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410 results for “eukaryotic”
DNA-stimulated liquid-liquid phase separation by eukaryotic topoisomerase II modulates catalytic function
<p>Type II topoisomerases modulate chromosome supercoiling, condensation, and catenation by moving one double-stranded DNA segment through a transient break in a second duplex. How DNA strands are chosen and selectively passed to yield appropriate topological outcomes – e.g., decatenation vs. catenation – is poorly understood. Here we show that at physiological enzyme concentrations, eukaryotic type IIA topoisomerases (topo IIs) readily coalesce into condensed bodies. DNA stimulates condensation and fluidizes these assemblies to impart liquid-like behavior. Condensation induces both budding yeast and human topo IIs to switch from DNA unlinking to active DNA catenation, and depends on an unstructured C-terminal region, the loss of which leads to high levels of knotting and reduced catenation. Our findings establish that local protein concentration and phase separation can regulate how topo II creates or dissolves DNA links, behaviors that can account for the varied roles of the enzyme in supporting transcription, replication, and chromosome compaction.</p>
Raw diffraction images of eukaryotic MATE transporter (AtDTX14)
<p>Multidrug And Toxic compound Extrusion (MATE) transporter exports xenobiotics by using the gradient of H<sup>+</sup>. The crystals were obtained within the lipidic cubic phase.</p> <p>288+85 (Auto+manual) small-wedge (5-20°/crystal) datasets collected from loop-harvested microcrystals using MX225HS CCD detector at a wavelength of 1 Å on BL32XU, SPring-8. The crystals belonged to space group P2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> with unit cell parameters a=52.8, b=86.8, c=116.4 Å.</p> <p>100 datasets were merged at 2.6 Å resolution in the published result (Miyauchi et al. Nature Communications, 2017; PDB code: 5Y50) using KAMO; see processing note https://github.com/keitaroyam/yamtbx/wiki/Processing-AtDTX14-data-(5Y50)</p> <p>Note that most frames have lipid rings.</p>
Elution profiles and protein interaction data accompanying "Ancient eukaryotic protein interactions illuminate modern genetic disorders"
<div> <p> </p> <table> <tbody> <tr> <td> <h2><strong>DESCRIPTION</strong></h2> </td> <td> <h2><strong>FILENAME</strong></h2> </td> <td> <h2><strong>LOCATION</strong></h2> </td> </tr> <tr> <td> <p>LECA 10K OG set</p> </td> <td> <p>leca_ogs_annotated.xlsx</p> </td> <td> <p>Paper, Table S1</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Summary of biological resources</p> </td> <td> <p>resource_summary.xlsx</p> </td> <td> <p>Paper, Table S2</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>LECA interactome (complexes)</p> </td> <td> <p>leca_ppis_fdr10_clustered_annotated.xlsx</p> </td> <td> <p>Paper, Table S3</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - ref proteomes</p> </td> <td> <p>cfms_ref_proteomes.xlsx</p> </td> <td> <p>Paper, Table S4</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>ML - top algorithms</p> </td> <td> <p>tpot_top_algorithms.xlsx</p> </td> <td> <p>Paper, Table S5</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>LECA interactome (pairwise)</p> </td> <td> <p>leca_ppis_fdr10_pairwise.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>UniProt Subcellular Localization IDs</p> </td> <td> <p>uniprot_localization_codes.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Dollo parsimony - ref proteomes</p> </td> <td> <p>dollo_parsimony_ref_proteomes.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Dollo parsimony - input trait matrix</p> </td> <td> <p>dollo_parsimony_count_matrix.tsv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - raw elution profiles</p> </td> <td> <p>amorphea_raw_elution_vectors.csv</p> <p>excavata_raw_elution_vectors.csv</p> <p>tsar_raw_elution_vectors.csv</p> <p>archaeplastida_raw_elution_vectors.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - normalized elution profiles</p> </td> <td> <p>amorphea_norm_elution_vectors.csv</p> <p>excavata_norm_elution_vectors.csv</p> <p>tsar_norm_elution_vectors.csv</p> <p>archaeplastida_norm_elution_vectors.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS/APMS - complete feature matrix</p> </td> <td> <p>feature_matrix.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>ML - top features</p> </td> <td> <p>linearsvc_top_100_features.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>OMIM disease propagation, statistics</p> </td> <td> <p>omim_disease_propagation_stats.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>OMIM disease propagation, top 20 hits per disease</p> </td> <td> <p>omim_disease_propagation_top20hits_per_disease.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Curated OMIM gene-disease relationships for LECA OGs</p> </td> <td> <p>omim_disease_network.tsv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td>Curated OMIM gene-disease relationships for human UniProt IDs</td> <td>omim_disease_groups.csv</td> <td>Zenodo</td> </tr> </tbody> </table> </div> <p> </p>
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.
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>
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>
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. 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The North Pacific Eukaryotic Gene Catalog: KOfam protein function annotations
<p><strong>KEGG functional annotation using KofamScan v1.3.0</strong></p> <p>These tables are larger alternative versions to the KOfam tables included in the North Pacific Eukaryotic Gene Catalog protein data repository here: <a href="../records/12630398">https://zenodo.org/records/12630398</a><br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.<br><br>Clustered protein sequences were annotated against the KEGG collection (release 104.0) of 20,819 protein family Hidden Markov Models (HMMs) using <a href="https://github.com/takaram/kofam_scan" target="_blank" rel="noopener">KofamScan </a>and KofamKOALA. Kofam annotation code is documented in the project github repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.kofamscan_function.sh">NPEGC.kofamscan_function.sh</a></p> <p>Excerpt of core NPEGC_kofam function:</p> <p><code># Define input FASTA</code><br><code>local INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code></p> <p><code># KofamScan call</code><br><code>${KOFAM_DIR}/kofam_scan-1.3.0/exec_annotation -f detail-tsv -E ${EVALUE} -o ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv ${FASTA_DIR}/${INPUT_FASTA}</code></p> <p>Unprocessed annotation results were filtered with a minimum score of 30 to remove low-scoring matches:<br><br><code>zcat NPac.<em><u>NPacID</u></em>.kofam.tsv.gz | awk -F'\t' '{ gsub(/"/, "", $5); $5 = $5 + 0; if ($5 >= 30) print }' | gzip > NPac.<em><u>NPacID</u></em>.UW.bf100.id99.aa.incT30.tsv.gz</code></p>
Sequence-dependent activity and compartmentalization of foreign DNA in a eukaryotic nucleus
<p>In eukaryotes, DNA-associated protein complexes co-evolve with genomic sequence to orchestrate chromatin folding. Here, we investigate the relationship between DNA sequence and the spontaneous loading and activity of chromatin components in the absence of co-evolution. Using bacterial genomes integrated into S. cerevisiae, which diverged from yeast 1.5 billion years ago, we show that nucleosomes, cohesins and the transcriptional machinery can lead to the formation of two different chromatin archetypes, one transcribed and the other silent, independently of heterochromatin formation. These two archetypes also form on eukaryotic exogenous sequences, depend on sequence composition, and can be predicted using neural networks trained on the native genome. They do not mix in the nucleus, leading to a bipartite nuclear compartmentalization, reminiscent of the organization of vertebrate nuclei. </p> <p> </p>
Quantifying the global biodiversity of Proterozoic eukaryotes
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Conflict over the eukaryote root resides in strong outliers, mosaics and missing data sensitivity of site-specific (CAT) mixture models
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Data from: Pleiotropy increases with gene age in six model multicellular eukaryotes
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