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20 results for “Gene catalog”
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
A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog - Supporting Data
<p><strong>Description </strong></p> <p>The following files are available : </p> <ul> <li>Simulated non-redundant Gene Catalog (SGC) composed of 128267 genes;</li> <li>Gene abundance profiles across 40 samples: raw read counts, gene length normalized base counts, depth file computed by the jgi_summarize_bam_contig_depth script provided by MetaBAT;</li> <li>Gold Standard (GS) and Gold Standard Single Assignment (GS_SA) binning results;</li> <li>Binning results obtained on the SGC with nine binning methods: MSPminer, MGS-canopy, DAS Tool, MaxBin2, MetaBAT2, SolidBin, CONCOCT, COCACOLA and MyCC.</li> </ul> <p><strong>License</strong></p> <p>These files are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3
<p>The North Pacific Eukaryotic Gene Catalog consolidates eukaryotic metatranscriptome data from three latitudinal transects of the North Pacific transition zone and one cruise in the subtropical gyre. Metatranscriptomes were gathered from latitudinally-resolved surface samples, and diel-resolved temporal studies, with samples taken in triplicate or duplicate and collected on 0.2-100 μm, 0.2-3 μm, and 3 μm-100 or 200 μm size fractions. These metatranscriptome data were <em>de novo</em> assembled into 175 independent assemblies, totalling 182 million clustered nucleotide contigs. Assemblies were annotated by taxonomy and function. This catalog provides assembled environmental contigs, their translated peptide sequences, and their taxonomic and functional annotations with the aim of facilitating continued discoveries about the molecular ecology of microbial eukaryotes in the North Pacific.<br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.</p> <div> <p>This dataset repository is associated with a codebase and documentation repository:<br><a href="https://github.com/armbrustlab/NPac_euk_gene_catalog" target="_blank" rel="noopener">https://github.com/armbrustlab/NPac_euk_gene_catalog</a><br>Please see this code repository for additional data and project updates<br><br>Translated and processed protein sequences and their annotations are available in this repository: <br><a href="../doi/10.5281/zenodo.10472589">https://zenodo.org/doi/10.5281/zenodo.10472589</a><br><br>99% identity clustered nucleotide sequences and kallisto enumerations are available here:<br><a href="../doi/10.5281/zenodo.10570448">https://zenodo.org/doi/10.5281/zenodo.10570448</a></p> </div> <div> <p>File contents: this repository contains five .tar.gz compressed tarballs with raw de novo Trinity assemblies of poly-A selected metatranscriptomes from the Gradients 1 through 3 cruises, and a plain-text file with the custom spike-in mRNA standards (CustomStandardSequences.txt)</p> </div> <div> <p><strong><br>Gradients1.KOK1606.PA.assemblies.tar.gz</strong><br>- Link to <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/tree/main/projects/G1PA" target="_blank" rel="noopener">G1PA project github page</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/KOK1606" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/KOK1606</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G1PA.process_short_reads.sh" target="_blank" rel="noopener">G1PA.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G1PA.trinity_assemblies.sh" target="_blank" rel="noopener">G1PA.trinity_assemblies.sh</a></p> </div> <div> <p><strong><br>Gradients2.MGL1704.PA.assemblies.tar.gz</strong><br>- Link to <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/tree/main/projects/G2PA" target="_blank" rel="noopener">G2PA project github page</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/MGL1704" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/MGL1704</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G2PA.process_short_reads.sh" target="_blank" rel="noopener">G2PA.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G2PA.trinity_assemblies.sh" target="_blank" rel="noopener">G2PA.trinity_assemblies.sh</a></p> </div> <div> <p><strong><br>Gradients3.KM1906.PA.assemblies.tar.gz</strong><br>- Link go <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/tree/main/projects/G3PA" target="_blank" rel="noopener">G3PA project github page</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/KM1906" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/KM1906</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G3PA_UW.process_short_reads.sh" target="_blank" rel="noopener">G3PA_UW.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G3PA_UW.trinity_assemblies.sh" target="_blank" rel="noopener">G3PA_UW.trinity_assemblies.sh</a></p> </div> <div> <p><strong><br>G3_diel.KM1906.PA.assemblies.tar.gz</strong><br>- Link go <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/tree/main/projects/G3PA" target="_blank" rel="noopener">G3PA project github page</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/KM1906" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/KM1906</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G3PA_diel.process_short_reads.sh" target="_blank" rel="noopener">G3PA_diel.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/G3PA_diel.trinity_assemblies.sh" target="_blank" rel="noopener">G3PA_diel.trinity_assemblies.sh</a></p> </div> <div> <p><strong><br>CustomStandardSequences.txt<br></strong>- Plain-text FASTA file with the spike-in standards used during mRNA extraction and sequencing prep<br>- Link to publication of spike-in standards methods: <a href="https://www.nature.com/articles/s41564-019-0507-5" target="_blank" rel="noopener">https://www.nature.com/articles/s41564-019-0507-5</a></p> </div> <div> <p>The 2015 SCOPE Diel metatranscriptome raw assemblies have been released in a previous Zenodo repository, and are not included again in this deposition. We provide the links to the Diel1 resources here:<br>- Diel1 raw metatranscriptome assembly Zenodo repository: <a href="../records/5009803" target="_blank" rel="noopener">https://zenodo.org/records/5009803</a><br>- Dataset DOI: <a href="https://doi.org/10.5281/zenodo.5009803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5009803</a><br>- Associated publication: <a href="https://www.frontiersin.org/articles/10.3389/fmicb.2021.682651/full" target="_blank" rel="noopener">https://www.frontiersin.org/articles/10.3389/fmicb.2021.682651/full</a><br>- Codebase: <a href="https://github.com/armbrustlab/diel_eukaryotes" target="_blank" rel="noopener">https://github.com/armbrustlab/diel_eukaryotes</a><br>- Simons CMAP cruise page and datasets: <a href="https://simonscmap.com/catalog/cruises/KM1513" target="_blank" rel="noopener">https://simonscmap.com/catalog/cruises/KM1513</a><br>- Short read processing code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/D1PA.process_short_reads.sh" target="_blank" rel="noopener">D1PA.process_short_reads.sh</a><br>- Trinity assembly code: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/D1PA.trinity_assemblies.sh" target="_blank" rel="noopener">D1PA.trinity_assemblies.sh</a></p> </div> <p><br><br></p>
The North Pacific Eukaryotic Gene Catalog: clustered nucleotide metatranscripts and read counts
<p>This data continues with the development of the NPEGC Trinity <em>de novo</em> metatranscriptome assemblies from the protein data repository of <a href="../doi/10.5281/zenodo.10472589">The North Pacific Eukaryotic Gene Catalog</a>. The nucleotide sequences corresponding to the NPEGC cluster representatives are collected together in these repository files:<br><br><em>NPac.G1PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G2PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G3PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G3PA_diel.bf100.id99.nt.fasta.gz</em><br><em>NPac.D1PA.bf100.id99.nt.fasta.gz</em><br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.<br><br>These nucleotide sequences have been sourced from the Zenodo repository for raw assemblies: <a href="../records/7332796">The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3</a></p> <p>Key processing steps are sampled below with links to the detailed code on the main github code repository: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog">https://github.com/armbrustlab/NPac_euk_gene_catalog</a></p> <p><br>Code used to build the kallisto indices and map the short reads against indices with kallisto are online in the code repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/NPEGC.nt_kallisto_counts.sh">NPEGC.nt_kallisto_counts.sh</a><br><br>There are two main steps:<br>1. Generate the kallisto index on the sets of clustered nucleotide metatranscripts<br>2. Map the short reads from environmental samples back to the assembly index</p> <p>As generated above, kallisto generates separate results files for each of the sample files. Even after compression, the total size of the tarballed kallisto output results directories are prohibitively large (>50GB). We use the code in this template R script to join together the 'est_count' estimated count values for the tens of millions of protein sequences in each project metatranscriptome, along with length.</p> <p>The code in this template script was used for each project: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/aggregate_kallisto_counts.R">aggregate_kallisto_counts.R</a><br>The output count files for each project are Gzip-compressed and uploaded to the NPEGC nucleotide data repository here: </p> <p><em>G1PA.raw.est_counts.csv.gz</em><br><em>G2PA.raw.est_counts.csv.gz</em><br><em>G3PA.raw.est_counts.csv.gz</em><br><em>G3PA_diel.raw.est_counts.csv.gz</em><br><em>D1PA.raw.est_counts.csv.gz</em></p>
Dog gut gene catalog. Supplemental data for "Similarity of the dog and human gut microbiomes in gene content and response to diet"
<p>Gene catalogue for the dog gut microbiome including</p> <ol> <li>FASTA file of nucleotide sequences (including padding, see coords file for exact coordinates)</li> <li>FASTA file of amino-acid sequences</li> <li>coords file (gene coordinates)</li> <li>Taxonomic predictions</li> <li>Functional predictions</li> </ol> <p>See the paper "<em>Similarity of the dog and human gut microbiomes in gene content and response to diet</em>" by Coelho et al. in Microbiome for details. We ask that you cite that publication when using this dataset in published literature</p>
eggNOG Mapper annotations of Mouse, Dog and Pig gut gene catalogs
<p><a href="https://github.com/jhcepas/eggnog-mapper">eggNOG-mapper</a> annotations of <a href="https://doi.org/10.1038/nbt.3353">mouse</a>, <a href="https://doi.org/10.1186/s40168-018-0450-3">dog</a> and <a href="https://doi.org/10.1038/nmicrobiol.2016.161">pig</a> gut, and <a href="https://doi.org/10.1038/nbt.2942">IGC</a> gene catalogs.</p>
The North Pacific Eukaryotic Gene Catalog: metatranscriptome assemblies with taxonomy, function and abundance annotations
<p>This data continues with the development of the unprocessed NPEGC Trinity <em>de novo</em> metatranscriptome assemblies, uploaded to this Zenodo repository for raw assemblies: <a href="../records/7332796">The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3</a><br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.</p> <p><br>Excerpts of key processing steps are sampled below with links to the detailed code on the main github code repository: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog">https://github.com/armbrustlab/NPac_euk_gene_catalog</a></p> <p><br>Processing and annotation of protein-level NPEGC metatranscripts is done in 6 primary steps:<br>1. Six-frame translation into protein sequences<br>2. Frame-selection of protein-coding translation frames<br>3. Clustering of protein sequences at 99% sequence identity<br>4. Taxonomic annotation against MarFERReT v1.1 + MARMICRODB v1.0 multi-kingdom marine reference protein sequence library with DIAMOND<br>5. Functional annotation against Pfam 35.0 protein family HMM profiles using HMMER3<br>6. Functional annotation against KOfam HMM profiles (KEGG release 104.0) using KofamScan v1.3.0<br><br><code># Define local NPEGC base directory here:</code><br><code>NPEGC_DIR="/mnt/nfs/projects/armbrust-metat"</code></p> <p><code># Raw assemblies are located in the /assemblies/raw/ directory</code><br><code># for each of the metatranscriptome projects</code><br><code>PROJECT_LIST="D1PA G1PA G2PA G3PA G3PA_diel"</code></p> <p><code># raw Trinity assemblies:</code><br><code>RAW_ASSEMBLY_DIR="${NPEGC_DIR}/${PROJECT}/assemblies/raw"</code><br><br><strong>Translation</strong><br>We began processing the raw metatranscriptome assemblies by six-frame translation from nucleotide transcripts into three forward and three reverse reading frame translations, using the transeq function in the EMBOSS package. We add a cruise and sample prefix to the sequence IDs to ensure unique identification downstream (ex, `>TRINITY_DN2064353_c0_g1_i1_1` to `>G1PA_S09C1_3um_TRINITY_DN2064353_c0_g1_i1_1` for the S09C1_3um sample in the G1PA assemblies). See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.6tr_frame_selection_clustering.sh">NPEGC.6tr_frame_selection_clustering.sh</a> for full code description.<br><br>Example of six-frame translation using transeq<br><code>transeq -auto -sformat pearson -frame 6 -sequence 6tr/${PREFIX}.Trinity.fasta -outseq 6tr/${PREFIX}.Trinity.6tr.fasta</code><br><br><strong>Frame selection</strong><br>We use a custom frame-selection python script <a href="https://github.com/armbrustlab/marferret/blob/main/scripts/python/keep_longest_frame.py">keep_longest_frame.py</a> to determine the longest coding length in each open reading frame and retain this sequence (or multiple sequences if there is a tie) for downstream analyses. See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.6tr_frame_selection_clustering.sh">NPEGC.6tr_frame_selection_clustering.sh</a> for full code description.<br><br><strong>Clustering by sequence identity</strong><br>To reduce sequence redundancy and near-identical sequences, we cluster protein sequences at the 99% sequence identity level and retain the sequence cluster representative in a reduced-size FASTA output file. See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.6tr_frame_selection_clustering.sh">NPEGC.6tr_frame_selection_clustering.sh</a> for full code description of linclust/mmseqs clustering.<br><br>Sample of linclust clustering script: core mmseqs function<br><code>function NPEGC_linclust {</code><br><code># make an index of the fasta file:</code><br><code>$MMSEQS_DIR/mmseqs createdb $FASTA_PATH/$FASTA_FILE NPac.$STUDY.bf100.db</code><br><code># cluster sequences at $MIN_SEQ_ID</code><br><code>$MMSEQS_DIR/mmseqs linclust NPac.${STUDY}.bf100.db NPac.${STUDY}.clusters.db NPac_tmp --min-seq-id ${MIN_SEQ_ID}</code><br><code># retieve cluster representatives:</code><br><code>$MMSEQS_DIR/mmseqs result2repseq NPac.${STUDY}.bf100.db NPac.${STUDY}.clusters.db NPac.${STUDY}.clusters.rep</code><br><code># generate flat FASTA output with cluster reps</code><br><code>$MMSEQS_DIR/mmseqs result2flat NPac.${STUDY}.bf100.db NPac.${STUDY}.bf100.db NPac.${STUDY}.clusters.rep NPac.${STUDY}.bf100.id99.fasta --use-fasta-header</code><br><code>}</code><br><br>Corresponding files uploaded to this repository: Gzip-compressed FASTA files after translation, frame-selection, and clustering at 99% sequence identity (.bf100.id99.aa.fasta.gz)<br><strong> </strong><em> NPac.G1PA.bf100.id99.aa.fasta.gz</em><br><em> NPac.G2PA.bf100.id99.aa.fasta.gz</em><br><em> NPac.G3PA.bf100.id99.aa.fasta.gz</em><br><em> NPac.G3PA_diel.bf100.id99.aa.fasta.gz</em><br><em> NPac.D1PA.bf100.id99.aa.fasta.gz</em><br><br><strong>MarFERReT + MARMICRODB taxonomic annotation with DIAMOND</strong></p> <p>Taxonomy was inferred for the NPEGC metatranscripts with the DIAMOND fast read alignment software against the <a href="../records/10586950">MarFERReT v1.1 + MARMICRODB v1.0 multi-kingdom marine reference protein sequence library (v1.1)</a>, a combined database of the <a href="https://doi.org/10.1038/s41597-023-02842-4">MarFERReT v1.1 marine microbial eukaryote sequence library</a> and <a href="https://doi.org/10.5281/zenodo.3520509">MARMICRODB v1.0 </a>prokaryote-focused marine genome database. See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.diamond_taxonomy.log.sh">NPEGC.diamond_taxonomy.log.sh</a> for full description of DIAMOND annotation.</p> <p>Excerpt of core DIAMOND function:<br><code>function NPEGC_diamond {</code><br><code># FASTA filename for $STUDY</code><br><code>FASTER_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code><br><code># Output filename for LCA results in lca.tab file:</code><br><code>LCA_TAB="NPac.${STUDY}.MarFERReT_v1.1_MMDB.lca.tab"</code><br><code>echo "Beginning ${STUDY}"</code><br><code>singularity exec --no-home --bind ${DATA_DIR} \</code><br><code> "${CONTAINER_DIR}/diamond.sif" diamond blastp \</code><br><code> -c 4 --threads $N_THREADS \</code><br><code> --db $MFT_MMDB_DMND_DB -e $EVALUE --top 10 -f 102 \</code><br><code> --memory-limit 110 \</code><br><code> --query ${FASTER_FASTA} -o ${LCA_TAB} >> "${STUDY}.MarFERReT_v1.1_MMDB.log" 2>&1</code><br><code>}</code><br><br>Corresponding files uploaded to this repository: Gzip-compressed diamond lowest common ancestor predictions with NCBI Taxonomy against a combined MarFERReT + MARMICRODB taxonomic library (*.Pfam35.domtblout.tab.gz)<br><em> NPac.G1PA.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><em> NPac.G2PA.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><em> NPac.G3PA.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><em> NPac.G3PA_diel.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><em> NPac.D1PA.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><br><strong>Pfam 35.0 functional annotation using HMMER3</strong><br>Clustered protein sequences were annotated against the Pfam 35.0 collection of 19,179 protein family Hidden Markov Models (HMMs) using <a href="http://hmmer.org/">HMMER 3.3 </a> with the <a href="https://academic.oup.com/nar/article/49/D1/D412/5943818">Pfam 35.0 protein family database</a>. Pfam annotation code is documented here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.hmmer_function.sh">NPEGC.hmmer_function.sh</a><br><br>Excerpt of core hmmsearch function:<br><br><code>function NPEGC_hmmer {</code><br><code># Define input FASTA</code><br><code>INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code><br><code># hmmsearch call:</code><br><code>hmmsearch --cut_tc --cpu $NCORES --domtblout $ANNOTATION_DIR/${STUDY}.Pfam35.domtblout.tab $HMM_PROFILE ${INPUT_FASTA}</code><br><code># compress output file:</code><br><code>gzip $ANNOTATION_DIR/${STUDY}.Pfam35.domtblout.tab</code><br><code>}</code><br><br>Corresponding files uploaded to this repository: Gzip-compressed hmmsearch domain table files for Pfam35 queries (*.Pfam35.domtblout.tab.gz)<br><em> G1PA.Pfam35.domtblout.tab.gz</em><br><em> G2PA.Pfam35.domtblout.tab.gz</em><br><em> G3PA.Pfam35.domtblout.tab.gz</em><br><em> G3PA_diel.Pfam35.domtblout.tab.gz</em><br><em> D1PA.Pfam35.domtblout.tab.gz</em><br><br></p> <p><strong>KEGG functional annotation using KofamScan v1.3.0</strong></p> <p>Clustered protein sequences were annotated against the KEGG collection (release 104.0) of 20,819 protein family Hidden Markov Models (HMMs) using <a href="https://github.com/takaram/kofam_scan" target="_blank" rel="noopener">KofamScan </a>and KofamKOALA. Kofam annotation code is documented here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.kofamscan_function.sh">NPEGC.kofamscan_function.sh</a></p> <p>Excerpt of core NPEGC_kofam function:</p> <p><code># Core function to perform KofamScan annotation</code><br><code>function NPEGC_kofam {</code><br><code> # Define input FASTA</code><br><code> local INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code></p> <p><code> # KofamScan call</code><br><code> ${KOFAM_DIR}/kofam_scan-1.3.0/exec_annotation -f detail-tsv -E ${EVALUE} -o ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv ${FASTA_DIR}/${INPUT_FASTA}</code></p> <p><code> # Keep best hit (data is already sorted by KofamScan)</code><br><code> sort -uk1,1 ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv > ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.best.kofam.tsv</code></p> <p><code> # Compress output file</code><br><code> gzip ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv</code></p> <p><code> # Compress best.kofam output file</code><br><code> gzip ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.best.kofam.tsv</code><br><code>}</code></p> <p><br><code># filter hits with a score > 30 in R</code></p> <p>Corresponding files uploaded to this repository: Gzip-compressed KofamScan domain table files for Kofam queries (*.best.Kofam.incT30.csv.gz):<em><br> NPac.G1PA.bf100.id99.aa.best.Kofam.incT30.csv.gz</em><em><br> NPac.G2PA.bf100.id99.aa.best.Kofam.incT30.csv.gz</em><br><em> NPac.G3PA.UW.bf100.id99.aa.best.Kofam.incT30.csv.gz</em><br><em> NPac.G3PA.diel.bf100.id99.aa.best.kofam.incT30.csv.gz</em><br><em> NPac.D1PA.diel.bf100.id99.aa.best.kofam.incT30.csv.gz<br><br></em>The full kofamscan tables with score >30 are deposited here: <a title="The North Pacific Eukaryotic Gene Catalog: KOfam protein function annotations" href="../records/13743267" target="_blank" rel="noopener">https://zenodo.org/records/13743267</a></p>
The North Pacific Eukaryotic Gene Catalog: KOfam protein function annotations
<p><strong>KEGG functional annotation using KofamScan v1.3.0</strong></p> <p>These tables are larger alternative versions to the KOfam tables included in the North Pacific Eukaryotic Gene Catalog protein data repository here: <a href="../records/12630398">https://zenodo.org/records/12630398</a><br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.<br><br>Clustered protein sequences were annotated against the KEGG collection (release 104.0) of 20,819 protein family Hidden Markov Models (HMMs) using <a href="https://github.com/takaram/kofam_scan" target="_blank" rel="noopener">KofamScan </a>and KofamKOALA. Kofam annotation code is documented in the project github repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.kofamscan_function.sh">NPEGC.kofamscan_function.sh</a></p> <p>Excerpt of core NPEGC_kofam function:</p> <p><code># Define input FASTA</code><br><code>local INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code></p> <p><code># KofamScan call</code><br><code>${KOFAM_DIR}/kofam_scan-1.3.0/exec_annotation -f detail-tsv -E ${EVALUE} -o ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv ${FASTA_DIR}/${INPUT_FASTA}</code></p> <p>Unprocessed annotation results were filtered with a minimum score of 30 to remove low-scoring matches:<br><br><code>zcat NPac.<em><u>NPacID</u></em>.kofam.tsv.gz | awk -F'\t' '{ gsub(/"/, "", $5); $5 = $5 + 0; if ($5 >= 30) print }' | gzip > NPac.<em><u>NPacID</u></em>.UW.bf100.id99.aa.incT30.tsv.gz</code></p>
Taxonomic and functional annotations of the Integrated non-redundant Gene Catalog 9.9
<p>The Integrated non-redundant Gene Catalog (<strong>IGC</strong>) 9.9 is a database of 9.9 million genes from 1267 individual fecal samples together with the Homo sapiens database (MetaHIT project, grant agreement 201052). This repository contains the taxonomic and functional annotation of the IGC database.</p> <p><strong>full_taxonomy_MetaHIT99.tsv </strong>: Taxonomic assignment of proteins from IGC database with the sequence aligner DIAMOND against the non-redundant NCBI database, with an e-value threshold of 10<sup>-4</sup> </p> <p><strong>KEGG89_IGC_hs99.table</strong> : Functional annotation of proteins from IGC database with KEGG resource with an e-value threshold of 10<sup>-5</sup>, a bit-score threshold of 60 and using the sensitive mode of DIAMOND</p>
A catalog of genes, genomes and species of the dog (Canis lupus familiaris) intestinal microbiota
<p></p><h1>Data sources</h1><br>This dataset was constructed using metagenomic sequencing data from the bioproject PRJEB20308 from Coelho et al. 2018 (129 samples)<br><h1>Metagenomic assembly</h1><br>First, sequencing adapters removal and read trimming was performed with fastp. Reads mapped on the host genome (ROS_Cfam_1.0 GCF_014441545.1) with bowtie2 were removed with samtools. Finally, Metagenomic assembly was performed with metaSPAdes. Contigs of less than 1500 bp were removed.<br><h1>MAGs recovery</h1><br>MAGs were generated with COMEBin (multi-coverage mode) and MAGs quality was assessed with CheckM2. MAGs with completeness < 70% or contamination > 5% or N50 < 5Kb were discarded. Pairwise Average Nucleotide Identity (ANI) was computed for all recovered MAGs with fastANI and dereplication at species level (ANI cutoff = 95%).<br><h1>Non-redundant gene catalog</h1><br>Genes were predicted on all contigs from metagenomic assemblies with Prodigal (parameters : -m -p meta). Genes were pooled and clustered with cd-hit-est (parameters -c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0) by choosing those from the longest contigs as representatives.<br><h1>MSPs recovery</h1><br>Reads were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (1,0M genes quantified in 129 samples). Then, co-abundant genes were binned in 234 Metagenomic Species Pan-genomes (MSPs, i.e. gene clusters that likely belong to the same microbial species) using MSPminer.<br><h1>MAGs and MSPs taxonomic annotation</h1><br>Dereplicated MAGs were annotated with GTDB-Tk based on GTDB r220. Then, MAGs taxonomic annotation was propagated to the corresponding MSPs.<br><h1>Construction of the phylogenetic tree</h1><br>39 universal phylogenetic markers genes were extracted from the dereplicated MAGs with fetchMGs. Then, the markers were separately aligned with MUSCLE. The 40 alignments were merged and trimmed with trimAl (parameters: -automated1). Finally, the phylogenetic tree was computed with FastTreeMP (parameters: -gamma -pseudo -spr -mlacc 3 -slownni).<h1>Mapping rate distribution across public cohorts</h1>We generated mapping rate distribution plots using Meteor2 (default parameters), comparing performance between: PRJEB20308 (cohort used in catalogue assembly) and PRNJNA714112 (independent cohort not used in assembly).<p></p>
An updated catalog of genes and species of the pig gut microbiota
<p></p><h1>Dataset overview</h1><br>We built an updated catalog of 9.3M genes found in the pig gut microbiota.<br>Co-abundant genes were binned in 1523 Metagenomic Species Pan-genomes (MSPs) for which we provide taxonomic labels and a phylogenetic tree. In addition, we reconstituted 7059 Metagenome-Assembled Genomes (MAGs) covering of 760 Metagenomic Species and we extracted 7331 viral genomes from assemblies.<br>Finally, we used Pairwise Comparative Modelling to predict 6140 antibiotic resistance genes.<br><br>This dataset can be used to analyze shotgun sequencing data of the pig gut microbiota.<br><h1>Methods</h1><br><h2>Sequencing data availability</h2><br>Sequencing data from Xiao et al. (PRJEB11755, n=287) and Kim et al. (PRJEB32496, n=36) was downloaded from the European Nucleotide Archive.<br><h2>Sequencing data quality control</h2><br>Illumina adapters removal and read trimming was performed with fastp . Reads mapped on the host genome (GCF_000003025.6) with bowtie2 were removed with samtools.<br><h2>Metagenomic assembly</h2><br>Metagenomic assembly was performed with metaSPAdes. Contigs of less than 1500 bp were removed.<br><h2>MAGs creation</h2><br>Reads of each sample were aligned to their respective assembly with bowtie2 and results were indexed in sorted bam files with samtools. Then, contigs coverage was computed in each sample with jgi_summarize_bam_contig_depths. MAGs were generated with MetaBAT 2 and MaxBin2. Finally, results of both tools were combined with DAS Tool and MAGs quality was assessed with checkM. MAGs with completeness < 70% or contamination > 5% were discarded.<br><h2>Extraction of viral genomes</h2><br>Candidates viral sequences were identified in assemblies with VirFinder. Then, viral genomes quality was assessed with checkV and those low or undetermined quality were discarded.<br><h2>Non-redundant gene catalog</h2><br>Genes were predicted on all contigs with Prodigal (parameters : -m -p meta ). Genes with missing start codon or shorter than 99 bp were discarded.<br>Then, partial and complete genes were separately clustered with cd-hit-est (parameters -c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0 ). The two non-redundant gene sets were merged by considering at first complete genes from the longest contigs (contact us for futher details).<br><h2>MSPs creation</h2><br>Using the Meteor software suite, reads from each sample were mapped against the non redundant catalog to build a raw gene abundance table (9.3 million genes quantified in 323 samples). This table was submitted to MSPminer that reconstituted 1523 clusters of co-abundant genes named Metagenomic-Species Pangenomes (MSPs).<br>Quality control of each MSP was manually performed by visualizing heatmaps representative of the normalized gene abundance profiles.<br><h2>Taxonomic annotation</h2><br>MAGs and MSPs were annotated with GTDB-Tk based on GTDB Release 05-RS95.<br><h2>Construction of the phylogenetic tree</h2><br>39 universal phylogenetic markers genes were extracted from the 1523 MSPs (or the corresponding MAGs if available) with fetchMGs. Then, the markers were separately aligned with MUSCLE. The 40 alignments were merged and trimmed with trimAl (parameters: -automated1). Finally, the phylogenetic tree was computed with FastTreeMP (parameters: -gamma -pseudo -spr -mlacc 3 -slownni).<br><h2>Prediction of antibiotic resistance genes</h2><br>Antibiotic resistance genes were predicted with the Pairwise Comparative Modelling approach (last version available here).<h1>Mapping rate distribution across public cohorts</h1>We generated mapping rate distribution plots using Meteor2 (default parameters), comparing performance between: PRJEB11755 and PRJEB32496 (cohort used in catalogue assembly) and PRJEB60032 and PRJNA494875 (independent cohort not used in assembly).<p></p>
A catalog of genes and species of the human oral microbiota
<p></p><h1>Data sources</h1><br>The oral gene catalog was built using three primary sources:<br><br>Bacterial Genomes from the Human Oral Microbiome Database (HOMD).<br>Fungal Genomes from the NCBI RefSeq database.<br>Metagenomic Sequencing Data from multiple oral microbiome studies.<br>The creation of the oral gene catalog was a multi-step process, combining and refining genes from each source.<br><br><h1>Bacterial Genes</h1><br>A total of 1,505 bacterial genomes were downloaded from HOMD (version 20170215, accessed in December 2017). Genes shorter than 60 nucleotides or containing ambiguous bases were filtered out. Redundancy was removed using CD-HIT-EST (v4.6; parameters: -aS 0.9 -c 0.95 -T 0 -M 0 -t 0 -d 0 -G 0). This process yielded 1,459,394 unique HOMD genes for the catalog.<br><br><h1>Fungal Genes</h1><br>1,017 fungal genomes were downloaded from NCBI RefSeq (May 2017). For the 492 genomes lacking existing annotations, gene calling was performed using Genemark-ES in fungi mode. After initial redundancy removal with CD-HIT-EST (v4.6; parameters: -aS 0.9 -c 0.95 -T 0 -M 0 -t 0 -d 0 -G 0), genes were selected for inclusion only if their corresponding genome was present in at least 20% of the samples in one of the metagenomic cohorts, determined by mapping reads with Bowtie2 (v2.2.3). This led to the selection of 2,440,644 fungal genes.<br><br><h1>Metagenomic Sequencing Data</h1><br>The gene catalog was supplemented with data from 689 oral metagenomes, including newly sequenced samples, from the following studies:<br><br>Human Microbiome Project (HMP): 382 samples (bioproject PRJNA255439).<br>Chinese Cohort: 212 samples (bioproject PRJEB6997).<br>TwinsUK Cohort: 48 newly sequenced samples (bioproject: PRJEB38483).<br>Raw reads were subjected to quality control and trimmed using AlienTrimmer 0.4.0 (parameters: -k 10 -l 45 -m 5 -p 40 -q 20). Human sequences were removed by mapping against the human reference genome (GRCh38.p11) using Bowtie2 2.2.3. Metagenomic assembly was performed using SPAdes 3.9.0 (parameters: "-k 21,33,55 --only-assembler –meta" for Illumina paired-end data, or "--iontorrent -t 24 -m 300 -k 21,33,55 --only-assembler" for Ion Torrent single-end data). Contigs shorter than 500 bp or with coverage less than 2x were discarded. Gene calling was conducted with Prodigal (parameters: -m -p meta). Genes shorter than 60 bp were filtered out, and redundancy was removed with CD-HIT-EST (v4.6; parameters: -aS 0.9 -c 0.95 -T 0 -M 0 -t 0 -d 0 -G 0).<br><br><h1>Final Gene Catalog</h1><br>The final gene catalog was assembled by sequentially adding non-redundant genes from each data source. Genes from HOMD and fungal genomes were combined first using cd-hit-est-2d. Then, non-redundant genes from the HMP, Chinese, and TwinsUK cohorts were sequentially added using cd-hit-est-2d (same parameters as cd-hit-est). A final redundancy removal step was performed. This process resulted in a catalogue of 8.4 million non-redundant genes<br><br><h1>MSPs Recovery</h1><br>The 689 metagenomic samples were aligned against the final gene catalog using the Meteor software suite to produce a gene abundance table. Then, co-abundant genes were binned into 853 Metagenomic Species Pan-genomes (MSPs) using MSPminer.<br><br><h1>MSPs Taxonomic Annotation</h1><br>Taxonomic annotation for the MSPs was performed by aligning all core and accessory genes against representative genomes from the GTDB database (release r214) using blastn (task: megablast, word_size: 16).<br><br>A species-level assignment was given if over 50% of the genes matched a representative genome with a mean nucleotide identity of at least 95% and a mean gene length coverage of at least 90%.<br>The remaining MSPs were assigned to a higher taxonomic level (genus to superkingdom) if more than 50% of their genes shared the same annotation.<h1>Mapping rate distribution across public cohorts</h1>We generated mapping rate distribution plots using Meteor2 (default parameters), comparing performance between: PRJEB6997, PRJNA255439, PRJNA48479 (cohort used in catalogue assembly) and PRJEB24090, PRJEB28422, PRJEB45799 (independent cohort not used in assembly).<p></p>
A catalog of genes and species of the brown rat (Rattus norvegicus) gut microbiota
<p></p><h1>Dataset overview</h1><br>We built a catalog of 5.9M genes found in the brown rat gut microbiota. Co-abundant genes were binned in 1627 Metagenomic Species for which we provide taxonomic labels.<br><br>This dataset can be used to analyze shotgun sequencing data of the brown rat gut microbiota.<h1>Data sources </h1><br>Rat fecal (and milk) samples characterized by shotgun metagenomic sequencing during the Mamiprooffi project. Sequencing data will be submitted soon on the European Nucleotide Archive (Bioproject PRJEB57230)<br>The gene catalog of the Sprague-Dawley rat gut metagenome published by Pan et al.<br><h1>Metagenomic assembly</h1><br>Metagenomic assembly was performed on the Mamiprooffi samples (Data Source 1) with SPAdes (parameters: --iontorrent --careful). Contigs of less than 1500 bp or successfully aligned on the rat genome (Rnor_6.0) were removed.<br>Non-redundant gene catalog<br>Genes were predicted on all contigs with Prodigal (parameters : -m -p meta ). Genes with missing start codon or shorter than 99 bp were discarded.<br>Then, partial and complete genes were separately clustered with cd-hit-est (parameters -c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0 ). Finally, these two non-redundant gene sets were merged with the previously published catalog (Data Source 2) using cd-hit-est-2d by considering at first complete genes (contact us for futher details).<br>Functionnal annotation<br>KEGG Orthologs (KOs) were assigned to genes of the final catalog with KofamScan (version 1.3.0, KEGG 107 database)<br><h1>Metagenomic Species</h1><br>Using the Meteor software suite, reads from samples in Bioprojects PRJEB57230 and PRJEB22973 were mapped against the final non redundant catalog to build a raw gene abundance table (5.9 million genes quantified in 370 samples). This table was submitted to MSPminer and Canopy. A total of 1627 clusters of co-abundant genes or MetaGenomic Species (MGS) were discovered.<br>Quality control of each MGS was manually performed by visualizing heatmaps representative of the normalized gene abundance profiles.<br><h1>Taxonomic annotation of Metagenomic Species</h1><br>MGS taxonomic annotation was performed by aligning all core and accessory genes against the GTDB r214 representative genomes using blastn [4] (version 2.10.1, task = megablast, word_size = 16). The 20 best hits for each gene were kept. A species-level assignment was given if > 50% of the genes matched a GTDB representative genome with a mean identity ≥ 95% and mean gene length coverage ≥ 90%. The remaining MGS were assigned to a higher taxonomic levels (genus to superkingdom) if more than 50% of their genes had the same annotation.<h1>Mapping rate distribution across public cohorts</h1>We generated mapping rate distribution plots using Meteor2 (default parameters), comparing performance between: PRJEB22973 and PRJEB57230 (cohort used in catalogue assembly) and PRNJNA609596 (independent cohort not used in assembly).<p></p>
A catalog of genes and species of the human skin microbiota
<p></p><h1>Dataset overview</h1><br>This dataset provides:<br>a non-redundant high-quality catalog of 2.9 million genes<br>392 Metagenomic Species Pangenomes (MSPs)<br>This dataset can be used to analyze shotgun sequencing data of the human skin microbiota.<br><h1>How to use this dataset</h1><br>Create a gene abundance table by aligning reads from each sample against the catalog. For this purpose, you can use Meteor or NGLess. Then, normalize raw counts by gene length.<br>Taxonomic profiling: the abundance of each species can be estimated as the average abundance of its 100 first core genes. To reduce the false positive rate, only consider that a species is present if at least 10/100 marker genes are detected.<br><h1>Methods</h1><br><h2>Data sources</h2><br>This dataset was built using the following data sources:<br>118 isolate-derived genomes from the HMRGD<br>246 isolate-derived genomes from the Skin Microbial Genome Collection (SMGC)<br>1,407 skin metagenome assemblies from the Skin Microbial Genome Collection (SMGC)<br><h2>Non-redundant gene catalog</h2><br>After filtering out short contigs (<1500 bp), genes were predicted with Prodigal on genomes (mode: single) and metagenome assemblies (mode: meta). Complete genes (partial=00) were pooled and clustered with cd-hit-est (parameters -c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0) by choosing those from the longest contigs as representatives.<br><h2>Functional annotation</h2><br>KOs assignments were obtained with KofamScan using the KEGG 107 database.<br><h2>MSPs recovery</h2><br>Reads from the 1,120 skin metagenomes available in the bioproject PRJNA46333 were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (2.9M genes quantified in 1,120samples). Then, co-abundant genes were binned in 392 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of co-abundant genes that likely belong to the same microbial species) using MSPminer.<br><h2>MSPs taxonomic annotation</h2><br>Taxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the GTDB database (release r214) using blastn (version 2.7.1, task = megablast, word_size = 16). A species-level assignment was given if > 50% of the genes matched the representative genome of a given species, with a mean nucleotide identity ≥ 95% and mean gene length coverage ≥ 90%. The remaining MSPs were assigned to a higher taxonomic level (genus to superkingdom), if more than 50% of their genes had the same annotation.<br><h2>Construction of the phylogenetic tree</h2><br>39 universal phylogenetic markers genes were extracted from the MSPs (or the corresponding genome if available) with fetchMGs. Then, the markers were separately aligned with MUSCLE. The 40 alignments were merged and trimmed with trimAl (parameters: -automated1). Finally, the phylogenetic tree was computed with FastTreeMP (parameters: -gamma -pseudo -spr -mlacc 3 -slownni).<h1>Mapping rate distribution across public cohorts</h1>We generated mapping rate distribution plots using Meteor2 (default parameters), comparing performance between: PRJNA46333 (cohort used in catalogue assembly) and PRJEB80549 (independent cohort not used in assembly).<p></p>
Gene catalogs and KEGG annotation related to root metagenomic samples
<p><span>Reference genomes of root microbes are essential for metagenomic analyses and mechanistic studies of </span><span>crop </span><span>root microbiome</span><span>s</span><span>. Combining high-throughput bacterial cultivation with metagenomic sequencing, we constructed comprehensive bacterial and viral genome collections from the roots of wheat, rice, maize, and Medicago. The crop root bacterial genome collection (CRBC) significantly expands the quantity and phylogenetic diversity of publicly available crop root bacterial genomes, with 6,699 bacterial genomes (</span><span>68.9 % </span><span>from isolates) </span><span>and 1,817 novel species,</span><span> expanding crop root bacterial diversity by </span><span>290.6%</span><span>. The crop root viral genome collection (CRVC) contains 9,736 nonredundant viral genomes, with 1,572 previously unreported genus-level clusters in crop roots. From these data, we identified conserved bacterial </span><span>functions</span><span> enriched in root microbiomes across soils and host species and uncovered previously unexplored bacteria–virus </span><span>connections</span><span> in crop root ecosystems. Together, the CRBC and CRVC serve as valuable resources for investigating microbial mechanisms and applications, supporting sustainable agriculture. </span></p> <p> </p> <p>The gene sets of 14 datasets are available under this accession.</p>
Prokaryotic gene catalog, prokaryotic Metagenome-Assembled Genomes (MAGs) and taxonomic profiling of metagenomic data of NEREA Augmented Observatory
<p>The NEREA_metaG directory is dedicated to the in-depth analysis of NEREA microbial communities using metagenomic sequencing data. </p> <p><strong>Gene catalog:</strong> This directory contains the gene catalog compiled from metagenomic data, which includes: Protein and nucleotide sequence files for genes; Cluster files grouping similar genes; Annotation files mapping genes to KEGG pathways; Normalized gene abundance profiles.</p> <div><strong>MAGs:</strong> Directory for Metagenome-Assembled Genomes (MAGs). It contains comprehensive annotation files for the MAGs, providing insights into gene functions, metabolic pathways, and other genomic features. It also contains the individual MAGs categorized by sample origin. Each MAG is stored in a compressed FASTA format.</div> <p><strong>mOTUs</strong>: Contains files related to microbial taxonomic units identified and quantified using the mOTUs profiler. </p>
eggNOG Mapper annotations of OM-RGC-v2 gene catalog
<p><a href="https://github.com/eggnogdb/eggnog-mapper">eggNOG-mapper</a> (v2.0.1) annotations of <a href="https://www.ocean-microbiome.org/">Ocean</a> (OM-RGC-v2) gene catalog.</p>
A catalog of genes, genomes and species of the cat (Felis catus) intestinal microbiota
<p></p><h1>Data sources</h1><br>This dataset was constructed using two different bioprojects:<br>PRJNA758898 from Ma et al. 2022. 16 samples from 16 animals.<br>PRJEB9357 from Deusch et al. 2015. 88 samples from 30 animals.<br>PRJEB4391 from Deusch et al. 2014. 36 samples from 18 animals.<br>PRJNA944553. 30 samples from 30 animals.<br>PRJNA908260 from Bai et al. 2023. 8 samples from 8 animals.<br>PRJNA923753 from Ho et al. 2023. 1 sample.<br><h1>Metagenomic assembly</h1><br>De novo metagenomic assembly was performed on samples listed above. First, sequencing adapters removal and read trimming was performed with fastp. Reads mapped on the host genome (GCF_018350175.1) with bowtie2 were removed with samtools. Finally, Metagenomic assembly was performed with metaSPAdes. Contigs of less than 1500 bp were removed.<br><h1>MAGs recovery</h1><br>MAGs were generated with COMEBin (multi-coverage mode) and MAGs quality was assessed with CheckM2. MAGs with completeness < 70% or contamination > 5% or N50 < 5Kb were discarded. Pairwise Average Nucleotide Identity (ANI) was computed for all recovered MAGs with fastANI and dereplication at species level (ANI cutoff = 95%).<br><h1>Non-redundant gene catalog</h1><br>Genes were predicted on all contigs from metagenomic assemblies with Prodigal (parameters : -m -p meta). Genes were pooled and clustered with cd-hit-est (parameters -c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0) by choosing those from the longest contigs as representatives.<br><h1>MSPs recovery</h1><br>Samples from multiple cohorts (listed above + PRJNA906124 from Lee et al. 2022) were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (1,3M genes quantified in 212 samples). Then, co-abundant genes were binned in 344 Metagenomic Species Pan-genomes (MSPs, i.e. gene clusters that likely belong to the same microbial species) using MSPminer.<br><h1>MAGs and MSPs taxonomic annotation</h1><br>Dereplicated MAGs were annotated with GTDB-Tk based on GTDB r214. Then, MAGs taxonomic annotation was propagated to the corresponding MSPs.<br><h1>Construction of the phylogenetic tree</h1><br>39 universal phylogenetic markers genes were extracted from the dereplicated MAGs with fetchMGs. Then, the markers were separately aligned with MUSCLE. The 40 alignments were merged and trimmed with trimAl (parameters: -automated1). Finally, the phylogenetic tree was computed with FastTreeMP (parameters: -gamma -pseudo -spr -mlacc 3 -slownni).<h1>Mapping rate distribution across public cohorts</h1>We generated mapping rate distribution plots using Meteor2 (default parameters) for PRJEB4391, PRJEB9357, PRJNA758898, PRJNA906124, PRJNA908260, PRJNA923753 and PRJNA944553 used in catalogue assembly.<p></p>
Generation of a transcriptome catalog for global analysis of gene expression in Ampelomyces quisqualis during mycoparasitization
GEO Series GSE22888. Ampelomyces quisqualis. 11 samples. Type: Expression profiling by array.
Generation of transcriptome catalogs for global analysis of gene expression in non-model organisms
GEO Series GSE14276. Vitis vinifera. 3 samples. Type: Expression profiling by array.
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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.