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MarFERReT: an open-source, version-controlled reference library of marine microbial eukaryote functional genes
<p>Metatranscriptomics generates large volumes of sequence data about transcribed genes in natural environments. Taxonomic annotation of these datasets depends on availability of curated reference sequences. For marine microbial eukaryotes, current reference libraries are limited by gaps in sequenced organism diversity and barriers to updating libraries with new sequence data, resulting in taxonomic annotation of only about half of eukaryotic environmental transcripts. Here, we introduce version 1.0 of the Marine Functional EukaRyotic Reference Taxa (MarFERReT), an updated marine microbial eukaryotic sequence library with a version-controlled framework designed for taxonomic annotation of eukaryotic metatranscriptomes. We gathered 902 marine eukaryote genomes and transcriptomes from multiple sources and assessed these candidate entries for sequence quality and cross-contamination issues, selecting 800 validated entries for inclusion in the library. MarFERReT v1 contains reference sequences from 800 marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level taxa, totaling nearly 28 million protein sequences with associated NCBI and PR2 Taxonomy identifiers and Pfam functional annotations. An accompanying MarFERReT project repository hosts containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT.<br><br>MarFERReT is linked to a code repository hosting containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT here: <a href="https://github.com/armbrustlab/marferret">https://github.com/armbrustlab/marferret</a></p> <p>The raw source data for the 902 candidate entries considered for MarFERReT v1.1.1, including the 800 accepted entries, are available for download from their respective online locations. The source URL for each of the entries is listed here in MarFERReT.v1.1.1.entry_curation.csv, and detailed instructions and code for downloading the raw sequence data from source are available in the MarFERReT code repository (<a href="https://github.com/armbrustlab/marferret/blob/main/docs/process_clean_marmicrodb.log.sh">link</a>). </p> <p>This repository release contains MarFERReT database files from the v1.1.1 MarFERReT release using the following MarFERReT library build scripts: <strong>assemble_marferret.sh</strong>, <strong>pfam_annotate.sh</strong>, and <strong>build_diamond_db.sh</strong><br><br>The following MarFERReT data products are available in this repository:</p> <p><strong>MarFERReT.v1.1.1.metadata.csv</strong><br>This CSV file contains descriptors of each of the 902 database entries, including data source, taxonomy, and sequence descriptors. Data fields are as follows:</p> <ol> <li><strong>entry_id</strong>: Unique MarFERReT sequence entry identifier.</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y/N). The Y/N values can be adjusted to customize the final build output according to user-specific needs.</li> <li><strong>marferret_name</strong>: A human and machine friendly string derived from the NCBI Taxonomy organism name; maintaining strain-level designation wherever possible.</li> <li><strong>tax_id</strong>: The NCBI Taxonomy ID (taxID).</li> <li><strong>pr2_accession</strong>: Best-matching PR2 accession ID associated with entry</li> <li><strong>pr2_rank</strong>: The lowest shared rank between the entry and the pr2_accession</li> <li><strong>pr2_taxonomy</strong>: PR2 Taxonomy classification scheme of the pr2_accession</li> <li><strong>data_type</strong>: Type of sequence data; transcriptome shotgun assemblies (TSA), gene models from assembled genomes (genome), and single-cell amplified genomes (SAG) or transcriptomes (SAT).</li> <li><strong>data_source</strong>: Online location of sequence data; the Zenodo data repository (<a href="../">Zenodo</a>), the datadryad.org repository (<a href="http://datadryad.org/">datadryad.org</a>), MMETSP re-assemblies on Zenodo (MMETSP)17, NCBI GenBank (<a href="https://www.ncbi.nlm.nih.gov/genbank/">NCBI</a>), JGI Phycocosm (<a href="https://phycocosm.jgi.doe.gov/phycocosm/home">JGI-Phycocosm</a>), the TARA Oceans portal on Genoscope (<a href="http://www.genoscope.cns.fr/tara/">TARA</a>), or entries from the Roscoff Culture Collection through the METdb database repository (<a href="https://metdb.sb-roscoff.fr/metdb/">METdb</a>).</li> <li><strong>source_link</strong>: URL where the original sequence data and/or metadata was collected.</li> <li><strong>pub_year</strong>: Year of data release or publication of linked reference.</li> <li><strong>ref_link</strong>: Pubmed URL directs to the published reference for entry, if available.</li> <li><strong>ref_doi</strong>: DOI of entry data from source, if available.</li> <li><strong>source_filename</strong>: Name of the original sequence file name from the data source.</li> <li><strong>seq_type</strong>: Entry sequence data retrieved in nucleotide (nt) or amino acid (aa) alphabets.</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file.</li> <li><strong>source_name:</strong> Full organism name from entry source</li> <li><strong>original_taxID</strong>: Original NCBI taxID from entry data source metadata, if available</li> <li><strong>alias:</strong> Additional identifiers for the entry, if available</li> </ol> <p><br><strong>MarFERReT.v1.1.1.curation.csv</strong><br>This CSV file contains curation and quality-control information on the 902 candidate entries considered for incorporation into MarFERReT v1, including curated NCBI Taxonomy IDs and entry validation statistics. Data fields are as follows:</p> <ol> <li><strong>entry_id:</strong> Unique MarFERReT sequence entry identifier</li> <li><strong>marferret_name: </strong>Organism name in human and machine friendly format, including additional NCBI taxonomy strain identifiers if available.</li> <li><strong>tax_id</strong>: Verified NCBI taxID used in MarFERReT</li> <li><strong>taxID_status</strong>: Status of the final NCBI taxID (Assigned, Updated, or Unchanged)</li> <li><strong>taxID_notes</strong>: Notes on the original_taxID</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file</li> <li><strong>n_pfams</strong>: Number of Pfam domains identified in protein sequences</li> <li><strong>qc_flag</strong>: Early validation quality control flags for the following: LOW_SEQS; less than 1,200 raw sequences; LOW_PFAMS; less than 500 Pfam domain annotations.</li> <li><strong>flag_Lasek</strong>: Flag notes from Lasek-Nesselquist and Johnson (2019); contains the flag 'FLAG_LASEK' indicating ciliate samples reported as contaminated in this study.</li> <li><strong>VV_contam_pct</strong>: Estimated contamination reported for MMETSP entries in Van Vlierberghe et al., (2021).</li> <li><strong>flag_VanVlierberghe: </strong>Flag for a high level of estimated contamination, from 'flag_VanVlierberghe' values over 50%: FLAG_VV.</li> <li><strong>rp63_npfams</strong>: Number of ribosomal protein Pfam domains out of 63 total.</li> <li><strong>rp63_contam_pct</strong>: Percent of total ribosomal protein sequences with an inferred taxonomic identity in any lineage other than the recorded identity, as described in the Technical Validation section from analysis of 63 Pfam ribosomal protein domains.</li> <li><strong>flag_rp63</strong>: Flag for a high level of estimated contamination, from 'rp63_contam_pct' values over 50%: FLAG_RP63.</li> <li><strong>flag_sum: </strong>Count of the number of flag columns (`qc_flag`, `flag_Lasek`, `flag_VanVlierberghe`, and `flag_rp63`). All entries with one or more flag are nominally rejected ('accepted' = N); entries without any flags are validated and accepted ('accepted' = Y).</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y or N).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins.faa.gz</strong><br>This Gzip-compressed FASTA file contains the 27,951,013 final translated and clustered protein sequences for all 800 accepted MarFERReT entries. The sequence defline contains the unique identifier for the sequence and its reference (mftX, where 'X' is a ten-digit integer value). </p> <p> </p> <p><strong>MarFERReT.v1.1.1.taxonomies.tab.gz</strong><br>This Gzip-compressed tab-separated file is formatted for interoperability with the DIAMOND protein alignment tool commonly used for downstream analyses and contains some columns without any data. Each row contains an entry for one of the MarFERReT protein sequences in MarFERReT.v1.proteins.faa.gz. Note that 'accession.version' and 'taxid' are populated columns while 'accession' and 'gi' have NA values; the latter columns are required for back-compatibility as input for the DIAMOND alignment software and LCA analysis. </p> <p>The columns in this file contain the following information:</p> <ol> <li><strong>accession</strong>: (NA)</li> <li><strong>accession.version</strong>: The unique MarFERReT sequence identifier ('mftX').</li> <li><strong>taxid</strong>: The NCBI Taxonomy ID associated with this reference sequence.</li> <li><strong>gi</strong>: (NA).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins_info.tab.gz</strong><br>This Gzip-compressed tab-separated file contains a row for each final MarFERReT protein sequence with the following columns:</p> <ol> <li><strong>aa_id</strong>: the unique identifier for each MarFERReT protein sequence.</li> <li><strong>entry_id</strong>: The unique numeric identifier for each MarFERReT entry.</li> <li><strong>source_defline</strong>: The original, unformatted sequence identifier</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.best_pfam_annotations.csv.gz<br></strong>This Gzip-compressed CSV file contains the best-scoring Pfam annotation for intra-species clustered protein sequences from the 800 validated MarFERReT entries; derived from the hmmsearch annotations against Pfam 34.0 functional domains. This file contains the following fields:</p> <ol> <li><strong>aa_id</strong>: The unique MarFERReT protein sequence ID ('mftX').</li> <li><strong>pfam_name</strong>: The shorthand Pfam protein family name.</li> <li><strong>pfam_id</strong>: The Pfam identifier.</li> <li><strong>pfam_eval</strong>: hmm profile match e-value score</li> <li><strong>pfam_score:</strong> hmm profile match bitscore</li> </ol> <p><br><strong>MarFERReT.v1.1.1.dmnd</strong><br>This binary file is the indexed database of the MarFERReT protein library with embedded NCBI taxonomic information generated by the DIAMOND makedb tool using the build_diamond_db.sh script from the MarFERReT /scripts/ library. This can be used as the reference DIAMOND database for annotating environment sequences from eukaryotic metatranscriptomes. <br><br></p>
Diel-regulated transcriptional cascades of microbial eukaryotes in the North Pacific Subtropical Gyre
<p>Trinity <em>de novo </em>assemblies of 24 poly-A+ selected, combined-replicate metatranscriptomes from HOE-Legacy 2 cruise KM1513 (Jul 24 - Aug 6, 2015). KM1513 cruise information, plots, and associated environmental data for the HOE Legacy II cruise can be found online at <a href="http://hahana.soest.hawaii.edu/hoelegacy/hoelegacy.html">http://hahana.soest.hawaii.edu/hoelegacy/hoelegacy.html</a>. Raw metatranscriptome short-read sequence data is available in the NCBI Sequence Read Archive under BioProject ID PRJNA492142. Code associated with this project is available on Github (<a href="https://github.com/armbrustlab/diel_eukaryotes">https://github.com/armbrustlab/diel_eukaryotes</a>).</p> <p> </p> <p> </p>
Plumes and Blooms: Microbial eukaryote diversity and composition
These are amplicon sequencing data collected during Plumes and Blooms (PnB) cruises conducted from March, 2011, through September, 2014. The V9 hypervariable region of the 18S rRNA gene derived from microbial eukaryotic communities was amplified and sequenced from 345 discrete seawater samples. Sample collection and laboratory methods are described in Catlett et al. 2020 and Catlett et al. in review. Bioinformatic and data manipulation methods follow those employed in Catlett et al. in review. The data are provided in two tables: one includes amplicon sequence variant (ASV) sequences and relative sequence abundances for each sampling event, and the other includes ASV taxonomy predictions for each ASV sequence. References: Catlett, D., P. G. Matson, C. A. Carlson, E. G. Wilbanks, D. A. Siegel, and M. D. Iglesias‐Rodriguez. 2020. Evaluation of accuracy and precision in an amplicon sequencing workflow for marine protist communities. Limnol. Oceanogr.: Methods. 18(1): 20-40. https://doi.org/10.1002/lom3.10343. Catlett, D., D. A. Siegel, P. G. Matson, E. K. Wear, C. A. Carlson, T. S. Lankiewicz, and M. D. Iglesias‐Rodriguez. In review. Integrating phytoplankton pigment and DNA meta-barcoding observations to determine phytoplankton community composition in the coastal ocean. Limnol. Oceanogr.
Fig. 3. A in Changing Views of Arctic Protists (Marine Microbial Eukaryotes) in a Changing Arctic
Fig. 3. A – Whole eukaryotic microbial community bootstrap-supported UPGMA hierarchical clustering tree based on Bray-Curtis ss-diversity metrics. Metazoa were excluded of this analysis; OTU definition as 98% similarity. Analysis were carried out in Qiime as in Kuczynski et al. 2002; samples were subsampled 100 times selecting 2533 sequences (75% of the smallest subsample). B – Only rare eukaryotic community bootstrap-supported UPGMA hierarchical clustering tree based on Bray-Curtis ss-diversity metrics. Metazoa and abundant OTUs (> 0.1%) were excluded of this analysis; OTU definition as 98% similarity. Analysis were carried out in Qiime as in Kuczynski et al. 2002; samples were subsampled 100 times selecting 125 sequences (75% of the smallest subsample).
Fig. 1 in Changing Views of Arctic Protists (Marine Microbial Eukaryotes) in a Changing Arctic
Fig. 1. Polar projections of Arctic Ocean, indicating the three regions outside of the Beaufort Sea, used as an example of community clustering in this review.
Fig. 1 in Eukaryotic Microbial Communities Associated with Rock-dwelling Foliose Lichens: A Functional Morphological and Microecological Analysis
Fig. 1. Photograph of a portion of a Flavoparmelia thallus showing an example of a radially oriented lobe with three segments sampled in analyzing the microbial communities: A – inner, B – middle, and C – outer. Scale bar: 5 mm.
Fig. 2 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. 2. Growth curves of Arcella intermedia and Pyxidicula operculata in the monospecific culture experiments (three replicates each). Dots represent the raw sampled data; colored intervals represent the 95% credibility intervals of cell counts from the Bayesian model fitting.
Fig. S2 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S2. Posterior distributions of the logistic model parameters. The values of K are in cells cm–2, r = d–1. P is the detection probability. P has a fixed range between 0.9 and 1. Color lines represents each one of the single-species experiments, color legend is in the right corner of the figure. A.intermedia experiments are Arc 1, 2 and 3. P.operculata experiments are Pyx 1, 2 and 3.
Fig. S1 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S1. Overview of data collection design. Microcosms are assembled and sampled by a sub- sampling strategy where the organisms are counted by eye. Model adjustment considers both the system dynamics and the sampling level.
Fig. S4. Growth curves for A.intermedia when started the experiment with a in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S4. Growth curves for A.intermedia when started the experiment with a single cell. Color points represents each one of the single-cell experiments, color legend is in the left corner of the figure. Black line correspond to the average growth between experiments.
Fig. S3 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S3. Posterior distributions of the competition model parameters for the species Arcella intermedia (A) and Pyxidicula operculata (P). Each colored line represent one of the replicates of the competition experiment (color legend shown in the last figure). The values of k are in a logarithmic scale of cells cm-2, r are in days–1. aAP is the competition coefficient of the influence of A species on P (Eq. 3), whereas aPA is the competition coefficient of the influence of P on A (Eq. 4).
Fig. 4 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. 4. Posterior estimates of the parameters of models fitted to cell counts in each culture. Each panel shows the medians (dots) and 95% credibility intervals (lines) of posterior distributions of one parameter of the models fitted to data from a replicate (seven for the competition cultures in lower part and three for mono-specific cultures in the upper part). In red, estimates for Arcella intermedia and in blue estimates for Pyxidicula operculata. The values of K are in cm–2, r are in days–1. The competition coefficients are α (red) and β (blue) of Eqs. 3–4.
Fig. 1 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. 1. Species used in this study. A – Arcella intermedia LEP isolate 6, magnification 630×. B – Pyxidicula operculata LEP isolate 1, magnification 1000×.
A Hard Day's Night: Diel shifts in microbial eukaryotic activity in the North Pacific Subtropical Gyre
<p><strong>A Hard Day’s Night: Diel shifts in microbial eukaryotic activity in the North Pacific Subtropical Gyre </strong>(<em>submitted</em>)</p> <p><strong>Authors: </strong>Sarah K. Hu<sup>1</sup>*, Paige E. Connell<sup>1</sup>, Lisa Y. Mesrop<sup>1</sup>, & David A. Caron<sup>1</sup></p> <p><sup>1</sup>University of Southern California, Biological Sciences, Los Angeles, CA, USA</p> <p> </p> <p><strong>Abstract</strong></p> <p>Molecular analysis revealed diel rhythmicity in the metabolic activity of single-celled microbial eukaryotes (protists) at station ALOHA in the North Pacific Subtropical Gyre. Diel trends among different protistan taxonomic groups reflected distinct nutritional capabilities and temporal niche partitioning. Changes in relative metabolic activities among phototrophs corresponded to the light cycle, generally peaking in mid- to late-afternoon. Metabolic activities of protistan taxa with phagotrophic ability were higher at night, relative to daytime, potentially in response to increased availability of picocyanobacterial prey. Tightly correlated Operational Taxonomic Units throughout the diel cycle implicated the existence of parasitic and mutualistic relationships within the microbial eukaryotic community, underscoring the need to define and include these symbiotic interactions in marine food web descriptions. This study provided a new high-resolution view into the ecologically important interactions among primary producers and consumers that mediate the transfer of carbon to higher trophic levels. Characterizations of the temporal dynamics of protistan activities contribute knowledge for predicting how these microorganisms respond to environmental forcing factors.</p> <p> </p> <p><a href="https://github.com/shu251/18Sdiversity_diel">See github for additional information on data analysis.</a></p>
Trophic guild data for microbial eukaryotes
<p>Data on trophic guilds of microbial eukaryotes derived from the following sources:</p> <p>Adl, S.M., Bass, D., Lane, C.E., Lukeš, J., Schoch, C.L., Smirnov, A., Agatha, S., Berney, C., Brown, M.W., Burki, F., Cárdenas, P., Čepička, I., Chistyakova, L., Campo, J. del, Dunthorn, M., Edvardsen, B., Eglit, Y., Guillou, L., Hampl, V., Heiss, A.A., Hoppenrath, M., James, T.Y., Karnkowska, A., Karpov, S., Kim, E., Kolisko, M., Kudryavtsev, A., Lahr, D.J.G., Lara, E., Gall, L.L., Lynn, D.H., Mann, D.G., Massana, R., Mitchell, E.A.D., Morrow, C., Park, J.S., Pawlowski, J.W., Powell, M.J., Richter, D.J., Rueckert, S., Shadwick, L., Shimano, S., Spiegel, F.W., Torruella, G., Youssef, N., Zlatogursky, V., Zhang, Q., 2019. Revisions to the Classification, Nomenclature, and Diversity of Eukaryotes. Journal of Eukaryotic Microbiology 66, 4–119. <a href="https://doi.org/10.1111/jeu.12691">https://doi.org/10.1111/jeu.12691</a></p> <p>Aleoshin, V.V., Mylnikov, A.P., Mirzaeva, G.S., Mikhailov, K.V. and Karpov, S.A., 2016. Heterokont Predator Develorapax marinus gen. et sp. nov.–A Model of the Ochrophyte Ancestor. Frontiers in microbiology, 7, p.1194. <a href="https://doi.org/10.3389/fmicb.2016.01194">https://doi.org/10.3389/fmicb.2016.01194</a></p> <p>Ann PJ, Huang JH, Wang IT, Ko WH, 2006. Pythiogeton zizaniae, a new species causing basal stalk rot of water bamboo in Taiwan. Mycologia 98: 116e120. <a title="https://doi.org/10.1080/15572536.2006.11832717" href="https://doi.org/10.1080/15572536.2006.11832717">https://doi.org/10.1080/15572536.2006.11832717</a></p> <p>Azevedo C., Hine P.M. (2016) Haplosporidia. In: Archibald J. et al. (eds) Handbook of the Protists. Springer, Cham. <a href="https://doi.org/10.1007/978-3-319-32669-6_16-1">https://doi.org/10.1007/978-3-319-32669-6_16-1</a></p> <p>Baldauf S.L., Strassmann J.E. (2017) Dictyostelia. In: Archibald J. et al. (eds) Handbook of the Protists. Springer, Cham. <a href="https://doi.org/10.1007/978-3-319-32669-6_14-1">https://doi.org/10.1007/978-3-319-32669-6_14-1</a> </p> <p>Beakes G.W., Thines M. (2016) Hyphochytriomycota and Oomycota. In: Archibald J. et al. (eds) Handbook of the Protists. Springer, Cham. <a href="https://doi.org/10.1007/978-3-319-32669-6_26-1">https://doi.org/10.1007/978-3-319-32669-6_26-1 </a></p> <p>Bell, E.M. and Laybourn‐Parry, J., 2003. Mixotrophy in the antarctic phytoflagellate Pyramimonas gelidicola (Chlorophyta: Prasinophyceae). Journal of Phycology, 39(4), pp.644-649. <a href="https://doi.org/10.1046/j.1529-8817.2003.02152.x">https://doi.org/10.1046/j.1529-8817.2003.02152.x</a></p> <p>Bennett R.M., Honda D., Beakes G.W., Thines M. (2017) Labyrinthulomycota. In: Archibald J. et al. (eds) Handbook of the Protists. Springer, Cham. <a href="https://doi.org/10.1007/978-3-319-32669-6_25-1">https://doi.org/10.1007/978-3-319-32669-6_25-1</a></p> <p>Bernard, Catherine, Alastair G. B. Simpson & David J. Patterson (2000) Some free-living flagellates (protista) from anoxic habitats Ophelia 52(2):113-142. <a href="https://doi.org/10.1080/00785236.1999.10409422">https://doi.org/10.1080/00785236.1999.10409422</a></p> <p>Bigelow, D. M., Olsen, M. W., & Gilbertson, R. L. (2005). Labyrinthula terrestris sp. nov., a new pathogen of turf grass. Mycologia 97:185–190. <a href="https://doi.org/10.1080/15572536.2006.11832852">https://doi.org/10.1080/15572536.2006.11832852</a></p> <p>Boltovskoy D., Anderson O.R., Correa N.M. (2017) Radiolaria and Phaeodaria. In: Archibald J. et al. (eds) Handbook of the Protists. Springer, Cham. <a href="https://doi.org/10.1007/978-3-319-32669-6_19-2">https://doi.org/10.1007/978-3-319-32669-6_19-2</a></p> <p>Bourland, W. A. & Struder-Kypke, M. C. 2010. Agolohymena aspidocauda nov. gen., nov. spec., a histophagous freshwater tetrahymenid ciliate in the family Deltopylidae (Ciliophora, Hymenostomatia), from Idaho (northwest USA): morphology, ontogenesis and molecular phylogeny. Eur. J. Protistol. 46:221–242. <a href="https://doi.org/10.1016/j.ejop.2010.04.003">https://doi.org/10.1016/j.ejop.2010.04.003</a></p> <p>Bower, S. M., McLean, N., & Whitaker, D. J. (1989). Mechanism of infection by Labyrinthuloides haliotidis (Protozoa, Labyrinthomorpha), a parasite of abalone (Haliotis kamtschatka) (Mollusca, Gastropoda). Journal of Invertebrate Pathology, 53, 401–409.</p> <p>Buaya, A. T., Ploch, S., Inaba, S., & Thines, M. (2019). Holocarpic oomycete parasitoids of red algae are not Olpidiopsis. Fungal systematics and evolution 4:21–31. <a href="https://doi.org/10.3114/fuse.2019.04.03">https://doi.org/10.3114/fuse.2019.04.03</a></p> <p>Bulman S., Neuhauser S. (2016) Phytomyxea. In: Archibald J. et al. 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Metabarcoding dataset of surface microbial eukaryotes in the Western Arctic Ocean (Canada Basin/Beaufort Sea)
<p>Fasta file of amplicon sequence variant (ASV) sequences, ASV phylogeny in Newick format, ASV table of relative abundance in samples and table of taxonomic information associated with each ASV. Raw sequence data were processed using DADA2 v1.14 and taxonomic assignment of ASV sequences was done using the PR2 database v4.12.0. The table of standardised environmental metadata (z-scores) is also provided.</p><p>Additionally, the OTU (98%) table of relative abundances in samples and phylogeny in Newick format is available.</p>
Fig. 2 in Changing Views of Arctic Protists (Marine Microbial Eukaryotes) in a Changing Arctic
Fig. 2. Loboea, collected from Northern Baffin Bay. Scale bar: 8 µm.
Supporting data and code for Nakov, Beaulieu, Alverson: Accelerated diversification is related to life history and locomotion in a hyperdiverse lineage of microbial eukaryotes (Diatoms, Bacillariophyta)
<p>This archive includes:</p> <p>1. Character and species richness datasets</p> <p>2. Phylogenies and time-calibration files</p> <p>3. R scripts</p>
Signal, Uncertainty, and Conflict in Phylogenomic Data for a Diverse Lineage of Microbial Eukaryotes (Diatoms, Bacillariophyta)
<p>This data depository contains analysis results from Parks, Wickett, and Alverson 2017 (Signal, Uncertainty, and Conflict in Phylogenomic Data for a Diverse Lineage of Microbial Eukaryotes (Diatoms, Bacillariophyta) (Mol. Biol. Evol. doi:10.1093/molbev/msx268)), and is made freely available to the research community.</p> <p>The file and subfolders here are as follows:</p> <p>gene_alignments<br> - contains compressed (tarred and gzipped) folders with all gene alignments at 0.2, 0.5 and 0.8 alignment column occupancy cutoffs. In each folder, there are also text files listing which gene alignments fall under which taxon occupancy subsetting strategy (i.e., 10-20% taxon occupancy, 40-60% taxon occupancy, 80-100% taxon occupancy, etc).</p> <p>gene_trees<br> - contains all (compressed) gene trees (bootstrapped versions, 100 bootstrap pseudo-replicates)) used in Astral analyses for each alignment column occupancy cutoff (0.2, 0.5, 0.8); nodes with less than 33% bootstrap support are collapsed.</p> <p>hmms.mafft_aligned<br> - contains (compressed) hmm specifications for each major diatom morphotype (radial and polar centrics, araphid and raphid pennates) from the 0.2 alignment column occupancy subset of the data. A summary of the sampling scheme and the hmm results/counts are also available in HMM_sampling.docx.</p> <p>mmetsp_nuclear_transcriptome_assemblies<br> - these are the compressed nuclear transcriptome assemblies that were done in-house (i.e., mostly MMETSP samples). Assemblies do not include organellar or rDNA loci.</p> <p>species_trees<br> - contains (compressed) species trees for all phylogenetic strategies and alignment column occupancy cutoff/data subset strategies.</p> <p>Suppl_1.MMETSP_basic_summaries.xlsx<br> - this an identical file to Parks, Wickett and Alverson 2017 supplementary file 1. This file contains taxon, strain and SRA information for all assembled taxa, and a variety of assembly metric information.</p> <p> </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>
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DANDI Archive for NWB datasets
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OpenNeuro
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