nf-core/seqsubmit release v1.0.0

Mon, 07 Sep 2026 00:00:00 +0100

We are pleased to announce the first release of nf-core/seqsubmit, a new Nextflow pipeline that automates the submission of sequencing data — reads, metagenomic assemblies, bined contigs (bins) and metagenome-assembled genomes (MAGs) — to the European Nucleotide Archive (ENA).

The scientific community generates a huge amount of nucleotide sequencing data but only a fraction is uploaded to public repositories, which greatly hinders its usability. Researchers must navigate complex database schemas, digest lengthy documentation, manually organise metadata according to strict specifications, and execute multiple interdependent steps. As a result, submission is either delayed or abandoned altogether, depriving the community of potentially valuable resources. nf-core/seqsubmit was started at the nf-core Hackathon 2025/2026 as a community-driven effort to remove that bottleneck.

For biological sequence data, the International Nucleotide Sequence Database Collaboration (INSDC) — comprising the EMBL-EBI European Nucleotide Archive (ENA), National Center for Biotechnology Information (NCBI) GenBank, and the DNA Data Bank of Japan (DDBJ) — provides a unified, internationally synchronised archiving system designed specifically for nucleotide sequences. Unlike general-purpose repositories such as Zenodo or Figshare, INSDC databases enforce standardised metadata schemas and structured relationships between biological data entities, and integrate deposited data into global sequence search systems, making it far more suitable for discovery and reuse. That structure comes at a cost, however: submission is considerably more complex than uploading a file, and each provider has different interfaces, tools and requirements.

Automating data preparation for ENA submission

The MGnify team works closely with ENA, retrieving data such as raw sequencing reads and assembled metagenomes, and depositing the products generated by our pipelines, including assembled metagenomes, binned contigs and Metagenome-Assembled Genomes (MAGs). Over years of doing this at scale, we built up a set of Python packages to partially automate the process, and shared them with external collaborators. nf-core/seqsubmit consolidates that expertise into a single, fully automated Nextflow pipeline.

nf-core/seqsubmit accepts four data types, calculates any required statistics, compiles the metadata required by ENA and performs the submission automatically. It organises the submission around the Study associated with the original raw reads, linking derived assemblies and MAGs back to that Study through their Sample records.

All submission is performed programmatically using ENA’s Webin-CLI tool making it a scalable approach suitable for high-throughput workflows.

The pipeline currently supports four modes:

reads — registers raw sequencing reads and their EXPERIMENT/RUN metadata (platform, instrument, library strategy, insert size, etc.), returning ERX/ERR accessions that downstream modes use as their source-RUN reference.
metagenomic_assemblies — validates assembly FASTA files (contig IDs and minimum contig count), computes coverage where it isn’t already known, and submits the assembly via assembly_uploader.
mags and bins — submit MAGs and bins, gathering additional mandatory metadata such as coverage depth, rRNA and tRNA genes presence/absence, NCBI taxonomy, genome completeness and contamination, and environmental context (provided by user as input metadata), before submitting via genome_uploader.

For each submitted type of data, the pipeline reports the ENA-assigned accession in a summary table.

Results

Beyond its own test suite, nf-core/seqsubmit has already been used in production by a partner project to submit its data to ENA, resulting in 26,621 bins, 4,756 MAGs, and 801 assemblies deposited.

Future work

A major hurdle users still face is registering Studies and Samples with ENA. This process involves selecting and organising the appropriate metadata, linking Samples to their corresponding sequencing-run files, and manually editing manifest files and spreadsheets. As a result, registration remains time-consuming and error-prone. Our team is actively working with ENA to develop utilities that streamline these steps and simplify the registration process.

As the project grows, we hope to attract collaborators familiar with NCBI and DDBJ submission systems, so that nf-core/seqsubmit can expand beyond ENA to support the full INSDC collaboration.

MGnify Proteins release - 2026_07

Fri, 17 Jul 2026 00:00:00 +0100

We are happy to announce a new release of the MGnify Proteins database. The last release of the database in April 2024 aggregated over 2.4 billion non-redundant protein sequences generated from publicly available metagenomic datasets. This latest release more than doubles that figure, with over 5.7 billion non-redundant protein sequences now available.

The release also includes over 1.6 billion cluster representatives computed by DIAMOND/Linclust¹ at 90% sequence identity (MGnify90). From this set, three further-filtered subsets were generated at 30% sequence identity, which we refer to collectively as “MGnify30”. As with previous releases, all files are available on our FTP server, and this release is supported on the MGnify Proteins Portal. The HMMER web server now also supports searches against the three MGnify30 subsets.

Figure 1: The flow of data from biome-tagged reads, to assemblies analysed by MGnify, to their resulting protein sequences that make up the MGnify Proteins database. The database is accessible in the same ways as before, with the addition of Parquet files now available in the FTP.

Parquet-based release of MGnify Proteins

A significant update with this release is the generation of all MGnify Proteins data in the Apache Parquet file format, as part of a shift in how we plan to distribute MGnify Proteins files going forward. There are numerous advantages to this shift:

Columnar data access: As Parquet is a columnar data format, certain query structures that are commonly used to search MGnify Proteins metadata become significantly faster, as only columns of interest need to be searched.
Native schemas: Parquet stores metadata about each column within a file - including data types - much like a relational database. This explicit structure enables more robust data validation and more efficient queries. For example, querying integer identifiers in a Parquet file is faster than in flat-files without a schema like .tsv, and Parquet’s structure also supports optimisations such as Bloom filters.
Native compression: Parquet is a compressed file format designed for efficient storage, with data types that help store data more efficiently.
Partial reading: Partial reading makes it possible to query remote Parquet data without downloading the entire source file.

The ecosystem for working with Parquet is broad, with support in most major programming languages, and with tools like DuckDB. The Parquet files for the MGnify database are available alongside the usual flat files on our FTP server, and can be queried with DuckDB like this:

SELECT *
FROM 'https://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2026_07/mgy_protein_sequences.parquet'
WHERE protein_id = 46;

protein_id	full_length	sequence
46	true	MAKEDNIEMQGTVLDTLPNTMFRVELENGHVVTAHISGKMRKNYIRILTGDKVTVELTPYDLSKGRIVFRSR

Please see the README for information about the schemas of each Parquet file, and our documentation for examples of how to use them. More in-depth release statistics can be found here.

Expanded biome coverage

This release of MGnify Proteins has expanded biome coverage. Human-related datasets have historically dominated the database, but with the latest release we have continued to broaden representation across all biomes, as shown in Figure 2, resulting in significantly more protein occurrences from environmental, engineered, and non-human host-associated biomes than before.

Figure 2: Biome distribution across MGnify Proteins releases in 2024_04 and 2026_07.

If you have any feedback or questions about the latest release of MGnify Proteins or any other MGnify resource, please get in touch!

Buchfink BJ, Barbé É, Ashkenazy H, Reuter K, Kennedy JA, Drost HG, “Clustering the protein universe of life using DIAMOND DeepClust”, Nature Methods 23, 724-727 (2026). doi:10.1038/s41592-026-03030-z ↩

Charting New Waters - MGnify Genomes Marine Catalogue v2.0 Released

Thu, 08 Aug 2024 00:00:00 +0100

We are excited to announce the release of an updated version of the MGnify Genomes marine catalogue. This latest release (v2.0) of the marine catalogue contains data from 1628 studies, including genomes from major sampling expeditions such as Tara Oceans, Malaspina, GO-Ship, and Geotraces, amongst others.

The inclusion of these datasets resulted in a broad geographical distribution of samples represented in the catalogue, as shown in the image below.

Figure 1: Geographical map of samples from which the genomes in marine catalogue v2.0 were derived. The resolution is such that multiple samples in close proximity may appear as a single data point. Those data points within land-masses are as a result of errors in the metadata associated with the submitted sequence data.

An important advancement in this version of the catalogue is that it is representative of the marine genomes available in the public archives at the time of generation. It includes not only those generated and submitted by MGnify, but also MAGs generated by other groups and submitted to the INSDC, as well as isolate genomes (and MAGs) from marine data as curated by MarDB. All genomes undergo the same filtering for a quality score of QS50 (QS, quality score, defined as completeness - 5 x contamination) resulting in a total of 50,866 genomes (50,634 MAGs and 232 isolates) included in v2.0 of the MGnify Genomes marine catalogue.

This catalogue was generated using v2.3.0 of the MGnify Genomes catalogue pipeline which is available on GitHub and WorkflowHub. Genomes are clustered into 13,223 species-level clusters, with a cluster representative genome defined for each. GTDB-Tk (Genome Taxonomy DataBase Toolkit) is used to assign a taxonomic rank to the clusters, allowing us to also understand the proportion of genomes within the catalogue which can be considered novel with respect to the current release of GTDB. The various statistics for the catalogue are shown in the table below.

Genomes in catalogue	50,866
Isolate genomes in catalogue	232
MAGs in catalogue	50,634
Species-level clusters	13,223
Bacterial species-level clusters	12,133
Archaeal species-level clusters	1,087
Pangenome clusters	7,095
Bacterial clusters novel wrt GTDB	5,260
Archaeal clusters novel wrt GTDB	536
Non-redundant sequences in protein catalogue	52,607,289
Protein sequence clusters at 90% amino acid identity	25,747,277

As with all MGnify Genomes catalogues, the data is available to access and query both through the MGnify website as well as via the MGnify API. The catalogue can be browsed as a list of genomes, with the ability to filter on metadata fields, or a taxonomic tree providing access to individual cluster representative genome records. Within the individual genome records there are comprehensive genome statistics, summaries of annotations, and an interactive genome browser allowing interrogation of the various annotation tracks and their genomic context. All results files can be downloaded via HTTP or FTP from the catalogue directory. There are also two sequence-based search options that can be carried out via the website or API. The first is a COBS (COmpact Bit-sliced Signature index)-based query for searching gene sequences against the catalogues. The second is a kmer-based search using Sourmash to allow querying of whole genomes or sets of genomes against the catalogues.

As always we would welcome any feedback you have on this latest version of the marine catalogue, or indeed any aspect of MGnify and MGnify genomes.

This work was funded by the European Union under the Horizon Europe Programme, Grant No. 101082304 (BlueRemediomics) and Grant No. 862923 (AtlantECO). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the granting authority, the Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.

Anvi’o visualisations for refinement of Eukaryotic MAGs

Tue, 28 May 2024 00:00:00 +0100

In the coming months we will be expanding MGnify Genomes to include Eukaryotic genomes. Take an early look at our plans to enable users to explore Eukaryotic genomes using Anvi’o.

Eukaryotic metagenome-assembled genome (MAG) generation relies on assemblies of metagenomic data, typically performed by metaSPAdes (Nurk et al. (2017)) or MEGAHIT (Li et al. (2015)). These tools are not biased toward specific organisms, however compared to prokaryotes, eukaryotic genomes are large, have more compositional variation and contain more repetitive regions, meaning they are complex to both assemble (with many fragments) and to generate MAGs. For example, during binning we have found that a single eukaryotic genome can be split across two MAG bins. To alleviate some of these challenges, EukCC2 (Saary et al. (2022)) contains a bin-merging functionality that connects bins from the same taxa, using measures of genome completeness and having paired-end reads connecting the two bins to validate merging. Others have used co-assembly and co-binning of multiple samples to overcome this problem (Delmont et al. (2022)), but related samples are not always available, hence MGnify has adopted single-sample approaches. Nevertheless, the challenges posed by eukaryotic MAG generation afford the need for users to inspect the quality of our automatically generated eukaryotic MAGs.

As part of the BlueRemediomics project, we have been investigating how to leverage the Anvi’o (Eren et al. (2021)) interactive interface for visualising genomes and their derived sample composition. Here, we share our prototype of Anvi’o visualisations of MGnify-generated eukaryotic MAGs. The eukaryotic MAGs were generated for the same datasets used in the Marine v2.0 prokaryotic genome catalogue. Here the MAGs were generated for each study in the dataset, on a sample by sample basis. Subsequently, all the MAGs were de-replicated together to remove redundancy, resulting in 12 species-level clusters, 8 of which comprised more than one genome. These 8 species-representative genomes were taxonomically classified into two families of green algae from the order Mamiellales. For these 8 species clusters, we mapped the read sequences pertaining to each sample that produced a genome in the species cluster, onto the species-representative genome and compiled them into Anvi’o profiles.

Figure 1. Hierarchical clustering of sample reads mapped to contig regions in genomes visualised with Anvi’o. The images are generated from Anvi’o profiles compiled from samples which contributed to 8 genomes. Use arrows to scroll to the next image. Access the interactive version for one example genome.

Visual representation of the hierarchical clustering of contigs in a eukaryotic genome by sample composition can allow a user to identify contigs which map uniquely to a subset of samples - indicating potentially contaminated contigs. More information on refinement can be found in the Anvi’o blogs.

The Anvi’o profiles for all 8 marine eukaryotic genomes can be found on the MGnify FTP server. To visualise them you will need to install Docker and run the commands below in the directory with the desired input genome:

docker pull meren/anvio:8
# start container
docker run --rm -it -v `pwd`:`pwd` -w `pwd` -p 8080:8080 meren/anvio:8
# in container
anvi-interactive -p ${GENOME_NAME}_MERGED/PROFILE.db -c ${GENOME_NAME}.db

This Anvi’o feature is still in development, and we welcome any feedback and suggestions on the prototypes via our MGnify helpdesk or @MGnifyDB. We aim to provide such Anvi’o eukaryotic MAG visualisations alongside eukaryotic genome catalogues.

MGnify Genomes mouse gut catalogue v1.0 released

Thu, 07 Mar 2024 00:00:00 +0000

We are thrilled to be presenting the latest MGnify Genomes catalogue comprising 112,951 genomes derived from mouse gut datasets, represented by 2,847 species-level cluster representative genomes.

This catalogue was generated as part of our work with the MRC funded National Mouse Genetics Network (NMGN), and combines three existing mouse gut genome catalogues, as well as genomes generated by MGnify from additional publicly-available datasets. The contributing catalogues are the Mouse Gastrointestinal Bacteria Catalogue (MGBC), the Comprehensive Mouse Microbiota Genome (CMMG) catalogue, and the integrated Mouse Gut Metagenomic Catalog (iMGMC) as published in Beresford-Jones et al. Cell Host & Microbe (2022), Kieser et al. PLoS Comput Biol (2022), and Lesker et al. Cell Rep (2020) respectively.

The total genomes in the catalogue were quality-filtered and clustered using the catalogue generation process as described in Gurbich et al. (2023). An overview of the process is shown in Figure 1 and the resulting taxonomic diversity of the mouse gut catalogue is represented in Figure 2.

Figure 1: Overview of catalogue generation. The mouse gut catalogue was generated from a combination of newly-assembled MAGs derived from publicly-available Whole Genome Shotgun (WGS) datasets, and existing genomes from three external catalogues. The MGnify Genomes pipeline (as described in Gurbich et al. (2023)) was used to produce the final mouse gut catalogue.

As with existing MGnify Genome catalogues, we provide an associated protein catalogue, available from our FTP site, clustered at 100%, 95%, 90% and 50% amino acid identity. MGnify Genomes’ search tools enable users to compare their own data against the catalogue. Gene sequences can be searched against the catalogue using a COBS-based search, whereas whole genomes or sets of genomes can be compared using a sourmash-based search.

Figure 2: Taxonomic diversity of mouse gut catalogue. A phylogenetic tree showing the diversity of the genomes in the mouse gut catalogue with taxonomy assigned using GTDB r214, and coloured at the Phyla level.

Future focus

Within the NMGN, some of the use-cases of this catalogue are to measure the variability in gut microbiomes between different mouse facilities, and to identify taxonomic and functional features that are associated with disease states or conditions of interest. More broadly this catalogue will support translational research between mouse and human: to what extent can the mouse and human microbiota be compared in terms of functional and taxonomic summaries.

Generating this catalogue relied on the availability of data within public repositories, and so was only possible thanks to the many researchers who have submitted their data to public resources such as the European Nucleotide Archive (ENA). We gratefully acknowledge everyone who has submitted their data to these resources, and also the MRC National Mouse Genetics Network for their generous funding. Any feedback on the new catalogue or any other aspect of the MGnify resource would be greatly appreciated and can be submitted through our helpdesk.

MGnify’s Notebook Server with MGnifyR launched

Thu, 07 Apr 2022 00:00:00 +0100

We are excited to announce the launch of MGnify’s Notebook Server. It provides an online, no-installation-needed, Jupyter Lab environment for users to explore programmatic access to MGnify’s datasets using Python or with R using the MGnifyR package.

Watch our video tutorial on YouTube.

The quantity and richness of metagenomics-derived data in MGnify grows every day. The MGnify website is the best place to start exploring and searching the MGnify database, and allows users to download modest query results as CSV tables.

For larger queries, or more complex requirements like fetching metadata from samples across multiple studies, a programmatic access approach is far better.

MGnify database entries for selected biomes

Programmatic access – fetching data from MGnify using a terminal command or code script – uses the MGnify API (Application Programming Interface). The API provides access to every data type in MGnify: Studies, Samples, Analyses, Annotations, MAGs etc: it is what lies behind the MGnify website. Using the API means you can fetch more data than is possible via the website, and can help you write reproducible analysis scripts.

The API can be explored interactively online, using the API Browser. But actually using the API first requires knowledge and/or installation of tools on your computer. This might range from a command line tool like cURL, to learning R and setting up the R Studio application, to setting up a Python environment and installing a suite of packages used for data analysis. Second, the API returns most data in JSON format: this is standard on the web, but less familiar for bioinformaticians used to TSVs and dataframes.

The MGnify Notebook Server and MGnifyR package are designed to bridge these gaps. Users can launch an online R and Python coding environment in their browser, without installing anything. The environment is hosted by EMBL’s Cell Biology and Biophysics Computational Support team, who support computational projects across EMBL. It already includes the main libraries needed for communicating with the MGnify API, analysing data, and making plots. It uses the popular Jupyter Lab software, which means you can code inside Notebooks: interactive code documents.

There are example Notebooks written in both R and Python, so users can pick whichever they’re more familiar with.

For many users, the online environment can serve as an extension of the website: enabling them to call a few queries on the API, join some data together, and create a datafile or plot they can save to their own computer. The MGnify website has deep-links into the Notebook Server that let users jump from looking at a Study on the website, for example, into a pre-configured R Notebook ready to access that Study using the API.

(This feature is available on the MGnify Beta website.)

For more advanced use cases, the Notebooks serve as an introduction to using the API, and guide users on how to set up a local environment to use the API.

MGnifyR

The MGnify Notebook Server also provides access and examples for the MGnifyR package, created by Ben Allen. MGnifyR wraps the MGnify API in R functions that will feel more familiar to R users and translates data from the API’s JSON standard format into dataframes and Phyloseq objects. It also provides features for cross-study analysis workflows, like computing differential abundance.

Cow rumen v1.0 MAG catalogue released

Tue, 14 Dec 2021 00:00:00 +0000

MGnify is delighted to announce the release of our latest MAG catalogue, comprising 2729 species-level cluster representative genomes derived from cow rumen datasets.

This catalogue is based on the Watson-lab rumen-uncultured genomes (RUGs) set published by Stewart et al. Nature Biotech (2019), and was generated as part of a collaboration between MGnify and the Watson-lab funded by BBSRC. The catalogue contains 5588 genomes from both the RUGs set as well as a large African cattle rumen-specific MAG set. To ensure compatibility across our MAG catalogues, the genomes were quality-filtered, dereplicated and clustered using the same parameters as our existing catalogues. The resulting taxonomic diversity can be seen in the image here:

As with our existing MAG catalogues, we have generated an associated protein catalogue which is available from our FTP site clustered at 100%, 95%, 90% and 50% amino acid identity.

We also provide search tools to compare your own data against this new catalogue. To compare a gene sequence against the catalogue we provide a BIGSI-based search, and to compare a whole genome or set of genomes you can use the Sourmash-based search.

We are particularly thrilled to be presenting the first MGnify MAG catalogue to incorporate publically available third-party generated MAGs, as this represents a scalable future for MAG identification. Availability of data within public repositories is a crucial aspect of open science, and this dataset is only available as a resource thanks to those researchers out there who submit their data to public resources such as ENA, and also to those bioinformatics researchers who subsequently submit their assemblies and MAGs to public repositories. We thank them all! Please let us know if you have any feedback on this new catalogue or any other aspect of MGnify using our helpdesk.

Human gut v2.0

Wed, 08 Dec 2021 00:00:00 +0000

Version 2 of the Unified Human Gastrointestinal Genome catalogue released

MGnify are excited to announce the release of version 2 of the Unified Human Gastrointestinal Genome (UHGG) catalogue. This is an updated version of the catalogue published by Almeida et al. Nature Biotech (2021). We have added 5,878 new genomes from two studies (PRJEB37358 and PRJNA544527), representing 129 new species.

Notably, this includes the set of isolate genomes from the most recent human gut culture collection generated by Poyet et al. Nature Medicine (2019). This means that the catalogue now contains a total of 289,232 prokaryotic genomes from the human gut microbiome clustered into 4,744 species representatives.

With the new genome additions, we have replaced 132 species representatives with better quality genomes (either a better quality MAG (metagenome assembled genome) or an isolate genome). To further increase the quality of the catalogue we have implemented the use of GUNC (a tool developed by the Bork group at EMBL) for genome quality filtering. Any singleton species (i.e., represented by only one strain) <90% complete flagged by GUNC as chimeric was excluded from the catalogue. Further details on the species replaced or excluded based on these criteria can be found in the associated README. We have also updated our genome accessions to use a standardised MGYG prefix, as well as a .version suffix to indicate an update to the genome sequence (such as for the removal of host contamination contigs).

As for v1.0 we have also generated pan-genomes and an associated protein catalogue from all the genomes. However, we have replaced the use of Roary with Panaroo, due to its increased stringency for generating pan-genomes from MAGs. Protein-coding sequences were annotated with updated versions of eggNOG and InterPro.

For those who are relying on v1.0 for their ongoing projects do not worry: all the data from the former version is still available on our FTP, and a mapping between the accession formats is provided in genomes-all_metadata.tsv.

Moving forward, we will continue to provide regular updates to our existing catalogues based on the availability and number of new genomes released, and technical advancements in the field. We hope you find these data useful and feel free to provide us with any feedback you have.

Publication Annotations

Wed, 17 Nov 2021 00:00:00 +0000

Automated annotations are now available for publications linked to metagenomic studies on MGnify, powered by Europe PMC.

Annotations on MGnify Publications – powered by Europe PMC

Europe PMC’s annotations use text mining to search through biological and medical research publications, finding mentions of known entities like diseases, chemicals, protein interactions, locations, and environments. Multiple groups provide the text mining algorithms and annotations, with each provider having a different specialism.

The EMERALD (Enriching MEtagenomics Results using Artificial intelligence and Literature Data) project is one of the 10 providers currently supported and focusses on annotating metagenomic terms. These cover the experimental methods such as the sequencing platform used, as well as other useful metadata like the Host environment a sample was taken from.

For example, if a paper mentions: “A sample was prepared for Illumina sequencing”, then EMERALD identifies the term ‘Illumina’ as a potential sequencing platform and stores an annotation pointing to the location of that phrase in the publication. Combined with open-access literature, these allow a new and rapid approach to exploring metagenomic data and research. The power of these annotations is in explicating a summary of the sample data and methods for a publication, beyond any formal keywords and metadata entered by authors into literature databases. In most cases, annotations are linked directly to the relevant section of a paper so that users can jump to the full text and understand the method or samples in more detail.

Within MGnify, these complement the snapshots of metadata derived from ENA and BioSamples provided on the sample pages.

The improved Publications pages of the MGnify website lists these Annotations alongside any related Samples.

At the time of writing (November 2021), 625/869 of the publications linked in MGnify have metagenomic annotations available. An average publication with available annotations has 69 tags and paper extracts, which are visualised as a hierarchical tree in MGnify for easy browsing of the terms and their context.

The extracted terms cover a rich array of metagenomic metadata, and the most common annotation type is generally descriptions of samples.

Jump into this paper about Kombucha to try it out and let us know what you think.

The Earth Microbiome Project

Wed, 17 Apr 2019 00:00:00 +0100

The Earth Microbiome Project is now available in MGnify. The Earth Microbiome Project (EMP)¹ is a wide ranging collaborative effort that attempts to characterise the taxonomic and functional diversity of microbial life on the planet. Founded in 2010, the project includes 96 different studies, comprising approximately 26k individual sequencing runs from a diverse range of biomes.

Recently, we have started collaborating with GBIF, who requested the analysis of the EMP data to increase the number of land-based taxonomic observations. We have now analysed all of the EMP microbial sequencing data and made it available through MGnify. This provides a single point of discovery and ensures that all of the data has been processed consistently with the same analysis pipeline, making it easier to compare EMP datasets to each other and to other studies in the resource. The table below lists the analysis accessions and titles for all EMP studies to help explore this highly valuable project.

1: https://www.nature.com/articles/nature24621

List of EMP studies in MGnify

Download table

Biome	Accession	Study name
	MGYS00003922	A combination of biochar-mineral complexes and compost improves soil bacterial processes, soil quality, and plant properties
	MGYS00003869	A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA
	MGYS00003917	Agricultural intensification and the functional capacity of soil microbes on smallholder African farms - kakamenga
	MGYS00003921	Agricultural intensification and the functional capacity of soil microbes on smallholder African farms -swkenya
	MGYS00003855	Anaerobic ammonium-oxidizing bacteria in marine environments: widespread occurrence but low diversity
	MGYS00003962	Bacterial Community Spatial and Temporal Variation in a North Temperate Bog Lake
	MGYS00003867	Bergen Ocean Acidification Mesocosms
	MGYS00003687	Biodiversity and Functional Patterns of Microbial Assemblages in Postglacial Pond Sediment Profiles
	MGYS00003806	Biogeographical distribution and diversity of microbes in methane hydrate-bearing deep marine sediments on the Pacific Ocean Margin
	MGYS00003809	Bioturbating shrimp alter the structure and diversity of bacterial communities in coastal marine sediments
	MGYS00003666	Bird Egg Shells from Spain
	MGYS00003909	Brazelton LostCity chimney biofilm
	MGYS00003754	Brazilian Antarctic cleanup 16S
	MGYS00003807	Canadian MetaMicroBiome Initiative samples from
	MGYS00003708	Catchment sources of microbes
	MGYS00003780	Catlin Arctic Survey 2010 L3
	MGYS00003769	Chu Changbai mountain soil
	MGYS00003677	Continental-scale variation in seaweed host-associated bacterial communities is a function of host condition, not geography
	MGYS00002673	Convergence of gut microbiomes in myrmecophagous mammals
	MGYS00003750	Defining seasonal marine microbial community dynamics
	MGYS00003759	Distinct microbial communities associated with buried soils in the Siberian tundra.
	MGYS00003810	Diversity of carbonate deposits and basement rocks in continental and marine serpentinite seeps.
	MGYS00003800	Dominguez sleep deprived flies
	MGYS00003786	Environmental metagenomic interrogation of Thar desert microbial communities
	MGYS00003911	Ercolini whole grain feces
	MGYS00003852	Examination of Microbial Communities through a Freshwater/saltwater Transition Zone in Cenotes, Yucatan, Mexico
	MGYS00003785	Exploring links between pH and bacterial community composition in soils from the Craibstone Experimental Farm.
	MGYS00003670	Ezenwa Cape Buffalo
	MGYS00003916	Friedman Alaska peat soils
	MGYS00003681	Garcia bird gut microbiome
	MGYS00003699	Geographic distance and pH drive bacterial distribution in alkaline lake sediments across Tibetan Plateau
	MGYS00003851	Gibbons tongue river 16S
	MGYS00003957	Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample - 3 prime
	MGYS00003864	Great Lake Microbiome
	MGYS00003951	Gut microbiome of hibernating bears
	MGYS00003923	Gut microbiota of phyllostomid bats that span a breadth of diets
	MGYS00003765	Halophilic Communities as a Source for Novel Lignocellulolytic Enzymes
	MGYS00003698	Hawaii Kohala Volcanic Soils
	MGYS00003963	Influence of tillage practices on soil microbial diversity and activity in a long-term corn experimental field under continuous maize production
	MGYS00004557	Intergenerational Lizard Lounges do not Explain Variation in the Gut Microbiomes of Green Iguanas
	MGYS00003812	Jurelivicius Antarctic cleanup
	MGYS00003854	Kilauea geothermal soils and biofilms
	MGYS00003925	Knight skin biogeography comparison
	MGYS00003865	Laboratory Directed Research and Development Biological Carbon Sequestration
	MGYS00003973	Latitudinal surveys of algal-associated microorganisms
	MGYS00003691	Long Term Soil Productivity project
	MGYS00003961	Long-term seasonal development in selected lakes of Northeast Germany
	MGYS00003659	Longitudinal analysis of microbial interaction between humans and the indoor environment
	MGYS00003853	magnificent mongolian microbes
	MGYS00003918	Malaysia Lambir Soils
	MGYS00003919	Malaysia Pasoh Landuse Logged Forest
	MGYS00003868	Marine CDOM accumulation during a coastal Arctic mesocosm experiment: No response to elevated pCO2 levels
	MGYS00003913	Marine mammal skin microbiomes
	MGYS00003793	McGuire Nicaragua coffee soil
	MGYS00001524	Metagenomics reveals sediment microbial community response to Deepwater Horizon oil spill
	MGYS00003862	Microbial communities of the deep unfrozen: Do microbes in taliks increase permafrost carbon vulnerability?
	MGYS00003673	Microbial community of the bulk soil and rhizosphere of rice plants over its lifecycle
	MGYS00003937	Microbial diversity in arctic freshwaters is structured by inoculation of microbes from soils
	MGYS00003705	Microbiome of honey bees from Puerto Rico
	MGYS00003914	Mission Bay Sediment Viromes
	MGYS00002184	Moving pictures of the human microbiome
	MGYS00003861	Myrold Alder Fir
	MGYS00003688	Myrold Oregon transect
	MGYS00003813	NEON: Directions and resources for long-term monitoring in soil microbial ecology
	MGYS00003772	New Zealand Free Air Carbon dioxide Enrichment soil samples
	MGYS00003920	Panama Precip Grad Soil
	MGYS00003706	Peralta starlings
	MGYS00003866	Polluted Polar Coastal Sediments
	MGYS00003814	Prokaryote populations of extant microbialites along a depth gradient in Pavilion Lake, British Columbia, Canada
	MGYS00003783	Protist diversity in a permanently ice-covered Antarctic Lake during the polar night transition
	MGYS00003707	Puerto Rico and Plantanal
	MGYS00003912	Recovery of biological soil crust-like microbial communities in previously submerged soils of Glen canyon
	MGYS00003773	Rees Vulcano island MedSea
	MGYS00003811	Replicating the microbial community and water quality performance of full-scale slow sand filters in laboratory-scale filters.
	MGYS00003863	Rio de Janeiro Coastline
	MGYS00003946	Seyler North Atlantic water column
	MGYS00003748	Song Colorado freshwater fish
	MGYS00003952	Song whitehead bats
	MGYS00003858	Spatial scale drives patterns in soil bacterial diversity.
	MGYS00003860	Spatial variation in arctic soil microbial communities in fire impacted permafrost ecosystems
	MGYS00003684	Spirito monensin cow hindgut
	MGYS00003870	Temple TX native exotic precip study
	MGYS00003798	The ecological dichotomy of ammonia-oxidizing archaea and bacteria in the hyper-arid soils of the Antarctic Dry Valleys (NZTABS)
	MGYS00003871	The gut microbiota distinguishes GI-healthy and -unhealthy captive colobineprimates
	MGYS00003977	The gut microbiota distinguishes GI-healthy and -unhealthy captive colobineprimates - Hale folivorous primates (HiSeq)
	MGYS00003950	The gut microbiota distinguishes GI-healthy and -unhealthy captive colobineprimates - Hale folivorous primates (MiSeq)
	MGYS00003924	The oral and skin microbiomes of captive Komodo Dragons are significantly shared with their habitat.
	MGYS00003808	The role of macrobiota in structuring microbial communities along rocky shores
	MGYS00003859	Understanding Cultivar-Specificity and Soil Determinants of the Cannabis Microbiome
	MGYS00003915	Urban stress is associated with variation in microbial species composition, but not richness, in Manhattan
	MGYS00003856	Viral communities associated with algal/coral interactions
	MGYS00003857	Whole-grain wheat consumption reduces inflammation in a randomized controlled trial on overweight and obese subjects with unhealthy dietary and lifestyle behaviors: role of polyphenols bound to cereal dietary fiber

Unprocessed studies
ERP022167	MGnify QC not passed
ERP023684	MGnify QC not passed
ERP010098	Failed ENA validation/processing
ERP006348	ENA suppressed at the submitter’s request