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opencc-by-4.0Oct 2019View details →

MARMICRODB database for taxonomic classification of (marine) metagenomes

UPDATE (April 2024): Please note that the combined fasta file (MARMICRODB.faa.bz2) associated with version 1.0.0 was mistakenly uploaded from an early prototype of the database. As a result, some taxonomy identifiers appended to the sequence records are mismatched with the names.dmp, nodes.dmp, and the MARMICRODB_catalog.tsv files. Importantly, the taxon IDs in the FM-compressed Kaiju database are correct and valid for use with the names.dmp and nodes.dmp files. Any analysis using the compressed Kaiju database (MARMICRODB.fmi) from version 1.0.0 will remain valid. However, if you have used the combined fasta file (MARMICRODB.faa.bz2) to generate your own Kaiju database and you used the taxdump files from version 1.0.0 I cannot guarantee the validity of those results. It is strongly recommended that you rebuild the combined fasta file from scratch using the FTP links to the NCBI assemblies provided in the MARMICRODB_catalog.tsv file. The combined fasta file has been removed from this version of the MARMICRODB. Disclaimer: MARMICRODB is optimized for metagenomic samples from the marine environment, in particular, planktonic microbes from the pelagic euphotic zone. We expect this database may also be useful for classifying other types of marine metagenomic samples (for example, mesopelagic, bathypelagic, or even benthic or marine host-associated), but it has not been tested for this purpose. The original purpose of this database was to quantify clades/ecotypes of Prochlorococcus, Synechococcus, SAR11/Pelagibacterales, SAR86, and SAR116 in metagenomes from Tara Oceans Expedition and the GEOTRACES project. We carefully annotated and quality controlled genomes from these five groups, but the processing of the other marine taxa was automated and unsupervised. Taxonomy for other groups was copied over from the Genome Taxonomy Database version RS83 (GTDB) [19,39] and NCBI Taxonomy [23] so any inconsistencies in those databases at the time of access will be propagated to MARMICRODB. If the user’s goal is to focus on a particular organism/clade that we did not curate in the database then the user should spend some time curating those genomes (ie checking for contamination, dereplicating, building a genome phylogeny for custom taxonomy node assignment). Currently the custom taxonomy is hardcoded in the MARMICRODB.fmi index, but if users wish to modify MARMICRODB by adding or removing genomes, or reconfiguring taxonomic ranks the names.dmp and nodes.dmp files can easily be modified. However, the Kaiju index will need to be rebuilt, which will require a high-performance compute cluster. Introduction: This sequence database (MARMICRODB) was introduced in the publication JW Becker, SL Hogle, K Rosendo, and SW Chisholm. 2019. Co-culture and biogeography of Prochlorococcus and SAR11. ISME J. doi:10.1038/s41396-019-0365-4. Please see the original publication and its associated supplementary material for the original description of this resource.  Motivation: We needed a reference database to annotate shotgun metagenomes from the Tara Oceans project [1] the <a href="https://www.geotraces.org/">GEOTRACES</a> cruises GA02, GA03, GA10, and GP13 and the HOT and BATS time series [2]. Our interests are primarily in quantifying and annotating the free-living, oligotrophic bacterial groups Prochlorococcus, Pelagibacterales/SAR11, SAR116, and SAR86 from these samples using the protein classifier tool Kaiju [3]. Kaiju’s sensitivity and classification accuracy depends on the composition of the reference database, and the highest sensitivity is achieved when the reference database contains a comprehensive representation of expected taxa from an environment/sample of interest. However, the speed of the algorithm decreases as the database size increases. Therefore, we aimed to create a reference database that maximized the representation of sequences from marine bacteria, archaea, and microbial eukaryotes while minimizing (but not excluding) the sequences from clinical, industrial, and terrestrial host-associated samples. Results/Description: MARMICRODB consists of 56 million sequence non-redundant protein sequences from 18769 bacterial/archaeal/eukaryote genome and transcriptome bins and 7492 viral genomes optimized for use with the protein homology classifier Kaiju [3]. To ensure maximum representation of marine bacteria, archaea, and microbial eukaryotes, we included translated genes/transcripts from 5397 representative “specI” species clusters from the proGenomes database [4]; 113 transcriptomes from the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) [5]; 10509 metagenome assembled genomes from the Tara Oceans expedition [6,7], the Red Sea [8], the Baltic Sea [9], and other aquatic and terrestrial sources [10]; 994 isolate genomes from the Genomic Encyclopedia of Bacteria and Archaea [11]; 7492 viral genomes from NCBI RefSeq [12]; 786 bacterial and archaeal genomes from MarRef [13]; and 677 marine single cell genomes [14]. In order to annotate metagenomic reads at the clade/ecotype level (subspecies) for the focal taxa Prochlorococcus, Synechococcus, SAR11/Pelagibacterales, SAR86, and SAR116, we generated custom MARMICRODB taxonomies based on curated genome phylogenies for each group. The curated phylogenies, Kaiju formatted Burrows-Wheeler index, translated genes, the custom taxonomy hierarchy, an <a href="https://htmlpreview.github.io/?https://github.com/slhogle/MARMICRODB/blob/master/MARMICRODB_kronaplot.html">interactive kronaplot of the taxonomic composition</a>, and scripts and instructions for how to use or rebuild the resource is available from <a href="https://doi.org/10.5281/zenodo.3520509">10.5281/zenodo.3520509</a>.  Methods: The curation and quality control of MARMICRODB single-cell, metagenome-assembled, and isolate genomes was performed as described in [15]. Briefly, we downloaded all MARMICRODB genomes as raw nucleotide assemblies from NCBI. We determined an initial genome taxonomy for these assemblies using checkM with the default lineage workflow [16]. All genome bins met the completion/contamination thresholds outlined in prior studies [7,17]. For single cell and metagenome-assembled genomes, especially those from Tara Oceans Mediterranean sea samples [18], we use the GTDB-Tk classification workflow [19] to verify the taxonomic fidelity of each genome bin. We then selected genomes with a checkM taxonomic assignment of Prochlorococcus, Synechococcus, SAR11/Pelagibacterales, SAR86, and SAR116 for further analysis and confirmed taxonomic assignment using blast matches to known Prochlorococcus/Synechococcus ITS sequences and by matching 16S sequences to the SILVA database [20]. To refine our estimates of completeness/contamination of Prochlorococcus genome bins we created a custom set of 730 single copy protein families (available from <a href="https://doi.org/10.5281/zenodo.3719132">10.5281/zenodo.3719132</a>) from closed, isolate Prochlorococcus genomes [21] for quality assessments with checkM. For Synechococcus we used the CheckM taxonomic-specific workflow with the genus Synechococcus. After the custom CheckM quality control, we excluded any genome bins from downstream analysis that had an estimated quality < 30, defined as %completeness – 5x %contamination resulting in 18769 genome/transcriptome bins. We predicted genes in the resulting genome bins using prodigal [22] and excluded protein sequences with lengths less than 20 and greater than 20000 amino acids, removed non-standard amino acid residues, and condensed redundant protein sequences to a single representative sequence to which we assigned a lowest common ancestor (LCA) taxonomy identifier from the NCBI taxonomy database [23]. The resulting protein sequences were compiled and used to build a Kaiju [3] search database.  The above filtering criteria resulted in 605 Prochlorococcus, 96 Synechococcus, 186 SAR11/Pelagibacterales, 60 SAR86, and 59 SAR116 high-quality genome bins. We constructed a high-quality fixed reference phylogenetic tree for each taxonomic group based on genomes manually selected for completeness and the phylogenetic diversity. For example the Prochlorococcus and Synechococcus genomes for the fixed reference phylogeny are estimated > 90% complete, and SAR11 genomes are estimated > 70% complete. We created multiple sequence alignments of phylogenetically conserved genes from these genomes using the GTDB-Tk pipeline [19] with default settings. The pipeline identifies conserved proteins (120 bacterial proteins) and generates concatenated multi-protein alignments [17] from the genome assemblies using hmmalign from the hmmer software suite. We further filtered the resulting alignment columns using the bacterial and archaeal alignment masks from [17] (http://gtdb.ecogenomic.org/downloads). We removed columns represented by fewer than 50% of all taxa and/or columns with no single amino acid residue occurring at a frequency greater than 25%. We trimmed the alignments using trimal [24] with the automated -gappyout option to trim columns based on their gap distribution. We inferred reference phylogenies using multithreaded RAxML [25] with the GAMMA model of rate heterogeneity, empirically determined base frequencies, and the LG substitution model [26](PROTGAMMALGF). Branch support is based on 250 resampled bootstrap trees. This tree was then pruned to only allow a maximum average distance to the closest leaf (ADCL) of 0.003 to reduce the phylogenetic redundancy in the tree [27]. We then “placed” genomes that either did not pass completeness threshold or were considered phylogenetically redundant by ADCL within the fixed reference phylogeny for each group using pplacer [28] representing each placed genome as a pendant edge in the final tree. We then examined the resulting tree and manually selected clade/ecotype cutoffs to be as consistent as possible with clade definitions previously outlined for these groups [29–32]. We then gave clades from each taxonomic group custom taxonomic identifiers and we added these identifiers to the MARMICRODB Kaiju taxonomic hierarchy. Software/databases used: checkM v1.0.11[16] HMMERv3.1b2 (http://hmmer.org/) prodigal v2.6.3 [22] trimAl v1.4.rev22 [24] AliView v1.18.1 [33] [34] Phyx v0.1 [35] RAxML v8.2.12 [36] Pplacer v1.1alpha [28] GTDB-Tk v0.1.3 [19] Kaiju v1.6.0 [34] GTDB RS83 (https://data.ace.uq.edu.au/public/gtdb/data/releases/release83/83.0/) NCBI Taxonomy (accessed 2018-07-02) [23] TIGRFAM v14.0 [37] PFAM v31.0 [38] Use example: Because we used custom taxonomic MARMICRODB users will find many reads assigned to non-standard NCBI taxonomy identifiers. However, these reads are easily parsable using the custom names.dmp and nodes.dmp files included with the database. We include a brief description of how to do this below. I typically run Kaiju like: <pre><code>kaiju -z 20 -a greedy -e 5 -m 11 -s 65 -E 0.05 -x \ -t nodes.dmp -f MARMICRODB.fmi \ -i inputfile_R1.fastq.gz \ -j inputfile_R2.fastq.gz \ -o MYOUTPUT.kaiju</code></pre> To obtain a parseable report that lists the custom taxonomic ranks from the nodes.dmp and names.dmp files run kaiju2krona on the output. <pre><code>kaiju2krona -t nodes.dmp -n names.dmp -i MYOUTPUT.kaiju -o MYOUTPUT.kaiju.krona </code></pre> This report shows counts assigned to each node in the custom taxonomy and will also include the names for each rank. You can easily parse this programmatically using a scripting language like python or by using unix utilities. File descriptions: MARMICRODB_catalog.tsv Tabular file of NCBI assembly accessions and associated taxonomic information for every genome in MARMICRODB. Also includes literature references for each genome where available. Header description: <ol> <li>genome: Unique identifier for each genome</li> <li>full_name: full organism name where available</li> <li>source: literature reference where available</li> <li>taxid: NCBI taxonomy ID for the assembly accession</li> <li>MARMICRODBtaxid: taxonomy ID used in the custom Kaiju database</li> <li>lineage_assignment: taxonomic lineage assignment from NCBI</li> <li>domain: archaea, bacteria, or eukaryote</li> <li>taxgroup: short descriptive group</li> <li>taxclade: higher resolution clade assignment where available</li> <li>habitat_source: whether genome derives from marine or aquatic source</li> <li>sequence_type: isolate, single cell genome (sag), metagenome assembled genome (mag), or transcriptome in case of eukaryotes</li> <li>assembly_ftp: NCBI ftp for assembly</li> <li>gbk_acc: assembly genbank or refseq accession number</li> <li>gtdb_taxonomy: taxonomic lineage assignment from GTDB-Tk v0.1.3 against GTDB v83</li> </ol> MARMICRODB_kronaplot.html <a href="http://htmlpreview.github.io/?https://github.com/slhogle/MARMICRODB/blob/master/MARMICRODB_kronaplot.html">Interactive Kronaplot</a> for the exploration of taxonomic composition of MARMICRODB MARMICRODB.faa.bz2 Fasta file of all protein sequences in MARMICRODB scripts.tar.gz directory containing scripts for generating Kaiju formatted database phylogenies.tar.gz directory containing detailed phylogenies for SAR11, Prochlorococcus, SAR86, and SAR116 MARMICRODB.fmi Kaiju index for MARMICRODB nodes.dmp nodes file for taxonomic assignment with Kaiju names.dmp names file for generating Kaiju reports References: 1.     Karsenti E, Acinas SG, Bork P, Bowler C, De Vargas C, Raes J, et al. A holistic approach to marine eco-systems biology. PLoS Biol. 2011;9: e1001177. 2.     Biller SJ, Berube PM, Dooley K, Williams M, Satinsky BM, Hackl T, et al. Marine microbial metagenomes sampled across space and time. Scientific Data. 2018;5: 180176. 3.     Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7: 11257. 4.     Mende DR, Letunic I, Huerta-Cepas J, Li SS, Forslund K, Sunagawa S, et al. proGenomes: a resource for consistent functional and taxonomic annotations of prokaryotic genomes. Nucleic Acids Res. 2017;45: D529–D534. 5.     Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 2014;12: e1001889. 6.     Tully BJ, Sachdeva R, Graham ED, Heidelberg JF. 290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ. 2017;5: e3558. 7.     Tully BJ, Graham ED, Heidelberg JF. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci Data. 2018;5: 170203. 8.     Haroon MF, Thompson LR, Parks DH, Hugenholtz P, Stingl U. A catalogue of 136 microbial draft genomes from Red Sea metagenomes. Sci Data. 2016;3: 160050. 9.     Hugerth LW, Larsson J, Alneberg J, Lindh MV, Legrand C, Pinhassi J, et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biol. 2015;16: 279. 10.     Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017. 11.     Mukherjee S, Seshadri R, Varghese NJ, Eloe-Fadrosh EA, Meier-Kolthoff JP, Göker M, et al. 1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life. Nat Biotechnol. 2017;35: 676–683. 12.     Haft DH, DiCuccio M, Badretdin A, Brover V, Chetvernin V, O’Neill K, et al. RefSeq: an update on prokaryotic genome annotation and curation. Nucleic Acids Res. 2018;46: D851–D860. 13.     Klemetsen T, Raknes IA, Fu J, Agafonov A, Balasundaram SV, Tartari G, et al. The MAR databases: development and implementation of databases specific for marine metagenomics. Nucleic Acids Res. 2018;46: D692–D699. 14.     Berube PM, Biller SJ, Hackl T, Hogle SL, Satinsky BM, Becker JW, et al. Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments. Scientific Data. 2018;5: 180154. 15.     Becker JW, Hogle SL, Rosendo K, Chisholm SW. Co-culture and biogeography of Prochlorococcus and SAR11. ISME J. 2019. doi:10.1038/s41396-019-0365-4 16.     Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25: 1043–1055. 17.     Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017. doi:10.1038/s41564-017-0012-7 18.     Tully BJ, Sachdeva R, Graham ED, Heidelberg JF. 290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ. 2017;5: e3558. 19.     Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019. doi:10.1093/bioinformatics/btz848 20.     Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41: D590–6. 21.     Biller SJ, Berube PM, Berta-Thompson JW, Kelly L, Roggensack SE, Awad L, et al. Genomes of diverse isolates of the marine cyanobacterium Prochlorococcus. Sci Data. 2014;1: 140034. 22.     Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11: 119. 23.     Federhen S. The NCBI Taxonomy database. Nucleic Acids Res. 2012;40: D136–43. 24.     Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25: 1972–1973. 25.     Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22: 2688–2690. 26.     Le SQ, Gascuel O. An improved general amino acid replacement matrix. Mol Biol Evol. 2008;25: 1307–1320. 27.     Matsen FA, Gallagher A, McCoy C. Minimizing the average distance to a closest leaf in a phylogenetic tree. arXiv [q-bio.PE]. 2012. Available: http://arxiv.org/abs/1205.6867 28.     Matsen F a., Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics. 2010;11: 538. 29.     Biller SJ, Berube PM, Lindell D, Chisholm SW. Prochlorococcus: the structure and function of collective diversity. Nat Rev Microbiol. 2014;13: 13–27. 30.     Giovannoni SJ. SAR11 Bacteria: The Most Abundant Plankton in the Oceans. Ann Rev Mar Sci. 2016. 31.     Dupont CL, Rusch DB, Yooseph S, Lombardo M-J, Alexander Richter R, Valas R, et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 2012;6: 1186–1199. 32.     Yang S-J, Kang I, Cho J-C. Expansion of Cultured Bacterial Diversity by Large-Scale Dilution-to-Extinction Culturing from a Single Seawater Sample. Microb Ecol. 2016;71: 29–43. 33.     Larsson A. AliView: a fast and lightweight alignment viewer and editor for large datasets. Bioinformatics. 2014;30: 3276–3278. 34.     Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7: 11257. 35.     Brown JW, Walker JF, Smith SA. Phyx: phylogenetic tools for unix. Bioinformatics. 2017;33: 1886–1888. 36.     Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22: 2688–2690. 37.     Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK, Beck E. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Res. 2013;41: D387–95. 38.     Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 2016;44: D279–85. 39.     Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018.

opencc-by-4.0Nov 2022View details →

Genetic Features of the Marine Polychaete Sirsoe methanicola from Metagenomic Data

WebAUGUSTUS input and output data used in for eukaryotic gene prediction in Sirsoe methanicola: WebAUGUSTUS input data: <ol> <li>capitella.fa - Nucleotide genomic sequence of Capitella teleta (NCBI accession: GCA_000328365.1)</li> <li>capitella-protein.faa - Protein sequences in the Capitella teleta genome (NCBI accession: GCA_000328365.1)</li> <li>big-contigs-wrapped.fa - Contigs >= 3,000 bp long assembled from the S. methanicola metagenomes (NCBI BioProject ID PRJNA689840) that did not bin into any bacterial MAGs</li> <li>small-contigs-wrapped.fa - Contigs< 3,000 bp long assembled from the S. methanicola metagenomes (NCBI BioProject ID PRJNA689840) that did not bin into any bacterial MAGs</li> </ol> WebAUGUSTUS output data: <ol> <li>augustus.bigcontigs.gff - WebAUGUSTUS GFF output file for contigs >=3,000 bp</li> <li>augustus.smallcontigs.gff - WebAUGUSTUS GFF output file for contigs <3,000 bp</li> <li>augustus.all.faa - All protein sequences predicted from the S. methanicola metagenomes using WebAUGUSTUS</li> </ol>

opencc-by-4.0Dec 2022View details →

Investigation of machine learning algorithms for taxonomic classification of marine metagenomes

Training, testing, and blind datasets used for machine learning algorithms for taxonomic classification of marine metagenomes: <ol> <li>K12.kmers.txt - 12bp k-mer vocabulary constructed by Jellyfish v1.1.11 from 47,894 genomes in GTDB release 202</li> <li>MarRef_1.6.tsv - Metadata file downloaded from MarRef v1.6</li> <li>MarRef.genustrain.fasta - Training set from MarRef v1.6 (seed=808) used for genus classification</li> <li>MarRef.genustest.fasta - Testing set from MarRef v1.6 (seed=747) used for genus classification </li> <li>MarRef.speciestrain.fasta - Training set from MarRef v1.6 (seed=808) used for species classification</li> <li>MarRef.speciestest.fasta - Testing set from MarRef v1.6 (seed=747) used for species classification</li> <li>MarRef.traintest.key.tsv - Table containing MarRef accession, GenBank accession, GenBank taxonomy ID, taxonomic information, and labels used for species and genus testing and training</li> <li>anonymous_reads_*.fq - Blind datasets (1-10) in interleaved fastq format</li> <li>reads_mapping_*.tsv - Key for blind datasets 1-10. Each sequence header is mapped to its corresponding MarRef accession and NCBI taxonomic ID.</li> </ol>

opencc-by-4.0Mar 2023View details →

metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data - use case

Data products returned by <a href="https://github.com/emo-bon/MetaGOflow">metaGOflow</a> (<a href="https://github.com/emo-bon/MetaGOflow/releases/tag/v1.0.0">v1.0.0</a>) and packed as a Research Object (RO) Crate, when performed with: <ul> <li>a seawater metagenomic sample (TARA OCEAN, <a href="https://www.ebi.ac.uk/ena/browser/view/ERR599171">ERR599171</a>)</li> <li>a fish gut sample (<a href="https://www.ebi.ac.uk/ena/browser/view/ERR4765907">ERR4765907</a>)</li> <li>a human gut sample (<a href="https://www.ebi.ac.uk/ena/browser/view/SRR9654976">SRR9654976</a>)</li> </ul> This Zenodo repo accompanies the metaGOflow paper and more about the analysis of this sample can be found there. You can also have a look at some visual components of the workflow at this <a href="https://data.emobon.embrc.eu/MetaGOflow/">GitHub page</a>.  The source code of metaGOflow is available through <a href="http://github.com/emo-bon/MetaGOflow">GitHub</a>.

Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction

The data consists of Illumina raw reads obtained from prokaryotes from coastal Mediterranean seawaters in Alicante, Spain, over the course of one year. Illumina DNA prep kit (IDT, Ref. 20026930) was used following the manufacturer's protocol to generate and sequence the libraries for the obtained DNA samples from  prokaryotic metagenomes. Subsequently, these libraries were sequenced using HiSeq X technology (150 PE, 1 lane) by two different facilities: Macrogen in Seoul, Republic of Korea, and the Genomics Unit of the Center for Genomic Regulation (CRG) in Barcelona, Spain. The dataset was used in order to ascertain the main microbial producers involved in the release of DNA packaged in EVs in our annual sampling. The samples are named according to the convention 'prokmeta[Month], where 'prokmeta' signifies prokaryotic metagenome, '[Month]' indicates the month of sampling, and "1" represents the forward , while (2) represent the  reverse read. For instance, 'prokmetaDEC.1' denotes the prokaryotic metagenome  for sample of December, with '1' indicating the forward read, while 'prokmetaDEC.2' represents the corresponding reverse read. This naming scheme is consistent across all samples in the dataset from sample of November 2021 to sample of June 2022.

Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction.

The dataset consists of third part of data for the same paper " Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction." Data were uploaded separately due to the limited space available for uploading which is f 50.00 GB and since i have more I could not upload them together. The data are Illumina raw reads obtained from vesicle fractions of 20%, isolated from Coastal Mediterranean seawaters in Alicante, Spain, over the course of one year. Illumina DNA prep kit (IDT, Ref. 20026930) was used following the manufacturer's protocol to generate and sequence the libraries for the obtained DNA samples from EVs 20% fraction metagenomes. Subsequently, these libraries were sequenced using HiSeq X technology (150 PE, 1 lane) by two different facilities: Macrogen in Seoul, Republic of Korea, and the Genomics Unit of the Center for Genomic Regulation (CRG) in Barcelona, Spain. The dataset was used in order to ascertain the main microbial organisms involved in the production of EVs and packaging of DNA into those  EVs in our annual sampling  and the types of genes that were packaged in vessicles isolated from the Coastal Mediterranean seawaters (Functional annotation of genes packaged in vesicles).  The data are labeled as  "EVs_20_DEC_1.fastq"  which denotes the vesicle 20% Optiprep fraction for sample  collected in December, with "1" indicating the forward read, while "EVs_20_DEC_2.fastq" represents the corresponding reverse read. This naming scheme is consistent across all vesicle fraction samples in the dataset.

Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction

The data consists  second part of data for the same paper " Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction." Data were uploaded separately due to the limited space available for uploading. The data are Illumina raw reads obtained from vesicle fractions of 25%, isolated from coastal Mediterranean seawaters in Alicante, Spain, over the course of one year. Illumina DNA prep kit (IDT, Ref. 20026930) was used following the manufacturer's protocol to generate and sequence the libraries for the obtained DNA samples from EVs fractions metagenomes. Subsequently, these libraries were sequenced using HiSeq X technology (150 PE, 1 lane) by two different facilities: Macrogen in Seoul, Republic of Korea, and the Genomics Unit of the Center for Genomic Regulation (CRG) in Barcelona, Spain. The dataset was used in order to ascertain the main microbial organisms involved in the production of EVs and packaging of DNA into those  EVs in our annual sampling  and the types of genes that were packaged in vessicles isolated from the Coastal Mediterranean seawaters (Functional annotation of genes packaged in vesicles).  The data are labeled as  "EVs_25_DEC_1.fastq"  which denotes the vesicle 25% Optiprep fraction for sample  collected in December, with "1" indicating the forward read, while "EVs_25_DEC_2.fastq" represents the corresponding reverse read. This naming scheme is consistent across all vesicle fraction samples in the dataset.

opencc-by-4.0Nov 2022View details →

Supplementary Dataset of Marine Metagenomes from Surface Sediments along the coastline of Kuwait

This dataset was obtained through shot gun metagenomic sequencing conducted on surface sediments in close vicinity of emergency outfalls discharged through the storm outlets. It provides information on the diverse microbial communities and dominant metabolic functions occuring at these sites under a selective force of various contaminants brought in through the effluent discharges.

zenodo32/100

In situ microcosms deployed at the coast of British Columbia (Canada) to study dilbit weathering and associated microbial communities under marine conditions. (Metagenomic data support).

This file repository contains the co-assembly of all samples from the manuscript entitled "In situ microcosms deployed at the coast of British Columbia (Canada) to study dilbit weathering and associated microbial communities under marine conditions". It also contains predicted genes sequence files in faa (fasta amino acids) and fna (fasta nucleic acids) formats.

opencc-by-4.0Aug 2020View details →

zenodo32/100

Coupled metalipidomics-metagenomics reveal structurally diverse sphingolipids produced by a wide variety of marine bacteria

Abstract Microbial lipids, used as taxonomic markers and physiological indicators, have mainly been studied through cultivation. However, this approach is limited due to the scarcity of cultures of environmental microbes, thereby restricting insights into the diversity of lipids and their ecological roles. Addressing this limitation, here we apply metalipidomics combined with metagenomics in the Black Sea, classifying and tentatively identifying 1623 lipid-like species across 18 lipid classes. We discovered over 200 novel, abundant, and structurally diverse sphingolipids in euxinic waters, including unique 1-deoxysphingolipids with long-chain fatty acids and sulfur-containing groups. Sphingolipids were thought to be rare in bacteria and their molecular and ecological functions in bacterial membranes remain elusive. However, genomic analysis focused on sphingolipid biosynthesis genes revealed that members of 38 bacterial phyla in the Black Sea can synthesize sphingolipids, representing a fourfold increase from previously known capabilities and accounting for up to 25% of the microbial community. These sphingolipids appear to be involved in oxidative stress response, cell wall remodeling and are associated with the metabolism of nitrogen-containing molecules. Our findings underscore the effectiveness of multi-omics approaches in exploring microbial chemical ecology. <div> </div> Repository content: 1) metalipidome_sphingolipids.zip: includes source data and code scripts used for figures regarding metalipidome and sphingolipids abundance, classification and diversity in this study. Files are organized as follows and are associated with the corresponding parts of the manuscript: Fig. 1a, Fig. 1b,  Fig. 2b,  Fig. 2c, Fig. 2e,  Fig. 2f, Fig. 2g, Fig. 2h, Fig. 4d, Supplementary Fig. 2, Supplementary Fig. 3. 2) Source data_major lipid classification.xlsx: includes original tables regarding metalipidome identification, abundance, precursor mass, retention time, classification as well as ID (name) in the molecular network. 3) Source data_sphingolipids information.xlsx: includes information about sphingolipids identification, precusor mass, retention time, peak intensity, elemental composition and etc. 4) Black_Sea_2013.code.tar.gz: contains the directory structure and code used for the metagenomics part of this project. Each directory contains a 'commands.sh', which contains the code to generate the content in that directory. Other shell and python scripts are always run from within 'commands.sh', with the exception of the files within the 'figures' directory which contains Jupyter labs and a python script that were run individually. 5) MAGs.tar.gz: all the MAGs generated by DAS Tool including CheckM and GTDB-Tk analyses (inside the 'binners' directory). Final taxonomic annotations of MAGs (see 'MAG2info.txt' file) are based on BAT annotatations with GTDB as a reference database, source data in the directory 'CAT_and_BAT_with_GTDB_refdb'. 6) abundance_profile.tar.gz: taxonomic annotation (based on CAT and BAT) and abundance of all scaffolds in the file 'big_table.txt'. Columns that start with 'mappings' are the read counts mapping to the scaffold in the sample from which it is assembled. Since the scaffolds were assembled per sample, only one of the 15 samples contains read mappings per scaffold. The last 15 columns (that start with 'BlackSea') are the depth per 1e8 mapped reads based on the all versus all mappings and were used for co-abundance analyses with sphingolipids. The file 'taxon2counts.txt' summarizes the taxonomic composition of the water column based on summing of the read mappings of the samples from which scaffolds were assembled (the 'mappings' columns in 'big_table.txt'), i.e. they represent all reads that could be associated with a certain taxon in that sample. The 'taxon2counts.txt' file used in Fig. 3c,d.

opencc-by-4.0Dec 2023View details →

zenodo32/100

Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction

The dataset includes additional data for the same paper, "Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction." The data were uploaded separately due to the 50.00 GB limit on available upload space. The gene sequences contained within EVs were annotated using the DRAM program within the Kbase platform, enabling us to identify the specific types and functions of genes packaged within EV fractions. This annotation process provided a comprehensive overview of the genetic cargo potentially encapsulated in EV fractions 25% and 20%. The provided Excel files represent the output from DRAM annotation and contain the detected genes in EV fractions 25% and 20%. Although the gene names in the files are labeled as 30%, 25%, and 20%, this is a naming error. The file labeled "annotation for EVs 25%" contains genes detected in the 25% EV fraction, while the file labeled "annotation for EVs 20%" corresponds to genes from the 20% EV fraction. There are two replicates for the 20% EV fraction, denoted as rep1 and rep2, and both samples were analyzed. The gene abundance was calculated as the mean percentage of the two replicates, corrected with standard error.

Supplementary material 9 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208

Table S4

Supplementary material 3 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208

Figure S3

Supplementary material 8 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208

Table S3

Supplementary material 5 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208

Figure S5

Supplementary material 6 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208

Table S1

Supplementary material 1 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208

Figure S1

Supplementary material 7 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208

Table S2

Supplementary material 2 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208

Figure S2

Supplementary material 4 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208

Figure S4