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opencc-by-4.0Feb 2014View details →

High-throughput poly(A) length measurement of HeLa and NIH 3T3 cells using TAIL-seq with MiSeq

This dataset contains the full raw data directory from Illumina MiSeq generated for Chang et al. (2014, DOI: 10.1016/j.molcel.2014.02.007). Please refer to the original paper and its supplementary materials for further details.

opencc-by-4.0Sep 2018View details →

Data for: "A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death"

Data related to the publication Murschhauser et al.: <a href="https://doi.org/10.1038/s42003-019-0282-0">A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death</a>. It contains fluorescence time traces of single cells marked with cell-event markers and observed by time-lapse microscopy. The cells were treated with nanoparticles at different doses (NP25 and NP100), with staurosporine (sts) or were left untreated for control (ctrl). See the above-mentioned publication for more details. The format of the data is described below. The file <code>Data_A549.zip</code> contains data measured with A549 cells, and the file <code>Data_Huh7.zip</code> contains data measured with Huh7 cells. Both files have the same structure. Each file contains the directories <code>Raw</code> and <code>Fitted</code> as well as a checksum file. The <code>Raw</code> directory contains single-cell fluorescence time courses as obtained by time-lapse microscopy. The <code>Fitted</code> directory contains the results of fitting model functions as well as properties of identified events, such as event times. The checksum file contains SHA256 checksums of all files within these directories and can be used to check file integrity. Both directories contain measurement directories. Each measurement directory contains the data corresponding to one experiment. The name of the measurement directory is the measurement identifier. Each measurement directory contains condition directories. Each condition directory contains data corresponding to one condition measured in the measurement and is named after the condition. Each condition directory contains marker directories. They are named after the fluorescence markers measured and contain files with single-cell data corresponding to the respective markers. The names of those files consist of multiple parts separated by underscores. The first two parts identify a position of the microscope. Since pairs of markers were measured, each position is present in two marker directories. The third part is the measurement identifier. The other parts will be described below. The <code>Raw</code> directory contains only CSV files with the raw fluorescence time courses. The filenames contain no other parts and have the suffix “.txt”. The first row of each CSV file is the time (in units of 10 minutes), and the other rows are the fluorescence time courses of the cells observed at the corresponding position (in arbitrary units). Each file in the <code>Raw</code> directory corresponds to a group of files in the <code>Fitted</code> directory. The <code>Fitted</code> directory contains three types of CSV files. Their names have “ALL” as fourth part, a session identifier as sixth part and the suffix “.csv”. The fifth part indicates the type of file and is one of the following: <ul> <li>“PARAMS” indicates the estimated values for the model parameters. Each row stands for one cell and each column for a parameter of the model function fitted to the data. The model functions are published with the <a href="https://doi.org/10.5281/zenodo.1418465">fitting software</a>.</li> <li>“SIMULATED” indicates the fitted traces. The traces are calculated using the model functions and the estimated parameters. The format is the same as for the raw traces, but the time is in units of hours and has a higher resolution.</li> <li>“STATE” indicates additional information extracted from the fitted traces. Each row stands for a cell and each column for a property. The first column is the number of the cell. The second column is the event time found (in hours); non-finite values indicate that no event time was found. The third and fourth columns contain the absolute and relative amplitude of the trace, respectively. The fifth column is the logarithmic likelihood of the best fit. The sixth column indicates an algorithm used for postprocessing, and the seventh column indicates the trace slope at the event. See the fitting software for details.</li> </ul>

opencc-by-4.0Feb 2019View details →

Dataset of confocal microscopy stacks from plant samples - ImageJ SurfCut: a user-friendly, high-throughput pipeline for extracting cell contours from 3D confocal stacks

This data set contains confocal stacks from Arabidopsis thaliana 35S::GFP-MBD light grown hypocotyl as well as propidium iodide stained cotyledon pavement cells and shoot apical meristem. This is the test dataset for the Fiji macro SurfCut (https://github.com/sverger/SurfCut; 10.5281/zenodo.2635737)   Material and methods: Plant material and growth conditions Arabidopsis thaliana wild type Col-0 and the microtubule reporter line GFP-MBD (WS-4, (Marc et al. 1998) were used. Seeds were cold treated for 48 hr to synchronize germination. Plants were then grown in a phytotron at 20°C, in a 16 hr light/8 hr dark cycle on solid Murashige and Skoog medium (MS medium, Duchefa, Haarlem, the Netherlands) with 0.8% agar, 1% sucrose, and no vitamin.   Confocal microscopy Cell contour staining in the case of PC_PI_Col0_(1-8).tif and SAM_PI_Col-0.tif was performed by staining the cell wall with Propidium Iodide (PI). Plants were immersed in 0.2 mg/ml propidium iodide (PI, Sigma-Aldrich) for 10 min and washed with water prior to imaging. For imaging, samples were either placed on a solid agar medium and immersed in water, or placed between glass slide and coverslip separated by 400 μm spacers to prevent tissue crushing. Images were acquired using a Leica TCS SP8 confocal microscope, equipped with a water immersion objective (HCX IRAPO L 25x/0.95 W). PI excitation was performed using a 552 nm solid-state laser and fluorescence was detected at 600–650 nm. GFP excitation was performed using a 488 nm solid-state laser and fluorescence was detected at 495–535 nm. Stacks of 1024x1024 pixels (pixel size of 0.363 x 0.363 micron) optical section were generated with a Z interval of 0.5 μm.   File list: Light grown hypocotyl, GFP-MBD reporter line: - Hypocotyl_GFP-MBD.tif Cotyledon’s pavement cells, PI staining: - PC_PI_Col0_1.tif - PC_PI_Col0_2.tif - PC_PI_Col0_3.tif - PC_PI_Col0_4.tif - PC_PI_Col0_5.tif - PC_PI_Col0_6.tif - PC_PI_Col0_7.tif - PC_PI_Col0_8.tif Shoot apical meristem, PI staining: - SAM_PI_Col-0.tif   Reference: Marc, Jan, Cheryl L. Granger, Jennifer Brincat, Deborah D. Fisher, Teh-hui Kao, Andrew G. McCubbin, and Richard J. Cyr. 1998. “A GFP–MAP4 Reporter Gene for Visualizing Cortical Microtubule Rearrangements in Living Epidermal Cells.” The Plant Cell 10 (11): 1927–39. https://doi.org/10.1105/tpc.10.11.1927.

openapache2.0Aug 2024View details →

High-throughput Computational Screening of Hydrocarbon Molecules for Long-wavelength Infrared Imaging

This repository contains datasets associated with the paper titled "High-throughput Computational Screening of Hydrocarbon Molecules for Long-wavelength Infrared Imaging," accepted at ACS Materials Letters Journal. Contents: <ol> <li> Optimized XYZ Coordinates: The hydrocarbon molecules' XYZ coordinates, obtained using the B3LYP functional and the 6-31g(d,p) basis set in Gaussian 16 software, used to simulate the IR spectra (including transition energies and absorption intensities) of the molecules. </li> <li> Broadened Molar Absorptivity IR Spectra: The dataset's IR spectra, broadened using a Lorentzian band shape with a gamma (half-width at half-height) value of 5 cm⁻¹. Molecules with imaginary frequencies have been excluded. </li> <li> Related SMILES Strings: Contains SMILES strings for these hydrocarbons. </li> <li> NUMBERS_SMILES.csv: Provides the associated SMILES string for each numerated XYZ coordinate. </li> </ol> For any inquiries, please contact Dr. Maliheh Shaban Tameh at malihe.shaban<a rel="noreferrer">@gmail.com</a>

opencc-zeroJul 2021View details →

Simultaneous genotyping of snails and infecting trematode parasites using high-throughput amplicon sequencing.

Several methodological issues currently hamper the study of entire trematode communities within populations of their intermediate snail hosts. Here we develop a new workflow using high-throughput amplicon sequencing to simultaneously genotype snail hosts and their infecting trematode parasites. We designed primers to amplify 4 snail and 5 trematode markers in a single multiplex PCR. While also applicable to other genera, we focused on medically and economically important snail genera within the Superorder Hygrophila and targeted a broad taxonomic range of parasites within the Class Trematoda. We tested the workflow using 417 Biomphalaria glabrata specimens experimentally infected with Schistosoma rodhaini, two strains of Schistosoma mansoni, and combinations thereof. We evaluated the reliability of infection diagnostics, the robustness of the workflow, its specificity related to host and parasite identification, and the sensitivity to detect co-infections, immature infections, and changes of parasite biomass during the infection process. Finally, we investigated its applicability in wild-caught snails of other genera naturally infected with diverse trematode assemblages. After stringent quality control the workflow allows the identification of snails to species level, and of trematodes to taxonomic levels ranging from family to strain. It is sensitive to detect immature infections and changes in parasite biomass described in previous experimental studies. Co-infections were successfully identified, opening the possibility to examine parasite-parasite interactions such as interspecific competition. Altogether, these results demonstrate that our workflow provides a powerful tool to analyze the processes shaping trematode communities within natural snail populations.

Figure S1 in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods

Figure S1. Map of the Gigantometra gigas mitogenome using Sanger method (GenBank accession number: MF177288). Genes in the outer circle indicate the direction of transcription of the majority strand (J-strand), and those in the inner circle indicate that of the minority strand (N-strand). The GC content, GC skew+, and GC skew- are separately shown in the circle.

Figure 6 in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods

Figure 6. Two examples of the heteroplasmic sites in Sanger sequencing which correspond to the differently sequenced sites. Panels A and B indicate the sites at which the second-peak is obviously higher than the third-peak and fourth-peak, and the base state of the second-peak can be obtained by at least one result of HTS. The different fluorescence densities of base situated at np 1923 in the cox1 are shown in the panel A, and the panel B shows the nucleotides with amino acids at np 1923 in the results of Sanger and HTS methods. The nucleotides are C in the results of HTS sequencing, while the corresponding nucleotides are T in the results of Sanger method in both positions, and the different nucleotides lead not to the amino acids changed. Panels C and D indicate the site at the unobvious second-peak, which is slightly higher than the third-peak and fourth-peak, and the base state of the second-peak can also be obtained by at least one result of HTS. Panel C shows the unobvious second-peak at np 7125, and the nucleotide and amino acid of the site in the results of Sanger and HTS methods are shown in panel D. The amino acids are listed using single-letter amino acid abbreviations.

Figure 4. Intraspecific pairwise K2P in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods

Figure 4. Intraspecific pairwise K2P distance of G. gigas based on barcode fragment size of cox1 (Sanger). The red boxplot shows the genetic distances of individuals in all three collecting sites, and the boxplots (blue, green, and yellow) separately show the distances of individuals within each place (HNYG, HNDL, and VIET). The pink boxplot shows the distances of the corresponding cox1 sequences obtained by the two sequencing methods. Abbreviation: HNYG—Yinggeling Nature Reserve, Hainan; HNDL— Diaoluoshan Nature Reserve, Hainan; VIET—northern Vietnam.

Figure S5 in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods

Figure S5. The coverage of short fragments at each position in the assembly results of HTS. The three results of HTS method were separately used as reference sequences to be mapped back onto the corresponding HTS scaffolds, and the mitochondrial genes were shown below the corresponding coverage. The scale bar had an indicator at the mean coverage level and the coverage for each nucleotide position was indicated by the height of the blue line.

Figure 3 in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods

Figure 3. The different nucleotides in the ITS-1 and ITS-2 regions are shown. The result shows the different nucleotides at nucleotide position np 1897 (G nucleotide and T nucleotide) and np 2790 (C nucleotide and T nucleotide) obtained by Sanger and HTS methods.

Figure 1. Gigantometra gigas. A. Female, dorsal view. B. Male, dorsal view. C in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods

Figure 1. Gigantometra gigas. A. Female, dorsal view. B. Male, dorsal view. C. The narrow distribution of G. gigas.

opencc-by-4.0Nov 2022View details →

A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster - DATASET 1 of 2

Dataset associated with "A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster" by Jones et al "A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster".   See http://lab.gilest.ro/coccinella for more information This is archive 1 of 2

opencc-zeroDec 2022View details →

A high-throughput multispectral imaging system for museum specimens

We present an economical imaging system with integrated hardware and software to capture multispectral images of Lepidoptera with high efficiency. This method facilitates the comparison of colors and shapes among species at fine and broad taxonomic scales and may be adapted for other insect orders with greater three-dimensionality. Our system can image both the dorsal and ventral sides of pinned specimens. Together with our processing pipeline, the descriptive data can be used to systematically investigate multispectral colors and shapes based on full-wing reconstruction and a universally applicable ground plan that objectively quantifies wing patterns for species with different wing shapes (including tails) and venation systems. Basic morphological measurements, such as body length, thorax width, and antenna size are automatically generated. This system can increase exponentially the amount and quality of trait data extracted from museum specimens.

opencc-by-4.0Dec 2021View details →

Data analysis of LiP-MS data for high-throughput applications

Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to any perturbation or stimulus, protein structure may be altered by a variety of molecular events, such as post translational modifications, protein-protein interactions, aggregation, allostery, or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of a variety of biological processes and pathways. Here we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale (Cappelletti et al., 2021; Piazza et al., 2020; Schopper et al., 2017). We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition (DIA) based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes.  In addition, we introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. Altogether, the newly proposed improvements expand the adaptability of the method and allow for its wide use in systematic functional proteomic studies and translational applications.

opencc-zeroJan 2023View details →

Data for: High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display

Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.

opencc-by-4.0Mar 2023View details →

Supplemental Data for Architector for high-throughput cross-periodic table 3D complex building

This repository contains all of the data presented in either the main text or the SI for the manuscript "Architector for high-throughput cross-periodic table 3D complex building".

opencc-by-4.0Jan 2023View details →

High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics

Data to support the paper - High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics. Data includes a summary of X-ray and LCMS results for the reactions executed on the OpenTrons, output reports and summaries from MSCheck (semi-automated LCMS analyzer tool) and the Python scripts used to execute single and multistep chemistry on the OpenTrons. Abstract We demonstrate that a simple workflow of array synthesis, combining low-cost robotics with analytic techniques to deconvolute crude reaction mixtures, is an effective way to collect structural data on a binding site.  Starting from the high information content of the crystallographic fragment screens on PHIP(2) (second bromodomain of the pleckstrin homology domain interacting protein), a collection of more than 1800 compounds was enumerated. Several thousand Crude Reaction Mixtures (CRMs) were synthesized on one robotic platform, an OpenTrons OT-1 liquid handler, using reaction sequences of up to 5 chemical steps. Analysis via MScheck, an algorithm-based system for finding a m/z in a CRM, significantly shortened product identification protocol times. 957 usable X-ray diffraction datasets were acquired, which resolved as 22 reaction products binding to the protein, 19 with conserved poses relative to the original fragment and 3 with a new, unexpected binding pose. The 22 crystallographic hit compounds were subsequently tested with peptide displacement alpha-screen assay and time-resolved grating-coupled interferometry-based biosensor assays, which confirmed one molecule with an IC50 = 34 μM and KD = 50 μM, from an inactive fragment.  The procedures described are entirely formulaic and engineerable and the method is eminently scalable. We anticipate that this cheap, low solvent-use approach will yield vast amounts of data, enabling rapid SAR landscape exploration around fragments, leading to faster fragment to lead times.

opencc-zeroJun 2023View details →

Data from: Accelerated high-throughput imaging and phenotyping system for small organisms

Studying the complex web of interactions in biological communities requires large multifactorial experiments with sufficient statistical power. Automation tools reduce the time and labor associated with setup, data collection, and analysis in experiments that untangle these webs. We developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping. We showcase the abilities of our HTE system in a study with 6,000 experimental units grown across 2,000 treatments. These automated tools facilitated the collection and analysis of time-resolved growth data, which revealed finer dynamics of plant-microbe interactions across environmental gradients. Altogether, our HTE system can run experiments with up to 11,520 experimental units and can be adapted for other small organisms.

opencc-by-4.0Jun 2023View details →

Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay - data from the article published in PlosOne

Data that have been used in the manuscript "Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay" by M. Fontaine et al., published in PlosOne in 2023.