based systems are necessary to maintain crop yields and soil health, and intercropping with native woody shrubs Guiera senegalensis has been discovered as a solution. We have previously shown that soil microbial communities are significantly altered by the presence of shrubs, and that these organisms may have plant growth promoting properties. Here, we augment those data with metagenomic and metatranscriptomic data across three nested experiments: a landscape scale experiment across a rainfall and soil type gradient, a long-term experimental site, and a growth chamber simulated drought experiment. We recovered 263 95% ANI dereplicated metagenome-assembled genomes (MAGs) of medium and high quality to evaluate their relative enrichment and what their encoded metabolisms reveal about mechanisms of microbiome millet support. These data contribute to our understanding of the role of the microbial community crop drought resilience in the Sahel and in semi-arid cropping systems globally. Here we present the DRAM annotations of each MAG, all associated metadata, viral genes and vOTUs from the Optimized Shrub Intercropping Study (OSS), and eukaryotic contigs from the OSS</p>
coauth-MAG-Geology
<h3><strong>Overview</strong></h3><p>This is a temporal higher-order network dataset, which here means a sequence of timestamped hyperedges where each hyperedge is a set of nodes. In this dataset, nodes are authors, and a hyperedge is a publication marked with the "Geology" tag in the Microsoft Academic Graph. Timestamps are the year of publication. The dataset is restricted to hyperedges that contain at most 25 nodes.</p><h4><strong>Statistics</strong></h4><p>Some basic statistics of this dataset are:</p><ul><li>Number of nodes: 1,256,385</li><li>Number of timestamped hyperedges: 1,590,335</li><li>Number of unique hyperedges: 1,207,390</li></ul><h4><strong>Source of original data</strong></h4><p>Source: <a href="https://www.cs.cornell.edu/~arb/data/coauth-MAG-Geology/">coauth-MAG-Geology dataset</a></p><h4><strong>References</strong></h4><p>If you use this data, please cite the following papers:</p><ul><li><a href="https://doi.org/10.1073/pnas.1800683115">Simplicial closure and higher-order link prediction</a>. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.</li><li><a href="https://doi.org/10.1145/2740908.2742839">An overview of Microsoft Academic Service (MAS) and applications</a>. Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June Hsu, and Kuansan Wang. Proceedings of WWW, 2015.</li></ul>
AWI-Gen 2 Microbiome Project MAGs September 2024
<p>Metagenome-assembled genomes, phage genomes, and related statistics for the AWI-Gen 2 Microbiome Project. </p>
75 Coral Endolith Bacterial Genomes (MAGs) from Red Sea corals Goniastrea edwardsi and Porites lutea
<p>The skeleton of reef-building corals harbors diverse microbial communities that can supply energy to the coral host tissues and might have an integral role in holobiont nutrient cycling. However, we are lacking functional insight of the endolithic microbiome at large. In particular, the link between endolithic microbiome diversity and encoded genomic potential to supply energy to the coral host during coral bleaching, in the absence of their Symbiodiniaceae photosymbionts, remains poorly understood. Here we assembled endolith bacterial MAGs from DNA of coral skeletons from Goniastrea edwardsi and Porites lutea, two common reef‐building corals from the central Red Sea.</p>
Merged anvio profiles for Wolbachia MAGs from Culex pipiens midgut and ovary samples
<p>Anvi’o merged profile databases for <em>Wolbachia</em> MAGs from <em>Culex pipiens</em> midgut and ovary metagenomes. </p>
Dataset: Roundhill Magnificent Seven ETF (MAGS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Supplemented SingleM package inclusive of MAGs beyond GTDB
<p>The main SingleM reference package is available at https://zenodo.org/doi/10.5281/zenodo.5739611 </p><p>Here, the default GTDB R214-based SingleM reference database (metapackage) was supplemented with genomes from the 'UHGG' version 2(Almeida et al. 2021), 'SPIRE' (excluding "specI" isolate genomes)(Schmidt et al. 2023), 'SMAG'(Ma et al. 2023), 'GEM'(Nayfach et al. 2021) MAG collections, as well as those from derived from Oceans by Paoli et. al.(Paoli et al. 2022).</p>
TOPC_bin_586 metagenome assembled genome (MAG)
<p><strong>Contig, gene sequences and functional annotation of the <em>TOPC_bin_586</em> metagenome assembled genome (MAG)</strong></p> <p>Data available:</p> <ol> <li>Nucleotide sequences of the contigs composing the MAG [<em>topc.bin.586.fna</em>]</li> <li>Amino acid sequences of the genes (open reading frames, ORFs) [<em>topc.bin.586_ORFs.faa</em>]</li> <li>Functional annotation table (tab-delimited) for the ORFs [<em>topc.bin.586_ORFs_annotation.tsv</em>]</li> </ol>
Text-fig. 1. Historical photo by M. Mag of the discovery of the silicified stem in the locality of Kučlín in 1976 (courtesy Regional Museum Teplice). in Silicified Stem From The Late Eocene Fossil Locality Of Kučlín (Czech Republic): Overview And New Remarks
Text-fig. 1. Historical photo by M. Mag of the discovery of the silicified stem in the locality of Kučlín in 1976 (courtesy Regional Museum Teplice).
Linked collectors and determiners for: MAG Herbarium: collections of vascular plants.
Natural history specimen data linked to collectors and determiners held within, "MAG Herbarium: collections of vascular plants". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/711e7fcd-4b37-471c-8967-1b3a607d2743">https://bionomia.net/dataset/711e7fcd-4b37-471c-8967-1b3a607d2743</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/711e7fcd-4b37-471c-8967-1b3a607d2743">https://gbif.org/dataset/711e7fcd-4b37-471c-8967-1b3a607d2743</a>. Formatted as a Frictionless Data package.
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>
MAGs and gapseq models for auxotrophy predictions in the human gut microbiome
<p>This dataset contains MAGs, their DNA sequence, genome statistics, quantification per sample, and their metabolic model reconstructions from two human population cohorts from northern Germany.</p>
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