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386 results for “transposons”

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zenodo32/100

Atypical epigenetic and small RNA control of degenerated transposons and their fragments in clonally reproducing Spirodela polyrhiza.

The dataset contains all the original raw files for images, including protein and RNA blots, DNA and protein sequences used for phylogenetic trees, do plots…, and any other type of source data, sorted by figure and figure panel. Plasmids generated for this study have been deposited in Addgene. They are listed below together with previously existing plasmids obtained from Addgene and used in this study. NGS data has been deposited on NCBI SRA, accession numbers of datasets used in each figure are listed accordingly in this document. Ready-to-visualize using IGV software files of all NGS datasets together with the S. polyrhiza 9509 gene and TE annotations are also provided.  The content of each file is:   FIGURE 3: -        3A: Picture of Spirodela polyrhiza (used as well in S19A, S26B, D).   FIGURE 5: -        5A: Western blot raw TIFF image files for the detection of H3K9me1, H3K9me2 and H3 in Arabidopsis and Spirodela.   FIGURE 7: -        7A: Western blot and Coomassie raw TIFF image files for the detection of FHA-AtAGO4_gDNA and FHA-SpAGO4a_cDNA in input and IP fractions from transient expression in N. benthamiana. -        7D: Raw scan image files of N. benthamiana leaves infiltrated with RUBY or Scarlet hairpin (hpScarlet) and Northern blots raw TIFF image files for the detection of siRNAs produced by RUBY and hpScarlet transiently expressed in N. benthamiana. -        7E: Raw scan image files of Spirodela cultures in dishes infiltrated with RUBY or Scarlet hairpin (hpScarlet) and Northern blots raw TIFF image files for the detection of siRNAs produced by RUBY and hpScarlet transiently expressed in Spirodela.   SUPPLEMENTAL FIGURE S6: -        Protein sequences, and their alignment, of several angiosperm DRB proteins, including those identified in the S. polyrhiza9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S7: -        Protein sequences, and their alignment, of several angiosperm RDR proteins, including those identified in the S. polyrhiza9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S8: -        Protein sequences, and their alignment, of several angiosperm DCL proteins, including those identified in the S. polyrhiza9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S9: -        Protein sequences, and their alignment, of several angiosperm AGO proteins, including those identified in the S. polyrhiza9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S10: -        DNA sequence of the Spirodela (Sp9509) Chromosome 7 fragment containing the AGO5 cluster.   SUPPLEMENTAL FIGURE S11: -        Protein sequences, and their alignment, of several angiosperm SHH proteins, including those identified in the S. polyrhiza9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S12: -        Protein sequences, and their alignment, of several angiosperm Snf2 remodelers proteins, including those identified in the S. polyrhiza 9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S13: -        Protein sequences, and their alignment, of several angiosperm Class V SET-domain containing proteins, including those identified in the S. polyrhiza 9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S14: -        Protein sequences, and their alignment, of several angiosperm DNA methyltransferase proteins, including those identified in the S. polyrhiza 9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S15: -        Protein sequences, and their alignment, of several angiosperm RNA pol large subunit proteins, including those identified in the S. polyrhiza 9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S16: -        Protein sequences, and their alignment, of several angiosperm SPT5 and SPT5L proteins, including those identified in the S. polyrhiza 9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S17: -        Protein sequences, and their alignment, of several animal and plant Uhrf/VIM proteins, including those identified in the S. polyrhiza 9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S18: -        S18A_B: Protein sequences, and their alignment, of several angiosperm SUVH4 and SUVH5/6 proteins, including those identified in the S. polyrhiza 9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs). -        S18C_D: Protein sequences, and their alignment, of several angiosperm ASI1 proteins, including those identified in the S. polyrhiza 9509 genome, used to build phylogenetic tree in fasta (.fa) format. Machine readable tree file is also provided in Nexus format (.nxs).   SUPPLEMENTAL FIGURE S19: -        Picture of Arabidopsis (used as well in S26 A,C).   SUPPLEMENTAL FIGURE S24: -        S24C: Raw TIFF image files of the coomassie staining of histone acid-extraction protein samples run on SDS-PAGE gel. -        S24D: Excel files with mass-spectrometry data used for quantification of histone modifications in Arabidopsis and Spirodela.   SUPPLEMENTAL FIGURE S27: -        S27A: Raw czi and TIFF image files of Arabidopsis interphase nuclei stained with DAPI. -        S27B: Raw czi and TIFF image files of Spirodela interphase nuclei stained with DAPI.   SUPPLEMENTAL FIGURE S34: -        DNA sequence files (fasta) of TEs used to generate dot plots.   SUPPLEMENTAL FIGURE S35: -        S35A: Western blot and Coomassie raw TIFF image files for the detection of FHA-AtAGO4_gDNA and FHA-SpAGO4a_gDNA in input and IP fractions from transient expression in N. benthamiana. -        S35B: Intron-annotated genomic DNA sequences of AtAGO4 and SpAGO4a in GenBank (.gbk) format. -        S35C: Raw image file of EtBr staining of agarose gel electrophoresis of 5’OH-RACE prior to gel excision and cloning. -        S35D: Western blot and Coomassie raw TIFF image files for the detection of FHA-AtAGO4_gDNA and FHA-SpAGO4a_cDNA in input and IP fractions from transient expression in N. benthamiana.   SUPPLEMENTAL FIGURE S36: -        DNA sequence files (fasta) of TEs used to generate dot plots.   SUPPLEMENTAL FIGURE S38: -        Pictures of Spirodela during pretreatment, manual and vacuum agroinfiltration and RUBY transient expression.   GENOME BROWSER TRACKS: -        The following Integrative Genomics Viewer browser (<a href="https://igv.org/">https://igv.org</a>) tracks are provided: SPIRODELA ·       Spirodela 9509 genome (this study) ·       Spirodela gene annotations (V3.0) ·       Spirodela TE annotations (this study) ·       Spirodela H3K9me1 as log2[H3K9me1/H3] (this study) ·       Spirodela H3K9me2 as log2[H3K9me2/H3] (this study) ·       Spirodela H3K27me3 as log2[H3K27me3/H3] (this study) ·       Spirodela H3K4me3 as log2[H3K4me3/H3] (this study) ·       Spirodela H3K9me1 as log2[H3K9me1/H3] for H3K27me1 (this study) ·       Spirodela H3K9me2 as log2[H3K9me2/H3] ] for H3K27me1 (this study) ·       Spirodela H3K27me3 as log2[H3K27me3/H3] ] for H3K27me1 (this study) ·       Spirodela TraPR purified 21-nt small RNAs (+ strand) (this study) ·       Spirodela TraPR purified 21-nt small RNAs (- strand) (this study) ·       Spirodela TraPR purified 22-nt small RNAs (+ strand) (this study) ·       Spirodela TraPR purified 22-nt small RNAs (- strand) (this study) ·       Spirodela TraPR purified 24-nt small RNAs (+ strand) (this study) ·       Spirodela TraPR purified 24-nt small RNAs (- strand) (this study) ·       Spirodela Illumina RNA seq coverage (this study) ·       Spirodela Illumina RNA seq reads (this study) ·       Spirodela PacBio Iso-seq coverage (this study) ·       Spirodela PacBio Iso-seq reads (this study)   ARABIDOPSIS ·       Arabidopsis Col-0 genome (TAIR10) ·       Arabidopsis gene annotations (TAIR10) ·       Arabidopsis TE annotations (TAIR10) ·       Arabidopsis seedlings H3K9me1 as log2[H3K9me1/H3] (this study) ·       Arabidopsis seedlings H3K9me2 as log2[H3K9me2/H3] (this study) ·       Arabidopsis seedlings H3K27me3 as log2[H3K27me3/H3] (this study) ·       Arabidopsis seedlings H3K4me3 as log2[H3K4me3/H3] (this study) ·       Arabidopsis seedlings TraPR purified 21-nt small RNAs (+ strand) (this study) ·       Arabidopsis seedlings TraPR purified 21-nt small RNAs (- strand) (this study) ·       Arabidopsis seedlings TraPR purified 22-nt small RNAs (+ strand) (this study) ·       Arabidopsis seedlings TraPR purified 22-nt small RNAs (- strand) (this study) ·       Arabidopsis seedlings TraPR purified 24-nt small RNAs (+ strand) (this study) ·       Arabidopsis seedlings TraPR purified 24-nt small RNAs (- strand) (this study)   NGS DATASETS:   All the NGS data generated for this study can be found under the SRA BioProject ID PRJNA1164696.  The data was used to generate the following figure panels: -        Figures: 1A-H, 2A-F, 3A-E, 4A-H, 5D-J, 6A-G, 7B, 7F-H -        Supplemental Figures: S1, S3, S4, S19, S20, S22, S23, S26, S28, S29, S30, S31, S32, S33, S35, S36, S37, S38.   Publicly available sequencing data (from indicated datasets) was used to generate the following figures: -        Figure 2A-F (Arabidopsis gene expression): GSM6892968   MASS SPECTROMETRY DATA:   The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD050443. Data was used to generate: -        Supplemental Figure 24D   PLASMIDS:   The following plasmids generated in this study can be retrieved from Addgene under the following ID#: -        p35S:FHA-AtAGO4_gDNA: #216838 -        p35S:FHA-SpAGO4a_gDNA: #216841 -        p35S::FHA-SpAGO4a_cDNA: #216842   The following plasmids used in this study were retrieved from Addgene under the following ID#: -        p35S:RUBY: #160908 -        pZmUbq:RUBY: #160909 -        p35S:GFP-GUS: #167122   The following plasmids were a gift from Dr. Marco Incarbone (Max Planck Institute of Molecular Plant Physiology, Potsdam Science Park, Potsdam 14476, Germany). -        pAtUBQ:hpScarlet

opencc-by-4.0Nov 2024View details →

zenodo32/100

Transposon DNA sequences facilitate the tissue-specific gene transfer of circulating tumor DNA between human cells

nuc_ctDNA_process ImageJ 1.x macros and Matlab code for processing 3D nuclear classification and quantification. This repo is designed to help you recreate the methods use in the associated publication. Please don't hesitate to contact if you have questions. Happy to debug, update, etc if there's need. Lif files: Use ImageJ 1.x macro in fiji folder to process lif files for subsequent ilastik and Matlab processing. Works with 3 channel data (DAPI, DIC, Rh-Red-X) and 4 channel data (DAPI, Cy5, Rh-Red-X, DIC). Generates .h5 or .tif files for ilastik raining, .jpgs for visualization and ROI overlays, and raw tif files for Matlab analysis. Macro Usage Drag and drop; click Run and select .lif of interest. Only 3D data will be included, single layer images will be noted in output. A table of dimensions and max intensities is also created. Save .csv image info, and .txt output log for reference. Organize Folder Structure Folders:  <ul> <li>Ilastik output</li> <li>Nuc</li> <li>Raw</li> <li>Roi</li> </ul>  ------------ <ul> <li>Place .h5 nuclear, or .tif nuclear and DIC, and .jpg thumbnail data in subfolder called “nuc”</li> <li>Place .tif raw data export into subfolder called “raw”</li> <li>Create subfolders “ilastik output” and “roi”</li> <li>Ilastik (version 1.3.2post1) trained with ~10-20% of datasets </li> <ul> <li>Ilastik side note: currently don't know how to share Ilastik projects without getting errors on loading for the given files and filepaths present during creation. You will need to train your own models. See NoPhotonLeftBehind for Ilastik series that includes training tips and details of features used for these data. <a href="https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ">https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ</a></li> </ul> <li>Models trained as Pixel Classifications – two classes, background and nucleus</li> <li>Ilsatik model trained to classify nuclear vs non nuclear – classical thresholding methods found to be less effective due to varying amounts on cytoplasmic DNA stain present.</li> <li>Single match and mismatch trained using nuclear channel only; double mismatch trained using nuclear and DIC channels together</li> <li>Data separated and models trained for each cell type due to distinct morphologies, e.g. MM1S model, HCT116 model, etc etc</li> <li>Probability density files </li> <ul> <li>Matlab looks for “*_nrmNuc.tiff“ in relative folder “.\ilastik output”, and this is the suffix added in the Fiji macro</li> <li>In ilastik, set output format to multipage tiff, and select path to .{nickname}.tiff. Note, use path of .{nickname}_nrmNuc.tiff if _nrmNuc is not added during your file collation and logistics to this point. Also note .tiff not .tif</li> <li>Leave image export settings as default; shape here is, for example, 16, 512, 512, 1, with axis order zyxc and data type float32</li> <li>In Batch Processing section, select all of the .h5 or .tif files in the “nuc” folder and Process all files</li> </ul> <li>Matlab UI </li> <ul> <li>Files Tab: </li> <ul> <li>Set Root – select folder containing “ilastik output”, “raw”, “roi”, and “nuc”</li> <li>Filename list will propagate, and Overview text at the top will highlight red if the correct number of files are not present in all folders. (TODO: - run test on error scenario to get instructions)</li> <li>Sig Num Chns – the total number of channels in the raw data tif files</li> <li>Rh/Cy5 Sig Chn – the 1 to N based index of the channel to measure inside the nucleus</li> <li>Rh/Cy5 Bkgd – the number of counts considered as background/cell autoflourescene/non-specific signal during measurements; only voxels with counts above this level will be included in the measurements</li> <li>ROI Num Chns – total number of channels in the ilastik probability density tiff files</li> <li>ROI Chn – 1 to N based index of channel to use for generating nuclear 3D ROIs</li> <li>Thumbnails on/off toggle when selecting images in list</li> <li>Currently only single or double channel analyses available (signal is measured inside and outside of nucleus 3D ROI)</li> <li>Click on files to view the nuc jpgs. Click Processing tab to experiment with settings. Note, above channel totals and indices do not currently have error checking. Check correct combinations if you receive tif read errors. Jpgs are loaded on each click, and raw is loaded on switching to Processing tab; expect short delay depending on file size and available disk read speeds.</li> <li>Open in Explorer button – no prizes for guessing that it opens the selected file in explorer. It defaults to the raw data.</li> <li>Process All button runs all the files using the settings in place in the Processing Tab. </li> <ul> <li>A dated folder in roi is created. Inside this folder there are four different types of output file:</li> </ul> </ul> <li>.bin – a binary mask of the 3D ROI</li> <li>_dims.bin – the dimensions of the binary mask</li> <li>.jpg – a thumbnail of ROI overlays</li> <li>.mat – parameters used for generating the ROIs (open .mat files, and click on the params variable in the Import Wizard to quickly view the relevant parameters) </li> <ul> <li>Use Masks dropdown: </li> <ul> <li>For faster re-processing of data with differing minimum number of voxels existing binary masks can be used</li> <li>Note, resulting .mat file in subsequent output will not reflect the parameters used to generate the binary masks – refer to the original folder (this is noted and will be added to newer versions)</li> </ul> <li> </li> </ul> <li>Processing tab: </li> <ul> <li>FFT % is the amount of Fourier space to keep; lower values retain low frequencies only – empirically determined for best resulting nuclear shape</li> <li>FFT Smooth value is Gaussian smoothing value in pixels applied to the ellipsoid mask used to retain the central region of Fourier space. Ringing can be seen for values close to 0, increase as needed.</li> <li>Gauss Smooth is the Gaussian smoothing applied to the raw prob data prior to Otsu thresholding. In noisy classifications thresholding leads to multiple fragmented regions; some smoothing prior to thresholding helps to ‘fuse’ these fragmented regions, prior to 3D FFT spatial filtering to smooth based on size.</li> <li>FFT xz factor is used to avoid smoothing nuclei in the z direction more than x and y. This value affects the ratio of xy and z of the 3D ellipsoid used to mask Fourier space. Set empirically; Click Run and then View Volume to inspect the z ‘stretch’.</li> <li>Button group options to apply different combinations of smoothing and FFT spatial filters: </li> <ul> <li>Gauss – uses Gauss Smooth value above; applied to raw prob data</li> <li>Otsu – Otsu binary threshold</li> <li>Fill – Binary fill applied after smooth and binarization</li> <li>FFT – 3D spatial filtering based on % of Fourier space</li> </ul> <li>Run, well, runs the analysis</li> <li>View Volume displays 3D viewer for resulting data set</li> <li>Min volume slider and value are used to exclude all 3D ROIs smaller than specified value; in voxels. Note slider is linear and plot is log.</li> </ul> <li>Notes: </li> <ul> <li>Requires Matlab 2018a or newer</li> <li>Requires Parallel Computing Toolbox for parfor loop in function ProcessAllButtonPushed. Change parfor to for if not available.</li> <li> </li> </ul> </ul> <li>Matlab filelist: </li> <ul> <li>*.mlapp</li> <li>import_tif.m</li> <li>bw_outline_p.m</li> <li>smth_otsu_fill_p.m</li> <li>LPFFT3D_p.m</li> <li>otsu_bw.m</li> <li>makepsd3.m</li> <li>ellipsoid_mask.m</li> <li>bin_load_mask.m</li> <li>process_ctDNA_table.m</li> <li>_p refers to passed param struct: </li> <ul> <li>wid = 3; % width of dilation in outline overlay</li> <li>pc; % percent of Fourier space to keep - smaller numbers -> more blurred out larger images</li> <li>pad = 1; % pad Fourier space to the next power of 2</li> <li>umpx = 0.09; % image pix size</li> <li>umpz = 0.3; % again in z</li> <li>fft_smth; % smoothing of the eliptical Fourier space mask</li> <li>gauss_smth; % sigma of Guass smooth for Guass, Otsu, Fill, BW</li> <li>scl = [1 1 1/0.3]; % scale ratios for volume viewer</li> <li>fft_xz_factor; % factor to increase or decrease the amount of z FFT smoothing compared to xy</li> <li>minvol = 0;</li> </ul> </ul> </ul>

opencc-by-4.0May 2024View details →

zenodo32/100

Transposon DNA sequences facilitate the tissue-specific horizontal transfer: te expression supplementary datasets

These datasets contain data on analyses of TE expression stability. Raw RNA seq counts were processed using DESeq2 R package. Low count genes and TEs were removed. Counts across samples were normalized for library sizes and log-transformed using 'regularized log' transformation. Batch normalization was performed on log-transformed data with ComBat function from sva R package.  Expression variability (EV) of TEs and genes (probes) was estimated using the previously described method [1, 2]. 1. Bashkeel, N., Perkins, T.J., Kærn, M. et al. Human gene expression variability and its dependence on methylation and aging. BMC Genomics 20, 941 (2019). https://doi.org/10.1186/s12864-019-6308-7 2. Alemu EY, Carl JW Jr, Corrada Bravo H, Hannenhalli S. Determinants of expression variability. Nucleic Acids Res. 2014;42(6):3503-3514. doi:10.1093/nar/gkt1364   PC.zip - the results of TE expression and stability in prostate cancer. <ul> <li>0.PC.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.PC.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.PC.TE.rlogcpm.mad.xls -stability measures according MAD (median absolute deviance) criteria </li> <li>0.PC.TE.rlogcpm.sd.xls - stability measures according SD criteria </li> <li>0.PC_TE_bootstrap.pdf - TE expression stability</li> <li>PC.deseq.logCPM.csv - TE log transformed expression matrix </li> <li>PC.TE_count_table.csv - TE raw count matrix </li> <li>PC_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul>   MM.zip - the results of TE expression and stability in multiple myeloma. <ul> <li>0.MM.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.MM.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.MM_TE_bootstrap.pdf - TE expression stability</li> <li>MM.deseq.logCPM.csv - TE raw count matrix </li> <li>MM.rlog.mad.xlsx- stability measures according MAD (median absolute deviance) criteria</li> <li>MM.rlog.sd.xlsx - stability measures according MAD (median absolute deviance) criteria </li> <li>MM.TE_count_table.csv - TE raw count matrix </li> <li>Myeloma_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul>

opencc-by-4.0Jun 2023View details →

ClinicalTrials.gov32/100

Transposon-manipulated Allogeneic CARCIK-CD19 Cells in Pediatric and Adult Patients With r/r ALL Post HSCT

ClinicalTrials.gov study NCT03389035. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad32/100

Data from: Genome-wide patterns of transposon proliferation in an evolutionary young hybrid fish

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publicNov 2018View details →

dryad32/100

Data from: Longevity and transposon defense, the case of termite reproductives

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publicApr 2019View details →

dryad32/100

Duck pan-genome reveals two transposon-derived structural variations caused bodyweight enlarging and white plumage phenotype formation during evolution

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publicNov 2023View details →

dryad28/100

Data from: Survey sequencing reveals elevated DNA transposon activity, novel elements, and variation in repetitive landscapes among vesper bats

The repetitive landscapes of mammalian genomes typically display high Class I (retrotransposon) transposable element (TE) content, usually around half of the genome. In contrast, the Class II (DNA transposon) contribution is typically small (<3% in model mammals). Most mammalian genomes also exhibit a precipitous decline in Class II activity beginning roughly 40 million years ago (Ma). The first signs of more recently active mammalian Class II TEs were obtained from the little brown bat, Myotis lucifugus and are reflected by higher genome content (~5%). To aid in determining taxonomic limits and potential impacts of this elevated Class II activity, we performed 454 survey sequencing of a second Myotis species as well as four additional taxa within the family Vespertilionidae and an outgroup species from Phyllostomidae. Graph-based clustering methods were used to reconstruct the major repeat families present in each species and novel elements were identified in several taxa. Retrotransposons remained the dominant group with regard to overall genome mass. Elevated Class II TE composition (3-4%) was observed in all five vesper bats while less than 0.5% of the phyllostomid reads were identified as Class II derived. Differences in satellite DNA and Class I TE content are also described among vespertilionid taxa. These analyses present the first cohesive description of TE evolution across closely related mammals, revealing genome-scale differences in TE content within a single family.

opencc-zeroDec 2011View details →

dryad28/100

Data from: Transposon proliferation in an asexual parasitoid

The widespread occurrence of sex is one of the most elusive problems in evolutionary biology. Theory predicts that asexual lineages can be driven to extinction by uncontrolled proliferation of vertically transmitted transposable elements (TEs), which accumulate because of the inefficiency of purifying selection in the absense of sex and recombination. To test this prediction, we compared genome-wide TE load between a sexual lineage of the parasitoid wasp Leptopilina clavipes and a lineage of the same species that is rendered asexual by Wolbachia-induced parthenogenesis. We sequenced the entire genomes of both the sexual and the asexual lineages using next-generation sequencing. We identified transposons of most major classes (including DNA transposons, LTR and LINE-like retrotransposable elements) in both lineages. Quantification of TE abundance using coverage depth showed that copy numbers in the asexual lineage exceeded those in the sexual lineage for DNA transposons, but not LTR and LINE-like elements. However, one or a small number of gypsy-like LTR elements exhibited a four-fold higher coverage in the asexual lineage. Quantitative PCR showed that high loads of this gypsy-like TE were characteristic for 11 genetically distinct asexual wasp lineages when compared to sexual lineages. Bisulfite sequencing revealed no DNA cytosine methylation of the gypsy-like TE in either lineage. We found no evidence for an overall increase in copy number for all TE types in asexuals as predicted by theory. Instead, we suggest that our results are best explained as side-effects of (epi)genetic manipulations of the host genome by Wolbachia. Asexuality is achieved in a myriad of ways in nature, many of which could create similar problems with TE proliferation.

opencc-zeroDec 2011View details →

zenodo28/100

Transposons are a major contributor to gene expression variability under selection in rice populations

Dataset and code related to the publication:  Castanera R., Morales-Díaz N., Gupta S., Purugganan M., Casacuberta JM (2022). Transposons are a major contributor to gene expression variability under selection in rice populations. Research Square. https://doi.org/10.21203/rs.3.rs-2197876/v1.  1) TIP_data folder:  INDICA_teinsertions_5_2_2_matrix_sorted_ID_MAF003.txt: TIP matrix used for TIP-eQTL mapping (binary form, where 0 = absence, 1= presence) INDICA_teinsertions_5_2_2_matrix_sorted_ID_MAF003.info: TIP information (chrom,start,superfamily,family,maf) JAPONICA_teinsertions_5_2_2_matrix_sorted_ID_MAF003.txt: TIP matrix used for TIP-eQTL mapping (binary form, where 0 = absence, 1= presence) JAPONICA_teinsertions_5_2_2_matrix_sorted_ID_MAF003.info:TIP information (chrom,start,superfamily,family,maf) 2) SNP_data folder:  SNP_matrix_final_IRGC_Indica_MAF003.lfmm: SNP binary matrix used for TIP-eQTL mapping  SNP_matrix_final_IRGC_Indica_MAF003.info: SNP information SNP_matrix_final_IRGC_japonica_MAF003.lfmm: SNP binary matrix used for TIP-eQTL mapping  SNP_matrix_final_IRGC_japonica_MAF003.info:SNP information 3) Expression data:  Normalized expression data and accessory files used for TIP-eQTL analysis (as described in TIP-eQTL_matrix.R) Code: PopoolationTE2.sh: Bash script to reproduce PopoolationTE2 analysis (TIP detection) TIP-eQTL_matrix.R: R code to reproduce eQTL analyses

opencc-by-4.0Feb 2023View details →

dryad28/100

Data from: Survey sequencing reveals elevated DNA transposon activity, novel elements, and variation in repetitive landscapes among vesper bats

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publicApr 2012View details →

dryad28/100

Data from: Transposon variants and their effects on gene expression in Arabidopsis

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publicApr 2013View details →

dryad28/100

Data from: Transposon proliferation in an asexual parasitoid

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publicMar 2012View details →

dryad28/100

Data from: The concerted impact of domestication and transposon insertions on methylation patterns between dogs and grey wolves

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publicNov 2015View details →

dryad28/100

High-throughput discovery of phage receptors using transposon insertion sequencing of bacteria

Open the record for dataset details and reuse information.

publicFeb 2021View details →

geo24/100

Transposon-encoded nucleases use guide RNAs to selfishly bias their inheritance

GEO Series GSE223127. Escherichia coli BL21(DE3). 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenJun 2023View details →

geo24/100

Acetyltransferase Enok regulates transposon silencing by promoting transcription at piRNA clusters and genes involved in piRNA biosynthesis [Enok ChIP-seq]

GEO Series GSE105025. Drosophila melanogaster. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenJan 2021View details →

geo24/100

A transposon sensor during epigenetic reprogramming consists of pervasive transcription and endosiRNAs in mouse ES cells [BS-Seq]

GEO Series GSE89697. Mus musculus. 18 samples. Type: Methylation profiling by high throughput sequencing.

openGEO-OpenNov 2017View details →

geo24/100

Requirements for multivalent Yb body assembly in transposon silencing in Drosophila

GEO Series GSE121605. Drosophila melanogaster. 4 samples. Type: Expression profiling by high throughput sequencing; Other.

openGEO-OpenApr 2019View details →

geo24/100

An epigenetic switch ensures transposon repression upon acute loss of DNA methylation in ES cells (WGBS)

GEO Series GSE71592. Mus musculus. 1 samples. Type: Methylation profiling by high throughput sequencing.

openGEO-OpenJan 2016View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record