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NGS data related to Dossmann et al.: Specific DNMT3C flanking sequence preferences facilitate methylation of young murine retrotransposons
No full textCloning and site-directed mutagenesis
The gene of the catalytic, C-terminal domain of murine DNMT3C (amino acid residues 439-740 of P0DOY1) was obtained in E. coli codon optimized form from IDT Integrated DNA Technologies. The gene fragment was cloned with the StrataClone PCR Cloning Kit (Stratagene) into a StrataClone Vector Mix amp/kan (Stratagene) and plasmid transformation was performed with StrataClone SoloPack competent E. coli cells (Stratagene). Cells were recovered in SOC medium at 37 °C with shaking at 200 rpm for 1 h and cell suspension was plated on 2% LB-agar containing ampicillin at 37 °C. The next day, white single colonies were used for inoculating 2% LB liquid cultures containing ampicillin, which were shake grown overnight at 37 °C with 200 rpm. The plasmid DNA was then isolated using the NucleoSpin® Plasmid Kit (MACHEREY NAGEL). Next, the DNMT3C fragment was cloned into a His-tagged pET28 expression vector (Gowher & Jeltsch, 2002) using Gibson assembly. The entire pET28 vector containing the DNMT3C catalytic domain was amplified by rolling circle PCR using Pfu DNA Polymerase (ThermoFisher SCIENTIFIC) and subsequent digested with DpnI. Plasmid purification was performed using NucleoSpin® Gel and PCR Clean-up Kit (MACHEREY-NAGEL) and finally transformed into One Shot®Stbl3™ Chemically Competent E. coli cells (ThermoFischer Scientific). Cell recovery was done in SOC medium at 37 °C with shaking at 200 rpm for 1 h. The recovered cell liquid was then grown on 2% LB-agar containing kanamycin and incubated overnight at 37 °C. Single colonies were used for inoculating 2% LB liquid cultures containing kanamycin, which were shake grown overnight at 37 °C with 200 rpm. Plasmid DNA was isolated with NucleoSpin® Plasmid Kit (MACHEREY NAGEL). Site-directed mutagenesis of the DNMT3C WT plasmid was performed by using mutagenic primers containing one or two mismatches at the desired position to precisely insert the mutation and PCR amplification of the vector using Pfu DNA polymerase (ThermoFisher SCIENTIFIC) basically as described above. Transformation and purification was done as described above. The DNA sequences in all steps of the procedure as well as the final plasmid DNA sequences were confirmed by Sanger DNA sequencing (Microsynth Seqlab GmbH).
Protein overexpression and purification
Plasmids expressing His-tagged DNMT3C catalytic domain and its C543N, V547A, E590K and C543N/V547A (NA) mutants were transformed and overexpressed in E. coli BL21 (DE3) codon plus RIL cells (Stratagene). The overexpression and purification were performed as described for DNMT3A catalytic domain (Emperle et al. 2014). Some precipitation of the protein occurred during dialysis for the DNMT3C WT as well as mutant proteins. The protein concentration and purity were determined using Coomassie-stained 12% SDS polyacrylamide gels revealing a purity of >95% of all purified proteins.
Analysis of flanking sequence preference with a randomized substrate and bioinformatic data
For analysis of the flanking sequence preference, substrate with CpG or CpX sites in a 10 bp randomized sequence context were prepared as described (Dukatz, et al. 2020; Dukatz, et al. 2022). Substrate methylation reactions were performed with different enzyme concentrations (0.1875-30 µM) in methylation buffer (20 mM HEPES pH 7.5, 1 mM EDTA, 50 mM KCl, 0.25 mg/mL bovine serum albumin, 1 mM AdoMet (Sigma)) using 1 µM randomized substrate. Methylation reactions were incubated at 37 °C for 30-60 min. The reactions were stopped by freezing in liquid N2, followed by 2 h digestion with proteinase K (NEB) at 42 °C and purification with NucleoSpin® Gel and PCR Clean-up Kit (Macherey-Nagel). Bisulfite conversion was performed as described in the standard protocol EZ DNA Methylation-Lightning™ Kit (Zymo Research). Samples were eluted with RNase free H2O. Library preparation was performed with two PCRs using variable primer pairs to introduce sample specific barcodes and indices for sample distinction and the sequencing reactions. Bioinformatic analysis of the NGS data was conducted as described (Dukatz, et al. 2020; Dukatz, et al. 2022). For determination of the methylation rates of all 256 NNCGNN sequences by one enzyme, methylation reactions of individual substrates were assumed to be independent and reaction velocities are of first order with respect to the substrate concentrations. The results of the individual reactions with different enzyme concentrations and incubation times were fitted to monoexponential reaction progress curves using variable virtual time values. Fitting was conducted with MatLab as described except that convergence was validated by serial fitting (Adam, et al. 2022).
References
Adam S, Bräcker J, Klingel V, Osteresch B, Radde NE, Brockmeyer J, Bashtrykov P, Jeltsch A. Flanking sequences influence the activity of TET1 and TET2 methylcytosine dioxygenases and affect genomic 5hmC patterns. Communications Biology 5, 92 (2022)
Dukatz M, Dittrich M, Stahl E, Adam S, de Mendoza A, Bashtrykov P, Jeltsch A. DNA methyltransferase DNMT3A forms interaction networks with the CpG site and flanking sequence elements for efficient methylation. J. Biol. Chem. 298(10), 102462 (2022)
Dukatz M, Adam S, Biswal M, Song J, Bashtrykov P, Jeltsch A. Complex DNA sequence readout mechanisms of the DNMT3B DNA methyltransferase. Nucleic Acids Res 48, 11495-11509 (2020)
Emperle M, Rajavelu A, Reinhardt R, Jurkowska RZ, Jeltsch A. Cooperative DNA binding and protein/DNA fiber formation increases the activity of the Dnmt3a DNA methyltransferase. J Biol Chem 289, 29602-29613 (2014)
Gowher H, Jeltsch A. Molecular enzymology of the catalytic domains of the Dnmt3a and Dnmt3b DNA methyltransferases. J Biol Chem 277, 20409-20414 (2002)<br
Results for Stokes-Darcy mortar method
No full textThis dataset contains the results of the examples shown in the publication #TODO: add doi once accepted#. The first one investigates the orders of convergence with respect to the mesh size for different element types, as well as the efficiency of the interface preconditioner. The second example simulates flow through a channel around a porous obstacle. The third one presents a porous medium with a highly heterogeneous permeability field based on the Society of Petroleum Engineers SPE10 benchmark, over which a free-flow field is simulated. The code for reproduction of these results can be found at DaRUS.
Each archive contains the results of one of the examples in the form of VTK files that can be visualized with e.g. ParaView. Furthermore, the .geo and .msh files are geometry and mesh files that can be opened with gmsh.</p
Supporting Information: Notebooks, Solute Configurations and Solvation Free Energy Data
No full textThis dataset contains three types of data: 1) Jupyter notebooks (.ipynb) for the calculation of solvation free energies and for the recreation of all figures in the publication; 2) Gromacs files containing the solute and solvent topology (.gro, .itp, .top), the trajectories (.trr) and simulation parameter files (.mdp); 3) Results for solvation free energies from DFT based on the PC-SAFT equation of state (.csv).
Additional information regarding the files of this dataset can be found in the README.md
Additional Material: Viscosities of Inhomogeneous Systems from Generalized Entropy Scaling
No full textThis data set contains data of three categories: 1) LAMMPS input files (.lammps), postprocessing python script (.py) and density and velocity profiles (.dat) from NEMD. 2) DFT three-dimensional density profiles (.npy) for all systems. 3) Jupyter notebooks (.ipynb) for the calculation of densities from DFT, viscosity and velocity profiles from entropy scaling and for the recreation of all figures in the publication
Ship-based GNSS Tsunami Detection Pilot Network
No full textThe dataset contains the real-time GNSS position solutions from ships in the
Ship-based GNSS Tsunami Detection Pilot Network. Each file contains the full
rate ascii position data for one calendar year per ship with one line per
record and the following columns:
YYYY MM DD HH MN SS LAT LON ELEV SIGMAY SIGMAX SIGMAZ
Date and time are in UTC. LAT LON are in degrees, with ELEV being the antenna
reference point height in meters above the WGS84 ellipsoid. SIGMAX|Y|Z are the
formal errors returned by the RTX Positioning service onboard each ship's GNSS
receiver (Units are meters).
Files are named by ship and year. (Note that the Maersk Aotea started the project
named Maersk Svend, and its name was changed during the project. We have gathered all the data under its Aotea name.
The R/Vs Kamimikai and Kilo Moana are research vessels from the University of Hawaii
The R/V Ron Brown is a NOAA research vessel.
M/V Mahimahi and Manoa are Matson Inc container ships.
Maersk ships are all the same model of container ship.
<p
COLife_02 - Gigamap and Game Design
No full textThe dataset covers a gigamapping of game design and execution of the game GoCOLife that introduces a more-than-human perspective to the players. The data were produced as part of a studio course 'COLife: More-than-Human Perspective to CoDesign' in the summer semester 2024
Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction
No full textThis is a PyTorch implementation of the paper Hyperbolic Embedding Inference for Structured Multi-Label Prediction published in NeurIPS 2022.
The code provides the Python scripts to reproduce the experiments in the paper, as well as a proof-of-concept example of the method. To execute the code, follow the instructions in the README.md file. For more info, please check the paper.
Please have no hesitation to contact the authors for any inquiries.</p
Replication Code for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures
No full textThis dataset consists of software code associated with the publication titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes a Dockerimage that contains the precompiled code for immediate use. For transparency, the Dockerfile is also provided.
1
Download the Dataset:
Download the compressed Dockerimage wrr_image.tar directly.
If you want to inspect the Dockerimage, you can have a look at the associated Dockerfile first. Inside the Dockerfile one will find an instance of git which is privately hosted and not guaranteed to be hosted forever. Source code can also be inspected inside the docker container.
2
Load Docker Image:
Load the Docker image from the provided tar.xz file.
docker load --input wrr_image.tar
3
Run Docker Container:
Run the Docker container with appropriate volume mounts.
docker run -v $(pwd)/share/:/home/wrr_user/code/simulations/run/customBoussinesq/share -it wrr_image
It might be that the image is called slightly differently for instance wrr_image:latest , one needs to check the terminal output.
This command mounts the share directory from your current host directory into the container's /home/wrr_user/code/simulations/run/customBoussinesq/share directory. This allows you to move simulation results outside of the container by moving the results into the share folder.
4
Running Computations:
The container is precompiled with necessary resources.
You can either submit bash scripts to the cluster scheduler or use the Allrun scripts inside the cases. Move to the desired case and type ./Allrun 4 to run with 4 cores.
Note that the computations are resource-intensive and may not work on a local machine, even with an appropriate number of cores set.
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Replication Data for: Experimentally uncovering isolas via backbone tracking
No full textThis dataset contains CAD model and technical drawings for the test rig in [1], as well as measurement data from [1].
Measurement data obtained by three types of nonlinear tests is available:
- Backbone tests with phase fixed at resonance
- Frequency-Response Curves (FRC) with fixed excitation amplitude and stepped phase values
- Frequency-Response Curves (FRC) with fixed excitation amplitude and stepped frequency values
The test methods are described in [1].
Velocity data was acquired using a multi-point vibrometer (MPV), and two single-point vibrometers (SPV 1-2). Single-point/differential vibrometers (SPVs) were used for feedback control during the nonlinear tests.
The naming and the content of each data file type is described in the file “README.txt”. The sensor locations are indicated in the file "Measurement_Points.pdf".
REFERENCES
[1] https://doi.org/10.25518/2684-6500.180
ACKNOWLEDGEMENTS
The authors are grateful for the funding received by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [Projects 438529800, 495957501]
Dumux code for modelling stable water isotopologue transport within soils using fractionation parameterizations
No full textDuMux source code to reproduce the results presented in J. Schneider, S. Kiemle, K. Heck, Y. Rothfuss, I. Braud, R. Helmig, J. Vanderborght (2024) Analysis of experimental and simulation data of evaporation-driven isotopic fractionation in unsaturated porous media. Vadose Zone Journal, e20363.
The contained application allows modeling stable water isotopologue transport and fractionation in soils. The application uses parameterization to describe the fractionation processes during soil water evaporation