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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>
Dominant nitrogen metabolisms of a tropical monomictic lake identified using genome resolved metatranscriptomics
<p><strong>Dataset for manuscript entitled "Dominant nitrogen metabolisms of a tropical monomictic lake identified using genome resolved metatranscriptomics"</strong></p> <p><strong>Abstract: </strong>Reactive nitrogen (N) is one of the principal drivers of primary productivity across aquatic ecosystems. However, N cycling in tropical inland waters is less well understood relative to temperate ecosystems. In particular, N cycling in monomictic tropical lakes may be distinct from that of their temperate counterparts due to the warm (>20 °C) temperatures of their anoxic hypolimnions (deepest portion of a stratified water column). These warm anoxic conditions have the potential to dramatically alter ecosystem biogeochemistry by releasing anaerobic metabolic pathways from temperature limitations that are present in anoxic strata of lake ecosystems at higher latitudes. To explore dominant N transformation pathways under warm anoxic conditions, we combined measurements of geochemistry and water column thermophysical structure with genome resolved metatranscriptomic analyses of the water column microbiome in Lake Yojoa, Honduras. Lake Yojoa is characterized by a pronounced accumulation of ammonium in the hypolimnions during stratification, beginning in May, and an annual mixing event that releases reactive N to the photic zone, in November. We sampled Lake Yojoa both above (1m) and below (16m) the thermocline at three locations, in June 2021, when the water column was stratified, and again at the same depths and locations in January 2022, when the water column was mixed. We identified 335 different lineages and significantly different microbiome membership between seasons and, in June, between depths. Expression of genes associated with N metabolic pathways was highest in June below the thermocline. In particular, we found <em>nrfA</em>, along with other respiratory NO<sub>2</sub><sup>-</sup> reductases, was upregulated relative to other N metabolism genes in the June hypolimnions, suggesting that dissimilatory nitrate reduction to ammonium (DNRA) was partially responsible for the previously observed ammonium accumulation. DNRA is infrequently identified as a dominant N transformation pathway in natural aquatic ecosystems. This work highlights the need to better define N biogeochemistry in the poorly understood warm anoxic hypolimnions of tropical lake ecosystems. Defining the dominant anaerobic pathways when temperature is not limiting is one key to providing a more complete understanding of how rapidly occurring global change is altering tropical inland waters.</p> <p><strong>Files described below: </strong></p> <p><em>Yojoa_572_MAGs_scaffolds.fna.zip </em>scaffolds for 572 dereplicated MAGs </p> <p><em>Yojoa_572_MAGs_annotations.tsv.zip</em> DRAM annotations for 572 dereplicated MAGs </p> <p><em>bipartitionsBranchLabels.nxr_nar_for_tree_aligned.fasta_mode_low.renamed </em>phylogenetic tree for nar/nxr genes</p> <p><em>bipartitionsBranchLabels.amo_pmo_fortree_aligned.fasta_mode_low.renamed </em>phylogenetic tree for amo/pmo genes</p> <p><em>YojoaMasterSheet_final.xls</em> MAG inventory with accession numbers and quality stats</p> <p><em>MetaT_norm.counts.rpk_edger.bins_mean.csv </em>mean geTMM values per bin expressed in metatranscriptomic data</p> <p><em>MetaT_geTMM_norm.counts.rpk_edger_genes.csv</em> geTMM values per gene expressed in metranscriptomic data</p> <p><em>geochem_data_Yojoa.csv</em> geochemical data from each location including NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, total phosphorus (TP), and dissolved organic carbon (DOC)</p> <p><em>profile_data_Yojoa.csv</em> depth profile data at each location including temperature and dissolved oxygen measurements</p>
Metatranscriptomic unigenes catalog of MICROSTORE project
<p><strong>Extracted from Monjot et al., 2023 </strong></p> <p><em>Sequencing data are archived at ENA under accession number PRJEB61527.</em></p> <p>The metatranscriptome derived unigene catalog and the assessment of their expression were obtained as described in Carradec <em>et al.</em> (2018). Paired-ends reads from each metatranscriptomic sample were assembled using velvet (v1.2.07) with a kmer size of 89 as described in Carradec <em>et al.</em> (2018). Isoform detection was performed using oases (v0.2.08). Contigs smaller than 150 bp were removed from further analysis. Contig redundancy was removed using CD-HIT-EST (v4.6.1), with the following parameters: <em>-id 95 -aS 90</em> (95% of nucleic identity over 90% of the length of the smallest sequence). For each cluster of contigs, the longest sequence was kept as reference for the unigene catalog. In order to estimate the expression of each unigene in each sample, cleaned reads were mapped against the reference catalog using the bwa tool (v0.7.15). The following parameters were used: <em>bwa aln -l 30 -O 11 -R 1; bwa sampe -a 20000 -n 1 –N; samtools; rmdup</em>. Low complexity reads were removed. Reads covering at least 80% of read length with at least 95% of identity were retained for further analysis. In the case of several possible best matches, a random one was picked.</p> <p>Proteins were predicted from all unigenes with <em>Transdecoder.LongOrfs</em> followed by <em>TransDecoder.Predict</em> (v5.5.0) using the default parameters. Then, unigenes without predicted protein were used for a second run with a minimum protein length of 70 <em>(-m</em>). Finally, the predicted proteins were tested against the AntiFam database (v7.0) (Eberhardt <em>et al.,</em> 2012) with <em>hmmsearch</em> using the <em>--cut_ga</em> parameter (Eddy, 2011).</p> <p>The KEGG Orthology (KO) identifiers were assigned by KoFamScan (v1.3.0) with the KO’s HMM profiles (2022-01-03 release). For proteins without significant hit, the best hit with an e-value <1e-5 was retained as described in Hu <em>et al.</em>(2018).</p> <p>Taxonomic affiliation was performed on proteins with the MMseqs2 suite (v407b315) (Steinegger & Söding, 2017), against the MetaEuk database (Levy Karin <em>et al.,</em> 2020). Taxonomy was assigned with mmseqs taxonomy and the parameters <em>--tax-lineage 1 --lca-mode 2 --max-seqs 100 -e 0.00001 -s 6 --max-accept 100</em>. The unigene catalog was cleaned of contaminants by excluding proteins and unigenes affiliated to Human, Bacteria, Archaea, Virus and Metazoans. </p> <p> </p> <p><strong>References</strong></p> <p>Carradec, Q., Pelletier, E., Da Silva, C., Alberti, A., Seeleuthner, Y., Blanc-Mathieu, R., et al. (2018) A global ocean atlas of eukaryotic genes. <em>Nat Commun</em> <strong>9</strong>: 373.</p> <p>Eddy, S.R. (2011) Accelerated Profile HMM Searches. <em>PLoS Comput Biol</em> <strong>7</strong>: e1002195.</p> <p>Hu, S.K., Liu, Z., Alexander, H., Campbell, V., Connell, P.E., Dyhrman, S.T., et al. (2018) Shifting metabolic priorities among key protistan taxa within and below the euphotic zone. <em>Environmental Microbiology</em> <strong>20</strong>: 2865–2879.</p> <p>Levy Karin, E., Mirdita, M., and Söding, J. (2020) MetaEuk—sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. <em>Microbiome</em> <strong>8</strong>: 48.</p> <p>Steinegger, M. and Söding, J. (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. <em>Nat Biotechnol</em> <strong>35</strong>: 1026–1028.</p>
Insight into the ecology of vaginal bacteria through integrative analyses of metagenomic and metatranscriptomic data
<p>Datasets and code used to analyze and and to prepare figures for: "Insight into the ecology of vaginal bacteria through integrative analyses of metagenomic and metatranscriptomic data", France et al 2022.</p>
MIntO: a Modular and Scalable Pipeline for Microbiome Metagenomic and Metatranscriptomic Meta-omics Data Integration
<p>To illustrate the use of MIntO, a set of 91 human fecal metagenomes from the Inflammatory Bowel Disease Multi’omics Database was selected (IBDMDB). We selected six participants diagnosed as non-IBD (P6018 (nIBD1), M2072 (nIBD2)); Crohn’s disease (H4006 (CD1) and H4020 (CD2)); and ulcerative colitis (H4019 (UC1) and H4035 (UC2)) that were followed for one year each. </p> <p>Here, we present the results from the <em>genome-based assembly-dependent</em> mode, where we used 91 metagenomic high-quality reads<strong> </strong>to recover 163 high-quality MAGs, which constituted a set of non-redundant genomes.</p>
Metatranscriptomic response of the wheat holobiont to decreasing soil water content
<p><strong>Background</strong></p> <p>This dataset contains processed high throughput metatranscriptomics (RNA) sequencing data related to the scientific article entitled <em>Metatranscriptomic response of the wheat holobiont to decreasing soil water content</em>. </p> <p>The files available in this archive are described below:</p> <p><strong>Contigs abundance:</strong><br> contigs/qc_mapping_stats.tsv<br> Contains read counts through various steps of the pipeline. </p> <p>contigs/merged_contigs_abundance.tsv<br> Number of reads that mapped to each contig for each sample. Rows = contig ID; column = sample ID.</p> <p>contigs/merged_contigs_abundance_cpm.tsv<br> Normalized (edgeR) number of reads (Count Per Million - CPM) that mapped to each contig for each sample. Rows = contig ID; column = sample ID.</p> <p><strong>Gene abundance:</strong><br> genes/merged_gene_abundance.tsv<br> Number of reads that mapped to each gene for each sample. Rows = gene ID; column = sample ID.</p> <p>genes/merged_gene_abundance_cpm.tsv<br> Normalized (edgeR) number of reads (Count Per Million - CPM) that mapped to each gene for each sample. Rows = gene ID; column = sample ID.</p> <p><strong>Beta diversity:</strong><br> Beta diversity tables computed (with microbiomeutils v0.9) on gene abundance and contig abundance and bacteria/archaea contigs abundance are available here:<br> betadiv/bray_curtis_contig_abundance/<br> betadiv/bray_curtis_gene_abundance/<br> betadiv/bray_curtis_contig_bacteriaArchaea/<br> Inside each of these directory is an index.html file allowing to visualize an Emperor interactive 3d vizualisation of beta diversity ordinations.<br> ./3d_bray_curtis_plot/index.html</p> <p><strong>Functional annotations:</strong><br> Gene functional annotations procedures are inspired from the JGI annotation workflow and is described in PMID: 31600863.</p> <p>annotations/annotations.tsv<br> Functional annotations and taxonomic lineages (see below) are merged in a single tabular separated file. <br> Contains the results of <br> DIAMOND BLASTp of each gene amino acid sequence against KEGG genes database.<br> DIAMOND BLASTp of each gene amino acid sequence against NCBI nr database.<br> HMMSCAN of each gene amino acid sequence against PFAM-A database (in domtblout format).<br> HMMSCAN of each gene amino acid sequence against PFAM-A database (in tblout format).<br> RPS-BLAST of each gene amino acid sequence against COG database.</p> <p><br> <strong>Contigs-based taxonomy:</strong></p> <p>consensus/taxonomy.tsv<br> Contains the taxonomy assignment for each contig. Taxonomy assignment was performed with PMID : 31640809. <br> consensus/feature_table_<normalized>_L1 to _L7.txt<br> Contigs abundance tables of each contig for each sample. Taxonomy assignment was performed with PMID : 31640809. </p> <p>consensus/feature_table.tsv<br> Contains the raw reads abundance of each contig across all samples.</p> <p>consensus/feature_table_normalized.tsv<br> Contains the normalized (with edgeR) reads abundance of each bin across all samples.</p> <p> </p>
Preprocessed Metatranscriptomic dataset
<p>Preprocessed Metatranscriptomic dataset</p>
Taxonomic and functional annotations of transcripts and proteins derived from a Metatranscriptomic study of microbial eukaryotes from Lake Pavin
<p>These data were obtain as part of a metatranscriptomic study (Monjot <em>et al.,</em> 2023, 2024). All scripts to obtain these annotations are available at https://github.com/amonjot/SSN_Monjot_2024. The sequencing data (i.e. metatranscriptomic) used to obtain this taxonomic and functional information are archived at ENA under accession number PRJEB61515.</p> <p>This repository also contains various protein sequence similarity networks (Lagoon_output.zip). As these files are very time-consuming to produce, we have provided them to complete all the steps in Monjot <em>et al,</em> 2024. All procedures to produce them are present on the following github repository : https://github.com/amonjot/SSN_Monjot_2024.</p> <p> </p>
Trimmed RNASeq pair for the Galaxy Training Network tutorial - "Metatranscriptomics analysis using microbiome RNASeq data"
<p>Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field. <br> This Galaxy training network tutorial will introduce researchers to the basic concepts and tools from the published ASaiM workflow (Batut et al, <em>GigaScience</em> (2018), 7 (6),<a href="http://dx.doi.org/10.1093/gigascience/giy057"> http://dx.doi.org/10.1093/gigascience/giy057</a>). </p> <p>The dataset is a trimmed version of one of the time points from a cellulose degradation biogas reactor dataset. The dataset has been trimmed to facilitate running the workflows for this tutorial. Any biological interpretation from the results would be incorrect, due to the trimmed version of the dataset.</p>
Metatranscriptome Data Tables from McGivern et al.
<h1>Description of Files</h1> <ul> <li> <p><a href="../api/records/13375972/draft/files/htseq_2304MAGs_100M_97_REVSTRANDED_no0s.txt/content" target="_blank" rel="noopener noreferrer">htseq_2302MAGs_100M_97_REVSTRANDED_no0s.txt</a> : Raw data file output from htseq-count of metatranscriptome counts. </p> </li> <li> <p><a href="../api/records/13375972/draft/files/getmms_ge5_1X_REV.txt/content" target="_blank" rel="noopener noreferrer">getmms_ge5_1X_REV_26samples.txt</a> : metatranscriptome counts converted to geTMM, and fitlered for genes with at least 5 read counts in at least 1 sample.</p> </li> <li> <p><a href="../api/records/13375972/draft/files/metaT_genes.csv/content" target="_blank" rel="noopener noreferrer">metaT_genes.csv</a> : metatranscriptome table of geTMM values across samples of genes</p> </li> <li> <p><a href="../api/records/13375972/draft/files/metaT_MAGs.csv/content" target="_blank" rel="noopener noreferrer">metaT_MAGs.csv</a> : metatranscriptome table of total MAG geTMM across samples</p> </li> <li> <p><a href="../api/records/13375972/draft/files/metaT_functions.csv/content" target="_blank" rel="noopener noreferrer">metaT_functions.csv</a> : metatranscriptome table of total geTMM by annotation ID across samples</p> </li> </ul>
Lake Erie Diel Coassembly (Metatranscriptomic)
<p>A coassembly (final.contigs.fa) containing 39 concatenated and assembled (coassembled) metatranscriptomic libraries that has been made publically available in conjunction with an American Society of Microbiology Resource Announcement (Zepernick et al., 2024; doi: 10.1128/mra.00659-24). The proteins (proteins.faa) and nucelotides (nucleotides.fna) that were generated from called open reading frames are included. </p>
A vast world of viroid-like circular RNAs revealed by mining metatranscriptomes
<p>Viroids and viroid-like agents are unique, minimal RNA replicators that typically encode no proteins and hijack cellular enzymes for their genome replication. As the extent and diversity of viroid-like agents are poorly understood, we developed a computational pipeline to identify viroid-like covalently closed circular (ccc) RNAs and applied it to 5,131 global metatranscriptomes and 1,344 plant transcriptomes. The search resulted in 11,420 viroid-like, ribozyme-containing cccRNAs spanning 4,409 species-level clusters, which is a five-fold increase compared to the previously known set of viroids and viroid-like RNA agents. Within this diverse collection, we identified numerous putative novel viroids, satellite RNAs, retrozymes, and ribozy-like viruses. We also found previously unknown ribozyme combinations and unusual ribozymes within the cccRNAs. Self-cleaving ribozymes were identified in both RNA strands of ambiviruses and some mito-like viruses as well as in capsid-encoding satellite virus-like cccRNAs. The broad presence of viroid-like cccRNAs in diverse transcriptomes and ecosystems implies that their host range is not limited to plants, and matches between viroid-like cccRNAs and CRISPR spacers suggest that some of them might replicate in prokaryotes.</p>
Detection of SARS-CoV-2 variants by genomic analysis of wastewater metatranscriptomic samples (Galaxy Training Material)
<p>The tutorial aims to train how to run workflows to analyze lineages abundances in SAR-CoV-2 wastewater metatranscriptomic samples. (https://training.galaxyproject.org/training-material/)</p>
Stordalen Mire July 2016 Metatranscriptome Data from McGivern et al.
<p>Tabs</p> <p>MetaT = information for the 27 metatranscriptomes</p> <p>metaT_genes = gene level mapping data</p> <p>metaT_MAGs = MAG level mapping data</p> <p> </p> <p>FUNDING:<br> This research is a contribution of the EMERGE Biology Integration Institute ((https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br> This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/fics.proj.2017.49950/60006215 and 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (<a href="https://www.google.com/url?q=https://ror.org/04xm1d337&sa=D&source=docs&ust=1674859614742521&usg=AOvVaw2XgXYw9eI4JIXRMKn3S9Se">https://ror.org/04xm1d337</a>) and the Environmental Molecular Sciences Laboratory (<a href="https://www.google.com/url?q=https://ror.org/04rc0xn13&sa=D&source=docs&ust=1674859614742655&usg=AOvVaw3UXdoHIFmVjc-mXUhDXYQt">https://ror.org/04rc0xn13</a>), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).</p>
Training Data for "Metatranscriptomics analysis using microbiome RNASeq data"
<p>Microbiomes play a critical role in host health, disease, and the environment.. Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field.<br> This tutorial from Galaxy training network will introduce researchers to the basic concepts of metatranscriptomics data analysis. It takes in paired-end datasets of raw shotgun sequences (in FastQ format) as an input and:</p> <ol> <li>preprocess</li> <li>extract and analyze the community structure (taxonomic information)</li> <li>extract and analyze the community functions (functional information)</li> <li>combine taxonomic and functional information to offer insights into taxonomic contribution to a function or functions expressed by a particular taxonomy.</li> </ol> <p>The dataset used in the tutorial comes from a time-serie analysis of a microbial community inside a bioreactor (Kunath et al, ISME, 2018). Only the data for one time point (1st) and a biological replicate (A) is analyzed here, after having been trimmed out the original file for the purpose of saving time and resources.</p>
Spatial metatranscriptomics resolves host-bacteria-fungi interactomes, Source Data
<p>Source Data for a publication: Spatial metatranscriptomics resolves host-bacteria-fungi interactomes. </p> <p>Includes the data sets to generate the results. </p> <p>Contains five different experiment types:</p> <p>- Pst bacterial infiltration experiment<br> - Enrichment experiment with different array types<br> - Comparison between SmT vs. Amp-seq<br> - Outdoor-grown leaf experiments<br> - Sterile leaf experiment</p> <p>For each of the experiments are included (if generated, see the README file):<br> - Gene count matrices<br> - Microbial taxa count matrices<br> - Bright field images<br> - Alignment files (Spot files)<br> - Putative microbial reads and related probe information<br> - Data for enrichment analysis<br> - Fluorescent images and corresponding fluorescent values</p>
Metatranscriptomic analysis uncovers prevalent viral ORFs compatible with mitochondrial translation
Open the record for dataset details and reuse information.
Diamond database for taxonomic annotation of fungal metatranscriptomics
<p>This is a protein fasta dataset for use with <a href="https://github.com/bbuchfink/diamond">diamond</a>. The <strong>fasta.gz</strong> file contains protein sequences for the following:</p> <ul> <li>1,164 genomes downloaded from JGI (<strong>taxonomy_taxids.tsv</strong>)</li> <li>121 genomes that are part of the <a href="https://bitbucket.org/dbeisser/taxmapper/src/master/">taxmapper</a> database (<strong>taxmapper_taxonomy_taxids.tsv</strong>) of which 6 were fungal</li> <li>the <em>Hygrophorus russula </em>MG78<em> </em>genome downloaded from NCBI.</li> </ul> <p>For the <em>H. russula</em> genome, genes were predicted using Augustus (v. 3.2.3) with the laccaria_bicolor model.</p> <p>The final protein database consists of a total of 17,694,143 protein sequences (14,976,193 from JGI, 2,708,401 from taxmapper and 9,549 from <em>H. russula</em>).</p> <p>The fasta file and associated taxonomic information files (nodes.dmp.gz & taxonmap.gz) can be used to build a diamond database compatible with diamond version 0.9.22:</p> <pre><code class="language-bash">zcat fasta.gz | diamond makedb -d diamond --taxonmap taxonmap.gz --taxonnodes nodes.dmp</code></pre> <p> </p>
Metatranscriptomics sample dataset
<p>Metatranscriptomics sample dataset</p>
Metagenomic and metatranscriptomics data for Bathymodiolus mussel and deep-sea sponge associated symbionts deposited in NCBI, IMG and other databases
<p>Metagenomic data for the sulfur- and methane-oxidizing symbionts of <em>Bathymodiolus</em> mussels and different sponge species deposited in the Integrated Microbial Genomes (IMG) database of the DOE Joint Genome Institute (http://img.jgi.doe.gov/) and NCBI until October 2017.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.