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410 results for “eukaryotic”
Figure 6a. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 6a. - Eupolybothrus cavernicolus Komerički & Stoev sp. n., male paratype. Figure 6a. tergite 14 and intermediate tergite, posteriodorsal view. Abbreviations: seta-free areas (sfa). Figure 6b. pretarsus of leg 10, ventral view. Abbreviations: anterior accessory claw (a), posterior accessory claw (p). <br> tergite 14 and intermediate tergite, posteriodorsal view. Abbreviations: seta-free areas (sfa).
Figure 10b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 10b. - Eupolybothrus cavernicolus Komerički & Stoev sp. n., male paratype. Figure 10a. close up of the tip of prefemoral spine p Figure 10b. coxal pore pit, meso-ventral view <br> coxal pore pit, meso-ventral view
Figure 16. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 16. - Eupolybothrus leostygis (Verhoeff, 1899), male: prefemur 15 showing the bare knob, dorsal view.
Figure 15b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 15b. - Eupolybothrus leostygis (Verhoeff, 1899), male. Figure 15a. tergite 14 and intermediate tergite, dorsal view Figure 15b. close up of posterior part of prefemur of leg 14 showing the expanded distal part bearing feebly defined setose protuberance <br> close up of posterior part of prefemur of leg 14 showing the expanded distal part bearing feebly defined setose protuberance
Figure 14a. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 14a. - Eupolybothrus leostygis (Verhoeff, 1899), male. Figure 14a. ocelli Figure 14b. forcipules, ventral view <br> ocelli
Figure 20c. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 20c. - Gene annotation. Original data available from GigaScience GigaDB (Stoev et al. 2013). Figure 20a. E-value, identity and species distribution statistics of the sequences that can find homologs on Nr database Figure 20b. COG functional classification of the transcripts Figure 20c. GO categories of the transcripts <br> GO categories of the transcripts
Figure 8b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 8b. - Eupolybothrus cavernicolus Komerički & Stoev sp. n., male paratype. Figure 8a. prefemur 15, mesoventral view. Abbreviations: prefemoral knob (pk), circular setose protuberance (cp), cluster of setae (sc). Figure 8b. close up of the prefemoral knob, ventral view <br> close up of the prefemoral knob, ventral view
Figure 18b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 18b. - Prefemur of male leg 15. From Stoev et al. (2010). Figure 18a. Eupolybothrus caesar Figure 18b. Eupolybothrus spiniger <br> Eupolybothrus spiniger
Figure 15a. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 15a. - Eupolybothrus leostygis (Verhoeff, 1899), male. Figure 15a. tergite 14 and intermediate tergite, dorsal view Figure 15b. close up of posterior part of prefemur of leg 14 showing the expanded distal part bearing feebly defined setose protuberance <br> tergite 14 and intermediate tergite, dorsal view
Figure 13. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 13. - Entrance of cave Miljacka II, type locality of Eupolybothrus cavernicolus Komerički & Stoev sp. n.
Figure 18a. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 18a. - Prefemur of male leg 15. From Stoev et al. (2010). Figure 18a. Eupolybothrus caesar Figure 18b. Eupolybothrus spiniger <br> Eupolybothrus caesar
Figure 19. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 19. - Delineation of Eupolybothrus species – Neighbor joining tree K2P distances. Visualised are the clusters obtained from the reversed Statistical Parsimony (SP) method and the Automatic Barcoding Gap Discovery (ABGD) procedure. Bootstrap support for the identified lineages are given above. The intraspecific genetic variability is given for each cluster. Source data is available in Suppl. material 1.
Figure 20b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 20b. - Gene annotation. Original data available from GigaScience GigaDB (Stoev et al. 2013). Figure 20a. E-value, identity and species distribution statistics of the sequences that can find homologs on Nr database Figure 20b. COG functional classification of the transcripts Figure 20c. GO categories of the transcripts <br> COG functional classification of the transcripts
Figure 17b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 17b. - Prefemur of male leg 15. From Stoev et al. (2010). Figure 17a. Eupolybothrus tabularum Figure 17b. Eupolybothrus excellens <br> Eupolybothrus excellens
Supplementary data and scripts for the publication "Discovery of numerous novel Helitron-like elements in eukaryote genomes using HELIANO"
<p>##Information of directories. Each directory contains necessary data and scripts to do the corresponding analysis</p> <p>1. Benchmarking/: benchmarking analysis for heliano. (For Figure 3, Supplementary Table S2)<br>2. GeneCapture/: analysis for captured genes within HLEs. (For Figure 6, Supplementary Table S11)<br>3. HLE_model_species/: analysis for heliano results on three model species. (For Table 1, Figure 4, Supplementary Table S5-9, Supplementary Figure 4)<br>4. HLE_sampled_species/: analysis for heliano results on 404 sampled species. (For Figure 5, Supplementary Table S10, Supplementary Figure 5)<br>5. hmm_model/: build hmm model for Hel and Rep domains. (For RepHel.hmm that is important to detect and classify HLEs)<br>6. Phylogenetical_tree/: making phylogenetical trees. (For Figure 1, Supplementary Figure 2-3)<br>7. Sample_species/: about how to sample species from various genome assemblies. (Supplementary Table S3)<br>8. HLE_feature/: information about the position of RepHel domain and the length of HLE sequences. (Supplementary Figure 1)</p>
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.
Conflict over the eukaryote root resides in strong outliers, mosaics and missing data sensitivity of site-specific (CAT) mixture models
Abstract Phylogenetic reconstruction using concatenated loci ("phylogenomics" or "supermatrix phylogeny") is a powerful tool for solving evolutionary splits that are poorly resolved in single gene/protein trees (SGTs). However, recent phylogenomic attempts to resolve the eukaryote root have yielded conflicting results, along with claims of various artefacts hidden in the data. We have investigated these conflicts using two new methods for assessing phylogenetic conflict. ConJak uses whole marker (gene or protein) jackknifing to assess deviation from a central mean for each individual sequence, while ConWin uses a sliding window to screen for incongruent protein fragments (mosaics). Both methods allow selective masking of individual sequences or sequence fragments in order to minimize missing data, an important consideration for resolving deep splits with limited data. Analyses focused on a set of 76 eukaryotic proteins of bacterial-ancestry previously used in various combinations to assess the branching order among the three major divisions of eukaryotes: Amorphea (mainly animals, fungi and Amoebozoa), Diaphoretickes (most other well-known eukaryotes and nearly all algae) and Excavata, represented here by Discoba (Jakobida, Heterolobosea, and Euglenozoa). ConJak analyses found strong outliers to be concentrated in under-sampled lineages, while ConWin analyses of Discoba, the most under-sampled of the major lineages, detected potentially incongruent fragments scattered throughout. Phylogenetic analyses of the full data using an LG-gamma model support a Discoba sister scenario (neozoan-excavate root), which rises to 99-100% bootstrap support with data masked according to either protocol. However, analyses with two site-specific (CAT) mixture models yielded widely inconsistent results and a striking sensitivity to missing data. The neozoan-excavate root places Amorphea and Diaphoretickes as more closely related to each other than either is to Discoba, a fundamental relationship that should remain unaffected by additional taxa.
Phylogenomic Analyses of 2,786 Genes in 158 Lineages Support a Root of The Eukaryotic Tree of Life Between Opisthokonts and All Other Lineages
<p><strong>Abstract</strong></p> <p>Advances in phylogenetic methods and high-throughput sequencing have allowed the reconstruction of deep phylogenetic relationships in the evolutionary history of eukaryotes. Yet, the root of the eukaryotic tree of life remains elusive. The most ‘popular’ (i.e. in textbooks and reviews) hypothesis for the root is between Unikonta (Opisthokonta + Amoebozoa) and Bikonta (all other eukaryotes), which emerged from analyses of a single gene fusion and a limited sampling of eukaryotic lineages. Subsequent highly-cited studies based on concatenation of genes supported this hypothesis with some variations or proposed a root within the Excavata. However, concatenation of genes neither considers phylogenetically-informative events (i.e. gene duplications and losses), nor provides an estimate of the root. A more recent study using gene tree-species tree reconciliation methods suggested the root lies between Opisthokonta and all other eukaryotes, but only including 59 taxa and 20 genes. Here we apply a gene tree – species tree reconciliation approach to a gene-rich and taxon-rich dataset (i.e. 2,786 gene families from two sets of ~158 diverse eukaryotic lineages) to assess the root, and we iterate each analysis 100 times to quantify tree space uncertainty. Our results estimate a root between Fungi and all other eukaryotes, or between Opisthokonta and all other eukaryotes, and reject alternative popular roots from the literature. Based on further analysis of genome size we propose Opisthokonta + others as the most likely root. Finding the root of the eukaryotic tree of life is critical for the field of comparative biology as it allows us to understand the timing and mode of evolution of characters across the evolutionary history of eukaryotes.</p> <p>Methods<br> Here we provide the alignments, gene trees, inputs, and outputs from our project entitled "Phylogenomic Analyses of 2,786 Genes in 158 Lineages Support a Root of The Eukaryotic Tree of Life Between Opisthokonts and All Other Lineages". Sequences and alignments were produced using the phylogenomic pipeline PhyloToL, which contains a taxon- and gene-rich database (including eukaryotes, archaea, and bacteria). These data were then used for 1) assessing the root of the eukaryotes and 2) for comparison with other previously published hypotheses. In both cases, we used the species tree - gene tree reconciliation tool iGTP. We also did a comparison of hypotheses using the likelihood-based tool SpeciesRax</p> <p><strong>iGTP</strong></p> <p>Input data</p> <p>These data are divided into four datasets based on taxa selection. For dataset SEL+, taxa were selected based on their taxonomy; for RAN+, taxa were selected randomly among the major eukaryotic clades Opisthokonta, Amoebozoa, Archaeplastida, Excavata, SAR, and some orphan lineages. Datasets SEL- and RAN- are the same as SEL+ and RAN+, but exclude microsporidians in order to account for and avoid long branch attraction due to microsporidians fast-evolutionary rates. We chose the gene families that contain at least 25 taxa representing at least four of the five major eukaryotic clades. Additionally, at least 2 of the major clades had to contain at least 2 minor clades (e.g. Glaucophytes and Rhodophyta are minor clades in the major clade Archaeplastida). In a pilot analysis, we produced an alignment and a phylogenetic tree for each gene family using the default settings of a previous version of PhyloToL (GUIDANCE V1.3.1 sequence cutoff = 0.3 and column cutoff = 0.4; RAxML quick tree with model PROTGAMMALG and no bootstraps). Then, we kept the gene families that are exclusive of eukaryotes or the ones in which eukaryotes were monophyletic. From a total of 3,002 gene families that met our criteria, 2786 passed the initial steps of PhyloToL when including only the data from the dataset SEL+. These 2,786 gene families were used for further analyses with all datasets.</p> <p>MSAs were produced with PhyloToL (GUIDANCE V2.02 sequence cutoff = 0.3, column cutoff = 0.4, number of iterations = 5; Sela, et al. 2015). The default parameters of PhyloToL include up to five iterations of GUIDANCE V2.02 with 10 bootstraps and MAFFT V7 with algorithm E-INS-i for less than 200 sequences or “auto” option if more than 200 sequences, and maxiterate = 1000. Instead, here we run up to five iterations of GUIDANCE with 20 bootstraps and the simple MAFFT algorithm FFT-NS-2. Then, we perform an additional GUIDANCE run with 100 bootstraps and the default MAFFT parameters for PhyloToL.</p> <p>Gene trees were inferred with RAxML v.8.2.4 with 10 ML searches for best-ML tree (option "-# 10"), using the rapid hill-climbing algorithm (option "-f d") and no bootstrap replicates. The protein evolution model used was evaluated during the gene tree inference (option "-m PROTCATAUTO") by testing all models available in RAxML (e.g. JTT, LG, WAG, etc) with optimization of substitution rates and of site-specific evolutionary rates which were categorized into four distinct rate categories for greater computational efficiency.</p> <p>We ran 100 repetitions of iGTP analyses per dataset. But, given the complexity of the datasets and the heuristic nature of some key steps of the iGTP algorithm (e.g. gene tree rooting and initial starting species tree generation), in a preliminary analysis, we faced two systematic challenges with iGTP as the inferred species tree was affected by: 1) the order of the leaves in the input unrooted gene tree Newick strings (i.e. the input trees were treated as rooted even though we specified that they were not); and 2) the input gene order in the 100 replicates. Therefore, we randomly shuffled the order of the leaves in the unrooted gene trees (keeping the same topology), and randomly shuffled the order of the input gene trees in each of the 100 replicates per dataset. Here we provide the 100 input files generated for those iGTP analyses. </p> <p>Output data</p> <p>Here we also share the data generated after two analyses: 1) root assessment and 2) hypothesis testing. For the former, we allowed iGTP to calculate the more parsimonious root given our input files. For the latter, we allowed iGTP to calculate the reconciliation cost of the gene trees given the input files and constraints in the species trees to reflect previously published root hypotheses. The constraints are explained in the README file.</p> <p>SpeciesRax</p> <p>Since we removed LGT and contamination from our dataset using a series of filters, we applied the model UndatedDL instead of UndatedDTL, which implies that we only took into consideration duplications and losses and ignored the transferences. Then, the command used for SpeciesRax was...</p> <p>./generax --families forGeneRax/famFile --species-tree forGeneRax/spsTreeAn.newick --strategy SKIP --rec-model UndatedDL --per-family-rates --prefix An_DS1 --si-strategy EVAL </p> <p>input </p> <p>Here, we are sharing all the necessary files to run SpeciesRax, including the mapping file (famFile), the species trees (spsTrees; the best iGTP constrained species trees per hypothesis), and their underlying gene trees (trees_r)</p> <p>output</p> <p>Output folder from SpeciesRax, which includes log files, events (duplications, losses) counts, and statistics (i.e., reconciliation likelihood values)</p> <p>Note: <br> * As in the iGTP analyses, for the SpeciesRax files, the words Op, Fu, Di, Un, An, refer to the five root hypotheses compared: Opisthokonta-others, Fungi-others, Discoba-others, Unikonta-Bikonta, and (Ancyromonadida + Metamonada)-others, respectively. <br> * For the SpeciesRax analyses, the Un word refers to Ut (Cavalier-Smith 2003) <br> </p>
Training data for 'Refining Manual Genome Annotations with Apollo (eukaryotes)' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for manual curation of eukaryotic genome annotation using Apollo.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.