Find research datasets worth reusing

opencc-zeroJan 2018View details →

Supplementary material 1 from: Ang Y, Rajaratnam G, Su KFY, Meier R (2017) Hidden in the urban parks of New York City: Themira lohmanus, a new species of Sepsidae described based on morphology, DNA sequences, mating behavior, and reproductive isolation (Sepsidae, Diptera). ZooKeys 698: 95-111. https://doi.org/10.3897/zookeys.698.13411

Aligned COI sequences for Themira putris, T. biloba, and Themira 'biloba-like' (i.e., T. lohmanus n. sp.) specimens : Data type: Fasta file

opencc-by-4.0May 2024View details →

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-4.0Jul 2024View details →

MADDD-seq, a novel massively parallel sequencing tool for simultaneous detection of DNA damage and mutations

The file "data.tar" contains the output of the MADDD-seq pipepline. There is one sub-folder per sample. For each sample, the most important files are: <ul> <li>max_variants_2.adduct.gtf : A GTF file with the location (and details) about each adduct called by the pipepline</li> <li>max_variants_2.DSC.vcf.gz : A VCF (Variant Call File) with information about mutations called.</li> <li>coverage.rds : pre-computed coverage information in binary format to be loaded in R.</li> </ul> To analyze this data, use the following R files: adducts.R, mutations.R and jason-function-2022-04.R   The file "kallisto-h5.tar"  contains the output of running Kallisto on the regular RNAseq data (for expression level analysis). To analyze this data, use the following R files: Yeast-MNNG-MGT.Rmd and myDESeq2.R   The source code of the R files will need to be modified to point at the location of files on the computer being used. These modifications are pointed by comments in the code and are located towards the start of each file.

Supplementary material 1 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126

Figure S1, S2 : Explanation note: Figure S1. Bayesian phylogeny of 29 samples of Quercus and one Trigonobalanus (outgroup) based on ITS sequences. Branches are labeled with posterior probabilites. Figure S2. Bayesian phylogeny of 29 samples of Quercus and one Trigonobalanus (outgroup) based on concatenated rbcL and matK sequences. Branches are labeled with posterior probabilities.

Supplementary material 9 from: Ushio M, Murakami H, Masuda R, Sado T, Miya M, Sakurai S, Yamanaka H, Minamoto T, Kondoh M (2018) Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding and Metagenomics 2: e23297. https://doi.org/10.3897/mbmg.2.23297

Bland-Altman plots for the total fish eDNA (a), Japanese anchovy (Engraulis japonicus; b) and Japanese jack mackerel (Trachurus japonicus; c). Dashed lines indicate 95% uppper and lower limits and solid line indicates mean value.

Supplementary material 6 from: Ushio M, Murakami H, Masuda R, Sado T, Miya M, Sakurai S, Yamanaka H, Minamoto T, Kondoh M (2018) Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding and Metagenomics 2: e23297. https://doi.org/10.3897/mbmg.2.23297

The numbers of eDNA copies of marine fish species quantified by metabarcoding with the internal standard DNA

Supplementary material 8 from: Ushio M, Murakami H, Masuda R, Sado T, Miya M, Sakurai S, Yamanaka H, Minamoto T, Kondoh M (2018) Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding and Metagenomics 2: e23297. https://doi.org/10.3897/mbmg.2.23297

The relationship between MiSeq sequence reads and DNA copy numbers quantified by qPCR. Correlations for the total fish eDNA (all data, a; enlarged figure, b), Japanese anchovy (Engraulis japonicus; all data, c; enlarged figure, d) and Japanese jack mackerel (Trachurus japonicus; all data, e; enlarged figure, f). Dashed and soild lines indicate 1:1 line and linear regression line, respectively. Regression lines in the enlarged figures were drawn by excluding outliers. All regression lines, except for the lines for total fish eDNA, were significant (P < 0.05). Dotted boxed regions in a, c and e correspond to the range of the graphs in b, d and f, respectively. The intensity of red colour indicates the slope of the regression line used to convert sequence reads to the copy numbers.

Supplementary material 4 from: Ushio M, Murakami H, Masuda R, Sado T, Miya M, Sakurai S, Yamanaka H, Minamoto T, Kondoh M (2018) Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding and Metagenomics 2: e23297. https://doi.org/10.3897/mbmg.2.23297

The relationship between MiSeq sequence reads and copy numbers of standard DNAs for 52 samples. Blue line indicates the linear regression between sequence reads and copy numbers. The regression lines are used to convert the MiSeq reads into the calculated copy numbers. Numbers in a grey region indicate sampling date. Note that regression slopes are different amongst samples, i.e. the number of sequence reads generated per eDNA copy is different amongst samples.

Supplementary material 5 from: Ushio M, Murakami H, Masuda R, Sado T, Miya M, Sakurai S, Yamanaka H, Minamoto T, Kondoh M (2018) Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding and Metagenomics 2: e23297. https://doi.org/10.3897/mbmg.2.23297

The relationship between regression residuals and copy numbers of standard DNAs for 52 samples. Dashed line indicates zero residuals.

opencc-by-4.0Nov 2018View details →

Bamfiles DNA sequencing reads HCT strain Fo1A and Fo2A

Reads obtained from DNA sequencing of two strains resulting from the horizontal chromosome transfer experiment described in [doi: 10.1038/nature08850] and submitted to ENA [PRJEB29294] . For each strains two paired-end libraries were generated, one with ifragment length of about 170 bp and one with fragmentlength of 500 bp. They were first mapped to the genome of Fo47, unmapped reads were subsequently mapped to the genome of Fol4287, as described in [doi: 10.1101/465070]. Putative PCR duplicates were removed using Picard tools and resulting bamfiles (one for the 170 fragment library and one for the 500 bp fragment library) were merged. These files are the resulting bamfiles. Fo1A_L170_L500.unmapped_to_Fo47illumina.Fol4287broad.bowtie2.sorted_dedup.bam (one chromosome: chr 14) Fo2A_L170_L500.unmapped_to_Fo47illumina.Fol4287broad.bowtie2.sorted_dedup.bam (two chromosomes: chr 14 + small chromosome)

Fig. 4 in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 4. Scanning electron photomicrographs of pollen grains from the Iresine clade (= Iresinoids). A, Iresine cassiniiformis (Borsch & al. 3792); B, Iresine type XXXIV (Borsch & al. 5412); C, Iresine rzedowskii (Borsch & al. 3793); D, Iresine ajuscana (Borsch & al. 5404); E, Iresine orientalis (Borsch & al. 5404); F, Iresine discolor (Purpus 3453); G, Magnification of aperture with details of mesoporium of pollen from the same plant; H, Iresine hartmanii (Tenorino 1864); I, Iresine type XXXIV (Borsch & al. 5390). — Scale = 10 µm apart from G where it is 4 µm.

Fig. 7. Scanning electron photomicrographs from the alternantheroid and gomphrenoid clades. A in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 7. Scanning electron photomicrographs from the alternantheroid and gomphrenoid clades. A, Pedersenia cardenasii (Borsch & Ortuño 3504); B, Pedersenia sp. (Borsch & Ibisch 3532); C, Magnification of aperture and details of mesoporia of pollen from the same plant; D, Hebanthe occidentalis (Borsch & Ortuño 3512); E, Pfaffia dunaliana (Borsch & Ortuño 3756); F, Magnification of aperture and details of mesoporia of pollen from the same plant. — Scale = 10 µm apart from C where it is 4 µm.

Fig. 5. Scanning electron photomicrographs from the Iresine clade. A in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 5. Scanning electron photomicrographs from the Iresine clade. A, Iresine hebanthoides (Borsch & al. 5415); B, Magnification of aperture and details of mesoporia of pollen from the same plant; C, Iresine sousae (Mendez Ton 7192, isotype B); D, Iresine nitens (Borsch & al. 3770); E, Magnification of aperture and details of mesoporia of pollen from the same plant; F, Iresine latifolia (Borsch & al. 3790); G, Magnification of aperture and details of mesoporia of pollen from the same plant; H, Iresine diffusa (Borsch & al. 3676); I, Irenella cysotricha (Asplund 16555). — Scale = 10 µm apart from B, E and G where it is 4 µm and I where it is 2 µm.

Fig. 6. Scanning electron photomicrographs from the Iresine clade. A in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 6. Scanning electron photomicrographs from the Iresine clade. A, Iresine angustifolia (Zumaya & al. 81); B, Iresine nigra (Zumaya & al. 77); C, View from a different angle onto a pollen grain from the same plant; D, Iresine interrupta (Zumaya 62); E, Iresine borschii (Ventura 9443, paratype); F, Iresine arbuscula (Castillo s.n.). — Scale = 10 µm.

Fig. 1 in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 1. Morphological diversity of Iresine. A, Synflorescence of I. interrupta (Borsch & al. 3789); B, Pistillate flowers at maturity and C, Staminate flowers of I. interrupta (Borsch & al. 3789); D, Upright woody stem of Iresine type XXXIV (Borsch & al. 5390); E, Inflorescence and F, Woody stem of I. nigra (S. Zumaya & al. 77); G, Part of synflorescence with staminate (Borsch & al. 5385) and H, Pistillate flowers of I. ajuscana (Borsch & al. 5367). — Photos: T. Borsch.

opencc-zeroMar 2019View details →

Supplementary material 2 from: Zhao Y, Liu X-z, Bai F-y (2019) Four new species of Tremella (Tremellales, Basidiomycota) based on morphology and DNA sequence data. MycoKeys 47: 75-95. https://doi.org/10.3897/mycokeys.47.29180

Supplementary material 2 from: Zhao Y, Liu X-z, Bai F-y (2019) Four new species of Tremella (Tremellales, Basidiomycota) based on morphology and DNA sequence data. MycoKeys 47: 75-95. https://doi.org/10.3897/mycokeys.47.29180

opencc-zeroMar 2019View details →

Supplementary material 1 from: Zhao Y, Liu X-z, Bai F-y (2019) Four new species of Tremella (Tremellales, Basidiomycota) based on morphology and DNA sequence data. MycoKeys 47: 75-95. https://doi.org/10.3897/mycokeys.47.29180

Supplementary material 1 from: Zhao Y, Liu X-z, Bai F-y (2019) Four new species of Tremella (Tremellales, Basidiomycota) based on morphology and DNA sequence data. MycoKeys 47: 75-95. https://doi.org/10.3897/mycokeys.47.29180

opencc-by-4.0Apr 2019View details →

DNA sequence influences hexasome orientation to regulate DNA accessibility

Each .mat file contains selected traces from one or more videos from a single flow cell. The file name contains the concentration of Gal4 used in the experiment. There are 7 variables in the .mat files data - cell array where each element is an Nx2 matrix containing donor, acceptor pixel intensities for each frame. FRET - cell array where each element is a Nx1 matrix containing FRET values for each frame path - cell array where each element is a Nx1 matrix containing the idealized FRET value calculated from vbFRET path2D - cell array where each element is an Nx2 matrix containing idealized intensities for donor and acceptor from vbFRET (not used) thigh - high fret state dwell times tlow - low fret state dwell times.  We also include a matlab progam, TracesView2, that can be used to scroll through the traces.

opennotspecifiedAug 2019View details →

FIGURES 1–3 in Stenelmis koreana Satô, 1978 (Coleoptera: Elmidae): confirmed as a wide-spread species by DNA-sequencing

FIGURES 1–3. Stenelmis koreana, Kyrgyzstan, 1) male habitus, 2) female habitus, 3) aedeagus in ventral and lateral view. Scales: 1 mm (1–2), 0.2 mm (3).