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opencc-by-nc-nd-4.0Apr 2024View details →

Draft genome sequences of Arabidopsis thaliana-associated micro-organisms from Reijerscamp soil, the Netherlands

Methodological summary and relevant references Compressed tar archive containing 447 draft bacterial genomes and their annotations used in several studies including Fourie et al. (2024; in review) and Selten et al. (2024; in prep). Genome sequences are obtained by Illumina-only sequencing of microbial cultures. Illumina reads were demultiplexed and cleaned with cutadapt (version 2.8) (Martin, 2011) and assembled into genomes using A5 (A5-miseq version 20160825) (Coil et al., 2014). Genome contamination and heterogeneity was checked with CheckM (version 1.1.3) (Parks et al., 2015) and any genomes with multiple single copy gene occurrences were subjected to MaxBin (version 2.2.7) (Wu et al., 2014) to separate the genomes from contaminated bacterial cultures. Any non-bacterial contigs in the genome assemblies were removed using MMSeqs2 (version 13.45111) (Steineigger & Schöding, 2017). Open reading frames were found and annotated by PROKKA (version 1.14.6) (Seemann, 2014) and EggNOG (version 2.1.4-2) (Cantalapiedra et al., 2021) respectively. Microbial cultures were derived from Arabidopsis thaliana roots grown in Reijerscamp soil, described in Stringlis et al., 2018 https://doi.org/10.1073/pnas.1722335115. The uploaded files are <ol> <li>Genome assemblies</li> <li>Prokka gene predictions in GFF3 format</li> <li>Predicted transcripts from genes in (2)</li> <li>Predicted proteins from genes in (2), and</li> <li>EggNOG annotations for the proteins in (4)</li> </ol> Genomes and annotations pending upload om NCBI GenBank (April 2024)

The composite metamorphic sequence in the Jiajika gneiss dome, Songpan-Ganze orogenic belt, eastern Tibet: P–T–D–t evolution and implications for lithium mineralization

This is the dataset for "The composite metamorphic sequence in the Jiajika gneiss dome, Songpan-Ganze orogenic belt, eastern Tibet: P–T–D–t evolution and implications for lithium mineralization". The dataset includes EMPA, whole-rock major elements, and geochronological data.

opencc-by-4.0Apr 2023View details →

Gene annotation of complete genome sequence of Achromobacter sp. strain E1

Open the record for dataset details and reuse information.

dryad32/100

Monotropoid Ericaceae 102-locus nuclear sequences matrices and plastid locus sequence matrix

Monotropoideae (Ericaceae) is a wholly leafless and holomycotrophic group of primarily temperate herbs with centers of diversity in western North America and east Asia. The eleven genera are structurally diverse and also vegetatively reduced, making relationships difficult to assess based on morphology. Previous molecular analyses have focused primarily on segments of the ribosomal RNA repeat and yielded sometimes conflicting topologies. We employed a genomic sampling approach to obtain 102 nuclear loci and plastid coding loci for nine of the genera, as well as sampling ITS-26S and plastid rps2 for a broader set of accessions via PCR and Sanger sequencing Data filtering for character completeness had a clear effect on relationships and branch support. Nuclear and plastid loci agree on a topology that resolves Allotropa and Hemitomes as sisters and Monotropsis sister to Eremotropa+Monotropa+Monotropastrum, relationships that were unclear from previous analyses. Hypopitys should be recognized as distinct from Monotropa, with previous ambiguity in rps2 relationships here explained by the existence of plastid and mitochondrial paralogs. Comparison of branch lengths between nuclear and plastid loci reveals increased rates of change in plastid sequences in most cases, especially in Monotropsis and Monotropa, but the reverse situation in Pityopus, with nuclear branches being unusually long, possibly indicating cryptic diversity. Parallel shifts in plant form to a ground-level floral display correlate with shifts from capsular to berry fruits and reduction in species ranges. The optimal tree from molecular data requires significant parallelism and/or reversal in morphological features due to the placement of Allotropa and Hemitomes.

opencc-zeroApr 2024View details →

The single cell RNA sequencing of Bullous Pemphigoid

The Sparse count tables in 10XGenomics format (barcodes.tsv.gz features.tsv.gz matrix.mtx.gz) of 33 samples as detailed  in Nat Commun 15, 5949 (2024).   Including single-cell RNA sequencing (scRNA-seq) from five lesions of  Bullous Pemphigoid (BP) patients and eight normal skin of healthy donors, eight PBMC of BP patients and eight PBMC of healthy donors, four blister of BP patients. If you utilize this dataset in your research, kindly cite our article:  Liu, T., Wang, Z., Xue, X. et al. Single-cell transcriptomics analysis of bullous pemphigoid unveils immune-stromal crosstalk in type 2 inflammatory disease. Nat Commun 15, 5949 (2024). https://doi.org/10.1038/s41467-024-50283-3.

Focal mechanisms for the 2016 Gyeongju earthquake sequence

Focal mechanism solutions for the 138 earthquakes that occurred in Gyeongju, South Korea from 12 Sepetember to 15 December 2016

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>

TCRb sequencing of CD4+ and CD8+ T cells from hematological patients (part 2)

This dataset contains TCRb sequencing of 177 samples from patients with aplastic anemia, myelodysplastic syndrome, immune thrombocytopenia, immunodeficiency or graft-versus-host disease and healthy controls. Samples are separated CD4+ or CD8+ cells from peripheral blood or bone marrow. The data has been produced with immunoSEQ platform (Adaptive Biotechnologies). The details regarding sample processing, sequencing and metadata can be found from the publication Somatic mutations associate with clonal expansion of CD8+ T cells (Lundgren et al, Science Advances, in press). The dataset is divided in 2 parts containing 91 (part 1) and 86 (part 2) files. Data is in immunoSEQ format (v2).

Transposase-Assisted Tagmentation: An Economical and Scalable Strategy for Single-Worm Whole-Genome Sequencing (elegans)

AlphaMissense identifies 23 million human missense variants as likely pathogenic, but only 0.1% have been clinically classified. To experimentally validate these predictions, chemical mutagenesis presents a rapid, cost-effective method to produce billions of mutations in model organisms. However, the prohibitive costs and limitations in the throughput of whole-genome sequencing (WGS) technologies, crucial for variant identification, constrain its widespread application. Here, we introduce a Tn5 transposase-assisted tagmentation technique for conducting WGS in C. elegans, E. coli, S. cervisiae, and C. reinhardtii. This method, demands merely 20 minutes of hands-on time for a single worm or single-cell clones and incurs a cost below 10 US dollars. It effectively pinpoints causal mutations in mutants defective in cilia or neurotransmitter secretion and in mutants synthetically sterile with a variant analogous to the oncogenic BRAF(V600E) mutation. Integrated with chemical mutagenesis, our approach can generate and identify missense variants economically and efficiently, facilitating experimental investigations of missense variants in diverse species.

A tufA reference sequence library for the green algal genus Caulerpa (Bryopsidales)

A 1230 nucleotide tufA gene sequence alignment (in fasta format) of 89 Caulerpa species and nine 'Pseudochlorodesmis' species, a Maximum Likelihood phylogeny (pdf file) based on this alignment, and an Excel file with listing details about all taxa included. These were first published in Draisma & Sauvage (2024): Jeju Journal of Islands Sciences 1: 1-11. https://www.jjis.or.kr/articles/xml/VxXM/

Protein sequences for Dephosphorylation sites and fine-tuning notebook

Protein sequences for Dephosphorylation sites

Tree Sequence and Genealogical Forest Files for a Simulated Human Chromosome 20

Dataset containing 640000 samples simulated using <a href="https://github.com/popsim-consortium/stdpopsim">stdpopsim</a> 0.2.0 and the <code>HapMapII_GRCh38</code> genetic map. The tree sequence was converted to a genealogical forest files via <a href="https://github.com/lukashuebner/gfkit">gfkit</a> version <code>fbd2740</code>.

Figure 6 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences

Figure 6. Chronogram of Fulgoroidea estimated using the Bayesian phylogenetic by MCMCTREE in paml. Time units are in millions of years. Estimated divergence times are shown near nodes.

Figure 5 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences

Figure 5. BI analysis (PhyloBayes) based on PCG12R–ATP8. Numerals at nodes are Bayesian posterior probabilities (PP). '–' indicates different clades.

Figure 1 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences

Figure 1. Sliding window analysis. The red curve shows the value of nucleotide diversity (Pi). Pi value of each PCG is shown below the arrows.

Figure 4 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences

Figure 4. BI analysis (MrBayes) based on PCG12R–ATP8. Numerals at nodes are Bayesian posterior probabilities (PP). '–' indicates different clades.

Figure 2 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences

Figure 2. Genetic distance (on average) and ratio of non-synonymous (Ka) to synonymous (Ks) substitution rates.

Figure 3 in A phylogeny with divergence-time estimation of planthoppers (Hemiptera: Fulgoroidea) based on mitochondrial sequences

Figure 3. ML analysis based on PCG12R–ATP8. Numerals at nodes are bootstrap values (BS). '–' indicates different clades.

Rock-magnetic, paleomagnetic and multimethod paleointensity data from the Pliocene Khaveti lava flow sequence in Georgia

The folder “1 Rock magnetic data Khaveti.zip” contains data in .txt format of IRM acquisition curves (extension .irm), hysteresis curves (extension .hys), backfield curves (extension .coe) and thermomagnetic magnetisation versus temperature curves (extension .rmp) obtained on Pliocene volcanic rocks from a lava flow sequence in the Lesser Caucasus in Georgia . Measurements were performed in the paleomagnetic laboratory of the University of Burgos (Spain). Columns are separated by tabs. Data can be visualised and analysed with the RockMagAnalyzer 1.0 software (Leonhardt, 2006). The folder “2 Paleomagnetic data Khaveti.zip” contains paleomagnetic thermal and alternating field demagnetisation data obtained on Pliocene volcanic rocks from a lava flow sequence in the Lesser Caucasus in Georgia. Measurements were performed in the paleomagnetic laboratory of the University of Burgos (Spain). Data are in .txt format with the extension .rs3. Columns are separated by empty spaces. Data can be visualised and analysed with the Remasoft software (Chadima and Hrouda, 2006). The folder “3 Paleointensity data Khaveti Thellier_Coe.zip” contains two folders with paleointensity determination data obtained with the Thellier-Coe method on Pliocene volcanic rocks from the Khaveti lava flow sequence in the Lesser Caucasus in Georgia. The two folders Thellier_Coe Khaveti_down and Thellier_Coe Khaveti_up contain paleointensity determination data obtained respectively in the 14 lower and 14 upper flows of the sequence. Experiments were carried out in the Paleomagnetics and Magnetic Materials Laboratory of the University of Hawai’i at Manoa. Data from each flow have been put in an own folder, and are in .txt format with the extension .tdt separated by tabs. Data can be visualised and analysed with the ThellierTool software (Leonhardt et al.,2004). The folder “4 Paleointensity data Khaveti Multispecimen.zip” contains two folders with paleointensity determination data from Pliocene volcanic rocks from the Khaveti lava flow sequence in the Lesser Caucasus in Georgia. These data were obtained with the multispecimen method (Biggin and Poidras, 2006; Dekkers and Böhnel, 2006; Fabian and Leonhardt, 2010). The folder MS_Morelia contains determinations carried out at Laboratorio Interinstitucional de Magnetismo Natural, Instituto de Geofísica, Unidad Michoacán, UNAM, Mexico. The folder MS_Burgos contains determinations carried out at the paleomagnetic laboratory of the University of Burgos. All files are Excel files, and each one corresponds to a single sample subjected to multispecimen analysis. Data can be visualised and analysed with the VBA based software tool “MSP-Tool” (Monster et al.,2015) directly included in each one of the sample files.   <h3>REFERENCES</h3>   Biggin, A., Poidras, T., 2006. First-order symmetry of weak-field partial thermoremanence in multi-domain ferromagnetic grains. 1. Experimental evidence and physical implications. Earth Planet. Sci. Lett. 245, 438–453. doi:10.1016/j.epsl.2006.02.035 Chadima, M. and Hrouda, F., 2006. Remasoft 3.0 a user friendly paleomagnetic data browser and analyzer. Travaux Géophysiques, XXVII, 20-21. Dekkers, M.J., Böhnel, H.N., 2006. Reliable absolute palaeointensities independent of magnetic domain state. Earth Planet. Sci. Lett. 248, 507–516. doi:10.1016/j.epsl.2006.05.040 Fabian, K., Leonhardt, R., 2010. Multiple-specimen absolute paleointensity determination: An optimal protocol including pTRM normalization, domain-state correction, and alteration test. Earth Planet. Sci. Lett. 297, 84–94. doi:10.1016/j.epsl.2010.06.006 Leonhardt, R., Heunemann, C. and Krása, D., 2004. Analyzing absolute paleointensity determinations: Acceptance criteria and the software ThellierTool4.0. Geochem. Geophys. Geosyst., Vol. 5, no. 12, doi.: 10.1029/2004GC000807. Leonhardt, R., 2006. Analyzing rock magnetic measurements; The RockMagAnalyzer 1.0 software.Computers and Geosciences, 32, 1420-1431. Monster, M.W.L., de Groot, L. V., Dekkers, M.J., 2015. MSP-Tool: A VBA-Based Software Tool for the Analysis of Multispecimen Paleointensity Data. Front. Earth Sci. 3, 1–9. https://doi.org/10.3389/feart.2015.00086