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317 results for “gene structure”
Data from: Life-history characteristics and landscape attributes as drivers of genetic variation, gene flow and fine-scale population structure in Northern Dolly Varden (Salvelinus malma malma) in Canada
The Northern Dolly Varden (Salvelinus malma malma) displays variable life-history types and occupies freshwater habitats with varying levels of connectivity. Here, we assayed microsatellite DNA variation in Northern Dolly Varden from the western Canadian Arctic to resolve landscape and life history variables driving variation in genetic diversity and population structure. Overall, genetic variation was highest in anadromous populations and lowest in those isolated above waterfalls with stream-resident forms intermediate between the two. Anadromous and isolated populations were genetically divergent from each other while no genetic differentiation was detectable between sympatric anadromous and stream-resident forms. Population structure was stable over 25 years, hierarchically organized and conformed to an isolation-by-distance pattern, but stream-isolated forms often deviated from these patterns. Gene flow occurred primarily among Yukon North Slope populations and between sympatric anadromous and resident forms. These results were sex-dependent to some extent, but were influenced more by reproductive status and life history. Our study provides novel insights into the life history, population demographic and habitat variables that shape the distribution of genetic variation and population structure in Arctic fluvial habitats while providing a spatial context for management and conservation.
Data from: The evolution of protein-coding gene structure in eukaryotes
<p>Introns are highly prevalent in most eukaryotic genomes. Despite the accumulating evidence for benefits conferred by the possession of introns, their specific roles and functions, as well as the processes shaping their evolution, are still only partially understood. Here we explore the evolution of the eukaryotic gene intron-exon structure by focusing on several key features such as the intron length, the number of introns, and the intron-to-exon ratio of protein-coding genes. We utilize whole genome data from 590 species covering the main eukaryotic taxonomic groups and analyze them within a statistical phylogenetic framework. We found that the basic gene structure differs markedly among the main eukaryotic phyla, with animals, and particularly chordates, displaying intron-rich genes, compared to plants and fungi. Reconstruction of gene structure evolution suggests that these differences had evolved prior to the divergence of the phyla, and have remained mostly conserved within groups. We revisit the previously reported association between the genome size and the mean intron length, and report that the correlation patterns differ considerably among phyla. Our findings suggest that the evolution of introns may be affected by different processes across the eukaryotic tree. The substantial diversity in gene structures may indicate that introns play different molecular and evolutionary roles in different organisms.</p>
Next-generation phylogeography of the banded newts (Ommatotriton): A phylogenetic hypothesis for three ancient species with geographically restricted interspecific gene flow and deep intraspecific genetic structure
<p class="MsoNoSpacing">Technological developments now make it possible to employ many markers for many individuals in a phylogeographic setting, even for taxa with large and complex genomes such as salamanders. The banded newt (genus <i>Ommatotriton</i>) from the Near East has been proposed to contain three species (<i>O. nesterovi</i>, <i>O. ophryticus </i>and <i>O. vittatus</i>) with unclear phylogenetic relationships, apparently limited interspecific gene flow and deep intraspecific geographic mtDNA structure. We use parallel tagged amplicon sequencing to obtain 177 nuclear DNA markers for 35 banded newts sampled throughout the range. We determine population structure (with Bayesian clustering and principal component analysis), interspecific gene flow (by determining the distribution of species-diagnostic alleles) and phylogenetic relationships (by maximum likelihood inference of concatenated sequence data and based on a summary-coalescent approach). We confirm that the three proposed species are genetically distinct. A sister relationship between <i>O. nesterovi</i> and <i>O. ophryticus</i> is suggested. We find evidence for introgression between <i>O. nesterovi</i> and <i>O. ophryticus</i>, but this is geographically limited. Intraspecific structuring is extensive, with the only recognized banded newt subspecies, <i>O. vittatus cilicensis</i>, representing the most distinct lineage below the species level. While mtDNA mostly mirrors the pattern observed in nuclear DNA, all banded newt species show mito-nuclear discordance as well.</p>
Spruce giga-genomes: structurally similar yet distinctive with differentially expanding gene families and rapidly evolving genes - orthogroups dataset
<p>Orthogroups clustering and analysis of pines and spruces, as reported in Gagalova et al., 2022</p>
Supplementary material 2 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Component data at the four successive thresholds used to illustrate Figure 5: Explanation note: Component data are used to illustrate the structure of the subset of Bactrocera carambolae and Bactrocera dorsalis populations. The highest Betweenness-centrality is highlighted in blue.
Supplementary material 1 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Component data at the five successive thresholds used to illustrate Figure 4: Explanation note: Component data are used to illustrate the structure of the subset of Bactrocera carambolae populations. The Highest Betweenness-centrality is highlighted in blue.
Supplementary material 4 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Comparisons among three different the individual admixture plots: Explanation note: Comparisons among the individual admixture plots of 289 individuals, for K = 3, considering correlated allele frequency, uncorrelated allele frequency, and missing data as recessive homozygotes for the null alleles, respectively.
Supplementary material 3 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Component data at the four successive thresholds used to illustrate Figure 6: Explanation note: Component data are used to illustrate the structure of the subset of the Salaya5 strain and wild populations. The highest Betweenness-centrality is highlighted in blue.
The gene structure annotation, gene function annotation and TE annatition files of the Glyphodes pyloalis's genome
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De novo assemblies for the manuscrip "Candida albicans isolates contain frequent heterozygous structural variants and transposable elements within genes and centromeres"
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Linking satellites to genes with machine learning to estimate phytoplankton community structure from space
<p><strong>General description</strong></p> <p>The datasets presented in this repository have served in the development of a new ocean color algorithm to derive the relative cell abundance of seven phytoplankton groups (output of algorithm #1, called SOMRCA), as well as their contribution to total chlorophyll a (ChlaPG, output of algorithm #2, called SOMChlF) at the global scale using an omic-based marker: psbO. The outputs of the algorithm SOMChlF were compared to the HPLC-based definition of phytoplankton groups.</p> <p>All the details about this study are found in El Hourany, R., Pierella Karlusich, J., Zinger, L., Loisel, H., Levy, M., and Bowler, C.: Linking satellites to genes with machine learning to estimate phytoplankton community structure from space, Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024, 2024.</p> <p>In "Tara_Oceans_psbO_dataset_Final.xlsx", it can be found the Tara Oceans' psbO metagenomic counts converted into relative cell abundance and Chlorophyll-a contribution for seven phytoplankton groups alongside satellite matchups. This dataset was used for algorithm development. In "Assets HPLC_SOMChlF.xlsx", the HPLC database was used as a comparison with Satellite-derived ChlaPG. Each data document presents a description sheet.</p> <p>In the following, the datasets used in this study are described.</p> <p><strong>Tara Oceans psbO metagenomic abundances</strong><br>The psbO gene is a single-copy gene in most eukaryotes and prokaryotes. We used psbO reads from the metagenomes generated by the Tara Oceans expedition as a proxy for phytoplankton relative cell abundance (see more details in Pierella Karlusich et al., 2023 Mol Ecol Res; https://doi.org/10.1111/1755-0998.13592).</p> <p>Among the 210 Tara Oceans stations, 145 stations sampled metagenomes in different ocean regimes from oligotrophic to eutrophic waters (Chl a from 0.01 to 10 mg m−3, median at 0.3 mg m−3) from 2009 to 2013. Seawater samples were filtered to differentiate five planktonic size fractions (0.22–3, 0.8–5, 5–20, 20–180, 180–2000 µm). <br>We retrieved the psbO read abundances from each Tara Oceans size-fractionated seawater sample from the Supplementary Material from Pierella Karlusich et al., 2023 Mol Ecol Res (https://www.ebi.ac.uk/biostudies/files/S-BSST761/psbO_mapping_against_Tara_Oceans_metagenomes.tsv).</p> <p>We used the psbO data to taxonomically differentiate seven phytoplankton groups: diatoms, dinoflagellates, green algae, haptophytes, pelagophytes, cryptophytes, and prokaryotes (cyanobacteria). The psbO read abundances of these seven groups are expressed as relative phytoplankton cell abundance (%) and their contribution to the Chlorophyll-a (Chla PG, in mg m-3). Phytoplankton that were not assigned to any of these seven groups (unclassified) represented less than 5 % of the total relative cell abundance among all size classes. To obtain a single value of relative cell abundance per station, we pooled the five size fractions into a single aggregated sample. For Chl a content estimation, we used a conversion via size-dependent weights (see formula 1 in El Hourany et al., 2024).</p> <p>There are two levels of information derived from the molecular dataset: relative abundance of psbO reads as a proxy for relative cell abundance and the fraction of Chl a that each group represents. Both types of information have different implications. Chl a is often used as a proxy for biomass, which is a relevant parameter for energy and matter fluxes (e.g., food webs, biogeochemical cycles). At the same time, cell abundance corresponds to species abundance for unicellular organisms, which is an important measure for inferring community assembly processes.<br> <br><strong>Satellite Matchups</strong><br>We used ocean color products from the GlobColour project (R2019, full archive reprocessed, 2020) to retrieve satellite matchups for the psbO-derived abundances. These products were constructed by merging data from various satellite sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Medium Resolution Imaging Spectrometer (MERIS), and Ocean and Land Colour Instrument (OLCI).</p> <p>We used 16 GlobColour products as inputs to retrieve the phytoplankton community structure: chlorophyll a concentration (Chl a, product name: CHL1-AVW), remote sensing reflectances (Rrs) at 11 wavelengths (412, 443, 469, 490, 510, 531, 547, 555, 620, 645, and 670 nm), light attenuation coefficient at 490 nm (Kd490), photosynthetically available radiation (PAR), normalized fluorescence light height (NFLH), and particulate backscattering at 443 nm (bbp). These products have daily and 4 km spatiotemporal resolution. In addition, we used the Climate Change Initiative (CCI) sea surface temperature (SST) product at 4 km resolution and daily frequency distributed by the Copernicus Marine Services (CMEMS) portal.</p> <p><strong>HPLC datasets</strong><br>To compare satellite-derived phytoplankton group Chla fractions' distribution (outputs of the algorithm named SOMChlF) with more conventional DPA-based products, we compiled a global HPLC dataset regrouping 12 000 HPLC observations from several HPLC datasets between 1997 and 2014. This HPLC dataset was collocated with the SOMChlF-based ChlaPG. This dataset depicts the abundance of the pigments most widely used to identify major phytoplankton groups: fucoxanthin (Fuco), peridinin (Perid), alloxanthin (Allo), zeaxanthin (Zea), chlorophyll b (Chl b), 19-hexanoyloxyfucoxanthin (19HF), and 19-butanoyloxyfucoxanthin (19BF).</p> <p>Diagnostic pigments were used to estimate the Chl a fraction for each phytoplankton group, namely diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelgophytes, and prokaryotes. The Chl a fraction per group is expressed by</p> <p>HPLC-based ChlaPG = Chla in-situ · DP · α / Sum (DP · α) where "α" is a coefficient associated with a diagnostic pigment (DP) for a specific PG.</p> <p>All the details are found in El Hourany, R., Pierella Karlusich, J., Zinger, L., Loisel, H., Levy, M., and Bowler, C.: Linking satellites to genes with machine learning to estimate phytoplankton community structure from space, Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024, 2024.</p> <p> </p>
Fig. 1 Mitochondrial genome structure and genes variability. a in Historical biogeography and mitogenomics of two endemic Mediterranean gorgonians (Holaxonia, Plexauridae)
Fig. 1 Mitochondrial genome structure and genes variability. a Mitogenomes of Paramuricea clavata and Paramuricea macrospina with genome size and gene annotation. GC-content and AT-content are shown in blue and green on the inner and outer surface of the ring, respectively. b Sliding window analysis of the complete mitochondrial genomes of P. clavata and P. macrospina. The black line indicates
Figure 2. Proposed general secondary structure model for the ITS1–5.8S rDNA–ITS2 in A revised taxonomy and phylogeny of opalinids (Stramenopiles: Opalinata) inferred from the analysis of complete nuclear ribosomal DNA genes
Figure 2. Proposed general secondary structure model for the ITS1–5.8S rDNA–ITS2–LSU rDNA of Opalinida* The expansion segments (ES#L) containing helices (in red) where there are important differences between genera are annotated. Colour code: yellow* ITS1 region; blue* 5.8S rRNA; magenta* ITS2 region; grey* LSU rRNA.
The gene structure annotation, gene function annotation and TE annatition files for the Cibotium barometz isolate CiBa-2024 genome
<p>This dataset comprises comprehensive annotation files for the genome of Cibotium barometz (Golden Chicken Fern), isolate CiBa-2024. It includes gene structure predictions, functional annotations, and transposable element (TE) identifications, complementing the chromosome-level genome assembly. The gene structure annotation provides detailed information on predicted gene models, including exon-intron boundaries and coding sequences. Functional annotations offer insights into the potential roles of identified genes, including Gene Ontology (GO) terms, protein domains, and pathway associations. The TE annotation file details the classification and distribution of transposable elements within the genome. These annotations were generated using state-of-the-art bioinformatics tools and databases, offering a valuable resource for researchers studying fern genomics, plant evolution, and the genetic basis of C. barometz's unique biological features, including its medicinal properties. This dataset aims to facilitate further research in comparative genomics, functional studies, and the exploration of fern biology and evolution.</p>
Figure 1 in Complete mitochondrial genome of the terrestrial isopod Cubaris murina Brandt, 1833: new family gene order and novel tRNA secondary structures
Figure 1. Mitochondrial genome synteny in Cubaris murina and closely related species. A dash (-) before the gene name means that the gene is encoded on the light strand. NCR means a non-coding region that is longer than 100 bp. Cubaris murina is marked in bold black and shades of grey.
Figure 2 in Complete mitochondrial genome of the terrestrial isopod Cubaris murina Brandt, 1833: new family gene order and novel tRNA secondary structures
Figure 2. Secondary structure of each transfer RNA (tRNA) visualised in Forna (http://rna.tbi.univie.ac. at/forna).
Table 3 in Complete mitochondrial genome of the terrestrial isopod Cubaris murina Brandt, 1833: new family gene order and novel tRNA secondary structures
<p><b>Table 3.</b> Characteristic (AT content, repeat, number of predicted secondary structure, range of <i>ΔG</i> value (kcal/mol)) of control region of <i>Cubaris murina</i> by RNAstructure.</p><table><tbody><tr><th></th><th></th><th></th><th></th><th>Length</th><th></th><th></th><th></th><th>Number of predicted</th><th></th></tr></tbody><tbody><tr><th>Species [reference]</th><td>Name</td><td>Start</td><td>Stop</td><td>(bp)</td><td>Location</td><td>%AT</td><td>Repeat</td><td>secondary structures</td><td><i>ΔG</i> value (kcal/mol)</td></tr><tr><th><i>Cubaris murina</i></th><td>NCR1</td><td>5219</td><td>5360</td><td>142</td><td>Between <i>nad1</i> and <i>trnN</i></td><td>52.80%</td><td></td><td>7</td><td>−16.9 to −15.4</td></tr><tr><th>[present study]</th><td>NCR2</td><td>6297</td><td>6666</td><td>370</td><td>Between <i>trnS1</i> and <i>trnL1</i></td><td>59.70%</td><td>CT-rich & AT-loop</td><td>20</td><td>−103.7 to −101.0</td></tr><tr><th></th><td>NCR3</td><td>12,550</td><td>12,753</td><td>204</td><td>Between <i>rrnL</i> and <i>trnE</i></td><td>71.10%</td><td>poly-A</td><td>7</td><td>−17.5 to −17.1</td></tr><tr><th></th><td>NCR4</td><td>12,813</td><td>12,950</td><td>138</td><td>Between <i>trnE</i> and <i>trnV</i></td><td>71.70%</td><td>AG-rich</td><td>5</td><td>−13.4 to −12.3</td></tr><tr><th><i>Panulirus argus</i> [Baeza, 2018]</th><td>NCR</td><td>13,525</td><td>14,326</td><td>801</td><td>Between <i>rrnS</i> and <i>trnI</i></td><td>69.60%</td><td>AT-rich</td><td>7</td><td>−99.20 to −94.52</td></tr><tr><th><i>Synalpheus microneptunus</i> [Chak <i>et al.</i>, 2020]</th><td>NCR</td><td>13,365</td><td>14,198</td><td>834</td><td>Between <i>rrnS</i> and <i>trnI</i></td><td>79.50%</td><td>AT-rich</td><td>20</td><td>− 104 (lowest)</td></tr></tbody></table>
Data from: Scale-dependent effects of landscape variables on gene flow and population structure in bats
Aim: A common pattern in biogeography is the scale-dependent effect of environmental variables on the spatial distribution of species. We tested the role of climatic and land cover variables in structuring the distribution of genetic variation in the grey long-eared bat, Plecotus austriacus, across spatial scales. Although landscape genetics has been widely used to describe spatial patterns of gene flow in a variety of taxa, volant animals have generally been neglected because of their perceived high dispersal potential.Location: England and Europe. Methods: We used a multiscale integrated approach, combining population genetics with species distribution modelling and geographical information under a causal modelling framework, to identify landscape barriers to gene flow and their effect on population structure and conservation status. Genotyping involved 23 polymorphic microsatellites and 259 samples from across the species' range. Results: We identified distinct population structure shaped by geographical barriers and evidence of population fragmentation at the northern edge of the range. Habitat suitability (as captured by species distribution models, SDMs) was the most important landscape variable affecting genetic connectivity at the broad spatial scale, while at the fine scale, lowland unimproved grasslands, the main foraging habitat of P. austriacus, played a pivotal role in promoting genetic connectivity. Main conclusions: The importance of lowland unimproved grasslands in determining the biogeography and genetic connectivity in P. austriacus highlights the importance of their conservation as part of a wider landscape management for fragmented edge populations. This study illustrates the value of using SDMs in landscape genetics and highlights the need for multiscale approaches when studying genetic connectivity in volant animals or taxa with similar dispersal abilities.
Data from: Contrasting patterns of population structure and gene flow facilitate exploration of connectivity in two widely distributed temperate octocorals
Connectivity is an important component of metapopulation dynamics in marine systems and can influence population persistence, migration rates and conservation decisions associated with Marine Protected Areas (MPAs). In this study, we compared the genetic diversity, gene flow and population structure of two octocoral species, Eunicella verrucosa and Alcyonium digitatum, in the northeast Atlantic (ranging from the northwest of Ireland and the southern North Sea, to southern Portugal), using two panels of 13 and 8 microsatellite loci, respectively. Our results identified regional genetic structure in E. verrucosa partitioned between populations from southern Portugal, northwest Ireland and Britain/France; subsequent hierarchical analysis of population structure also indicated reduced gene flow between southwest Britain and northwest France. However, over a similar geographical area, A. digitatum showed little evidence of population structure, suggesting high gene flow and/or a large effective population size; indeed, the only significant genetic differentiation detected in A. digitatum occurred between North Sea samples and those from the English Channel/northeast Atlantic. In both species the vast majority of gene flow originated from sample sites within regions, with populations in southwest Britain being the predominant source of contemporary exogenous genetic variants for the populations studied. Overall, historical patterns of gene flow appeared more complex, though again southwest Britain appeared to be an important source of genetic variation for both species. Our findings have major conservation implications, particularly for E. verrucosa, a protected species in UK waters and listed by the IUCN as 'Vulnerable', and for the designation and management of European MPAs.
Data from: Landscape genomics of Populus trichocarpa: the role of hybridization, limited gene flow and natural selection in shaping patterns of population structure
Populus trichocarpa is an ecologically important tree across western North America. We used a large population sample of 498 accessions over a wide geographical area genotyped with a 34K Populus SNP array to quantify geographical patterns of genetic variation in this species (landscape genomics). We present evidence that three processes contribute to the observed patterns: (1) introgression from the sister species P. balsamifera (2) isolation-by-distance and (3) natural selection. Introgression was detected only at the margins of the species' distribution. Isolation-by-distance was significant across the sampled area as a whole, but no evidence of restricted gene flow was detected in a core of drainages from southern British Columbia. We identified a large number of FST outliers. GO analyses revealed that FST outliers are overrepresented in genes involved in circadian rhythm and response to red/far-red light when the entire dataset is considered, while in southern British Columbia heat response genes are overrepresented. We also identified strong correlations between geoclimate variables and allele frequencies at FST outlier loci that provide clues regarding the selective pressures acting at these loci.
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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.