Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
59
datasets available to search
ShareScore release 0.9.0
Dataset results
59 results for “Metagenomic analysis”
An evaluation of the accuracy and speed of metagenome analysis tools
<p>Metagenome studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial and aquatic ecosystems to human skin and gut. With the advent of high-throughput sequencing platforms, the use of large scale shotgun sequencing approaches is now commonplace. However, a thorough independent benchmark comparing state-of-the-art metagenome analysis tools is lacking. Here, we present a benchmark where the most widely used tools are tested on complex, realistic data sets. Our results clearly show that the most widely used tools are not necessarily the most accurate, that the most accurate tool is not necessarily the most time consuming and that there is a high degree of variability between available tools. These findings are important as the conclusions of any metagenomics study are affected by errors in the predicted community composition and functional capacity.</p>
Data for the publication "Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer"
<p>This dataset encompasses all data needed to reproduce the analyses presented in <a href="https://www.nature.com/articles/s41591-019-0406-6">Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer</a></p> <p>You can also check the <a href="https://github.com/zellerlab/crc_meta">GitHub repository</a></p>
Analysis of pangolin metagenomic datasets reveals significant contamination, raising concerns for pangolin CoV host attribution
<p>Supplementary Figures, Information and Data to accompany:</p> <p>Analysis of pangolin metagenomic datasets reveals significant contamination, raising concerns for pangolin CoV host attribution<br> <em>Adrian Jones, Daoyu Zhang, Yuri Deigin and Steven C. Quay</em></p> <p>https://arxiv.org/abs/2108.08163</p> <p> </p> <p> </p>
Results of a Galaxy metagenomic analysis of bee gut microbiome data from PRJNA977416
<p>This dataset contains the outputs of a metagenomic Galaxy workflow run on the raw data of the project PRJNA977416, including the CSV file of associated metadata and the workflow.ga used for the analysis.</p> <p>Firstly, it has information on taxonomic assignment with :</p> <ul> <li>the reports of all samples for Kraken2, Bracken, and MetaPhlan taxonomic profilers. </li> <li>two tabular files obtained with Taxpasta, which merge samples and standardize taxonomic abundances.</li> <li>for the Bracken standardised abundance, a file with the measures of alpha diversity calculated </li> <li>two HTML files giving access to the Krona diagram for this taxonomic composition.</li> </ul> <p>Secondly, it contains functional informations with :</p> <ul> <li>a tabular file with the relative abundance of all GO terms for all samples</li> <li>a directory detailing pathways and genes families detected.</li> </ul>
Training datasets for 16S Metagenomics analysis with FROGS
<p>This training dataset is from 2 imaginary microbiome samples. Each one is from a paired end 16S amplicon sequencing run and contains 2 fastq files (forwards and reverse.)</p> <p>It is a useful dataset for demonstrating:</p> <ul> <li>16S metagenomics analysis techniques</li> <li>Differences between microbiome samples</li> </ul>
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.)
metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data - use case
<p>Data products returned by <a href="https://github.com/emo-bon/MetaGOflow">metaGOflow</a> (<a href="https://github.com/emo-bon/MetaGOflow/releases/tag/v1.0.0">v1.0.0</a>) and packed as a Research Object (RO) Crate, when performed with:</p> <ul> <li>a <strong>seawater metagenomic sample </strong>(TARA OCEAN, <a href="https://www.ebi.ac.uk/ena/browser/view/ERR599171">ERR599171</a>)</li> <li>a <strong>fish gut </strong>sample (<a href="https://www.ebi.ac.uk/ena/browser/view/ERR4765907">ERR4765907</a>)</li> <li>a<strong> human gut </strong>sample (<a href="https://www.ebi.ac.uk/ena/browser/view/SRR9654976">SRR9654976</a>)</li> </ul> <p>This Zenodo repo accompanies the metaGOflow paper and more about the analysis of this sample can be found there.</p> <p>You can also have a look at some visual components of the workflow at this <a href="https://data.emobon.embrc.eu/MetaGOflow/">GitHub page</a>. </p> <p>The source code of metaGOflow is available through <a href="http://github.com/emo-bon/MetaGOflow">GitHub</a>.</p>
An evaluation of the accuracy and speed of metagenome analysis tools
<p>Metagenome studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial and aquatic ecosystems to human skin and gut. With the advent of high-throughput sequencing platforms, the use of large scale shotgun sequencing approaches is now commonplace. However, a thorough independent benchmark comparing state-of-the-art metagenome analysis tools is lacking. Here, we present a benchmark where the most widely used tools are tested on complex, realistic data sets. Our results clearly show that the most widely used tools are not necessarily the most accurate, that the most accurate tool is not necessarily the most time consuming and that there is a high degree of variability between available tools. These findings are important as the conclusions of any metagenomics study are affected by errors in the predicted community composition and functional capacity.</p>
Flexible metagenome analysis using the MGX framework -- Benchmark data
<p>Synthetic benchmark metagenomes and annotations used to benchmark taxonomic classification approaches</p> <p>in https://doi.org/10.1186/s40168-018-0460-1</p>
UHGG v1 database for inStrain genome resolved metagenomic analysis
<p>A series of files that are useful for profiling metagenomic communities with the program inStrain.</p>
Comprehensive, targeted eukaryotic metagenomics analysis of environmental DNA biodiversity using Oxford Nanopore sequencing
<p><span>Metagenomics has become a prominent technology for studying the functional potential of all organisms in a microbial and eukaryotic community. The study of symbiotic organisms from different classes or kingdoms, including those previously unknown, is possible with simultaneous and equally efficient metagenomic analysis of these species. A variety of targeted primer sets are used for eukaryotic metagenomic biodiversity, including those that are universal for specific families, classes</span><span>,<span> or kingdoms. The most universal for all existing cellular organisms is the presence of ribosomal RNA encoding gene sequences. For eukaryotic sequences, these are 16S and 23s rDNA, </span>and <span>for eukaryotic sequences of nuclear (18S and 28S) and mitochondrial (12S and 16S) ribosomal RNA. Here we present the application of the eukaryotic metagenomics approach to the simultaneous, quantitative</span>,<span> and unbiased identification of most eukaryotic species. To achieve this, we have developed a universal PCR assay that targets the most conservative nuclear regions of the ribosomal gene for all cellular organisms, including plants, algae, fungi, protists, insects</span>,<span> and animals. The amplification product contains polymorphic regions of both ribosomal genes and the intergenic spacer. The size of the PCR products varies by class, kingdom</span>,<span> or domain, ranging from 2 kb for fungi to 7 kb for birds. This assay is also adapted for use with the Oxford Nanopore Rapid Barcoding Library Kit, which enables metagenomic biodiversity analysis. Our approach provides a rapid, sensitive</span>,<span> and equally efficient way to study the composition of eDNA from mixed species in the environment. This protocol reduces the time and cost of metagenomic biodiversity analysis using Oxford Nanopore sequencing. We can efficiently analyze the biodiversity of mixed species present in environmental samples.</span></span></p>
Oxford Nanopore sequencing for comprehensive, targeted eukaryotic metagenomics analysis of environmental DNA biodiversity
<p><span>The study of symbiotic organisms from different classes or kingdoms, including those previously unknown, is possible with simultaneous and equally efficient metagenomic analysis of these species. A variety of targeted primer sets are used for eukaryotic metagenomic biodiversity, including those that are universal for specific families, classes</span><span>,<span> or kingdoms. The most universal for all existing cellular organisms is the presence of ribosomal RNA encoding gene sequences. For eukaryotic sequences, these are 16S and 23s rDNA, </span>and <span>for eukaryotic sequences of nuclear (18S and 28S) and mitochondrial (12S and 16S) ribosomal RNA. Here, we present the application of the eukaryotic metagenomics approach to the simultaneous, quantitative</span>,<span> and unbiased identification of most eukaryotic species. </span></span></p>
Metagenomic analysis of gut microbiome illuminates the mechanisms and evolution of lignocellulose degradation in mangrove herbivorous crabs
<p><strong>Background:</strong></p> <p>Sesarmid crabs dominate mangrove habitat as the major primary consumers, which facilitates the trophic link and nutrient recycling in the ecosystem. Therefore, the adaptations and mechanisms of sesarmid crabs to herbivory is not only crucial to terrestrialization and its evolutionary success, but also to the healthy functioning of mangrove forest ecosystems. Although endogenous cellulases expressions were reported in crab species, it remains unknown if the endogenous enzymes alone can complete the whole lignocellulolytic pathway, or they also depend on the contribution from their intestinal microbiome. We attempt to investigate the role of gut symbiotic microbes of mangrove-feeding sesarmid crabs in plant digestion using a comparative metagenomic approach.</p> <p><strong>Results:</strong></p> <p>Metagenomics analyses on 43 crab gut samples from 23 species of mangrove crabs revealed a wide coverage of 127 CAZy families and nine KOs targeting lignocellulose and their derivatives in all species analyzed, including predominantly carnivorous species, suggesting the crab species gut microbiome have lignocellulolytic capacity regardless of dietary preference. Microbial cellulase, hemicellulase and pectinase genes in herbivorous and detritivorous crabs were differentially more abundant when compared to omnivorous and carnivorous crabs, indicating the importance of gut symbionts in lignocellulose degradation in mangrove crabs and the enrichment of lignocellulolytic microbes in response to diet with higher lignocellulose content. The herbivorous and detritivorous crabs showed highly similar CAZyme composition compared to dissimilarities observed in taxonomic profiles observed in both groups, suggesting a stronger selection force to gut microbiota by its functional capacity than by taxonomy. The gut microbiota in herbivorous sesarmid crabs were also enriched with nitrogen reduction and fixation genes, implying possible roles of the gut microbiota in supplementing nitrogen that is deficient in plant diet.</p> <p><strong>Conclusions:</strong></p> <p>Endosymbiotic cellulolytic microbes play an important role in lignocellulose degradation in most crab species but their abundance is strongly correlated with dietary preference, and they are highly enriched in herbivorous sesarmids, thus enhancing their capacity for digestion of mangrove leaves. Dietary preference is a stronger driver in determining the microbial CAZyme composition and taxonomic profile in mangrove crab microbiome, resulting in functional redundancy of endosymbiotic microbes. Our results showed that crabs implement a mixed mode of digestion utilizing both endogenous and microbial enzymes in lignocellulose degradation, as observed in most of the more advanced herbivorous invertebrate species.</p>
Freshwater viral metagenome assembled genomes (vMAGs) used for vContact2 analysis in publication Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments
<p>This dataset contains all freshwater viruses that were mined from publicly available data in an effort to provide biogeographical context to viral communities identified from the Columbia River. These two files include data from:</p> <p>1) East River, CO (PRJNA579838)</p> <p>2) A previous study from the Columbia River, WA (PRJNA375338)</p> <p>3) Prairie Potholes, ND (PRJNA365086)</p> <p>4) Amazon River (PRJNA237344)</p> <p> </p> <p>Manuscript title Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments</p>
Supp. Info. for Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution
<p>Supplemanty Information for <strong>Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution</strong></p> <p>Files:</p> <p>Supp_Info_1_PRJNA641544_DG14_DG18.xlsx</p> <p>Supp_Info_3_PRJNA606875_SRR11093270_reads_blast_nt_seq5_hsps1_PCT80_E0.05_hsps.txt</p> <p>Supp_Info_4_PRJNA573298_Analysis.xlsx</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.