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837 results for “conformation”
CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning
<p>CREMP: A resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical tight-binding DFT calculations. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics.</p> <p>We provide the data in two available formats, either as Python pickle files, which provide quick read access with RDKit version 2022.09.5 or later, and as text-based SDF files with associated metadata in JSON format. Each file is named based on its amino acid sequence, with residues separated by periods, using standard one-letter codes with lowercase letters representing D-amino acids and "Me" prefixes representing <em>N</em>-methylated amino acids. The sequences are in no particular order, e.g., "C.R.E.M.P" and "R.E.M.P.C" correspond to the same peptide macrocycle. The filename extensions are ".pickle", ".sdf", and ".json".</p> <p>Each file in the “pickle” folder contains a Python dictionary with amino acid sequence, SMILES, CREST metadata, and a single RDKit molecule object containing all conformers. All files in the folder were compressed into a single “pickle.tar.gz” archive. In the “sdf_and_json” folder, each individual SDF file contains all conformers, each associated with its own JSON file that contains CREST metadata. Similarly, all are compressed into another single archive, “sdf_and_json.tar.bz2”. A single summary CSV file is also provided containing ”sequence”, “smiles”, “num_monomers”, “num_atoms”, “num_heavy_atoms”, along with the CREST metadata “totalconfs”, “uniqueconfs”, “lowestenergy”, “poplowestpct”, “temperature”, “ensembleenergy”, “ensembleentropy”, and “ensemblefreeenergy”. The number of unique conformers with different 3D structures is given by “uniqueconfs”, while “totalconfs” includes the number of rotamers in addition.</p> <p>The unzipped sizes of the archives are approximately 32 GB for "pickle.tar.gz" and 210 GB for "sdf_and_json.tar.bz2". If you encounter errors when trying to load the pickle files, please make sure your RDKit version is at least 2022.09.5. If that doesn't work, try other Python versions.</p>
Molecular dynamics trajectories for "Reservoir-REMD facilitates kinetic rescue from metastable peptide conformations
<p>The molecular dynamics-generated ensemble dataset for cyclo-(cGHHQKLV), used in the manuscript "Reservoir-REMD facilitates kinetic rescue from metastable peptide conformations". The dataset consists of 14 + 6 =20 .dcd files, and one .pdb file for rendering.</p>
Classifying protein kinase conformations with machine learning: data
<p>This data collection accompanies the manuscript "Classifying protein kinase conformations with machine learning".</p> <p>It is created using the <a href="https://github.com/edikedik/kinactive">kinactive</a> v0.1 tool written in pure Python v3.10. <strong>Note that the data are provided for the reference and reproducibility purposes and will not be compatible with later versions of `kinactive` built upon <a href="https://github.com/edikedik/lXtractor">lXtractor</a> > 0.1.1.</strong> Refer to the <a href="https://kinactive.readthedocs.io/en/latest/index.html">kinactive documentation</a> for instructions on how to obtain an actualized version of the structural kinome collection.</p> <p>File descriptions:</p> <ul> <li>db_v3.tar.gz -- a structural kinome collection archive. One can unpack it and inspect the contents or load it into the Python interpreter using `kinactive` or `lXtractor` tools.</li> <li>db_af2.tar.gz -- an AlphaFold2 kinome collection for Swiss-Prot sequences.</li> <li>default_*_vs.tsv -- structure/sequence variables calculated with lXtractor and used in an interpretable ML pipeline.</li> <li>*_features.tsv -- lists of ranked features selected by the <a href="https://github.com/edikedik/eBoruta">eBoruta</a> tool for each classifier.</li> <li>Supplement_labels.tsv -- ML model predictions for each PK domain structure found in db_v3.</li> <li>predictions_af2.csv -- Active/Inactive and DFG labels predicted for domains in db_af2.</li> </ul> <p> </p>
Free energy simulations of receptor-binding domain opening in the SARS-CoV-2 spike indicate a barrierless transition with slow conformational motions
<p>This online data set accompanies the manuscript entitled "Free energy<br> simulations of receptor-binding domain opening in the SARS-CoV-2 spike<br> indicate a barrierless transition with slow conformational motions."</p> <p>The dataset is composed of the following files:</p> <p>* pmf0-now.dcd -- pmf63-now.dcd : molecular dynamics trajectory frames in<br> each of the 64 umbrella sampling windows, from which water has been<br> removed to save space</p> <p>* s1am_0-now.pdb -- s1am_63-now.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, from which water has been removed,<br> corresponding to the trajectory data above</p> <p>* view -- Visual Molecular Dynamics command script to load a trajectory, <br> e.g., in Linux, use "vmd -e view"</p> <p>* s1am_0-cg.dcd -- s1am_63-cg.dcd : molecular dynamics<br> trajectory frames in each of the 64 umbrella sampling windows, coarse-grained to<br> 1 bead per residue.</p> <p>* s1am_0-cg.pdb -- s1am_63-cg.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, corresponding to the coarse-grained trajectory<br> data above.</p> <p>* viewcg -- Visual Molecular Dynamics command script to load a<br> coarse-grained trajectory, e.g., in Linux, use "vmd -e viewcg"</p> <p>* 0readme -- brief instructions on how to view the trajectories</p> <p>* colors.vmd -- utility script for VMD</p> <p>* covmacros.vmd -- VMD script to define coronavirus spike subdomains</p> <p>* fe.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the free energy profiles</p> <p>* diff.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the diffusion and mean first passage times calculations</p> <p>* pca-qha.zip -- ZIP archive that contains the data and Matlab analysis files<br> to compute the autocorrelation functions of trajectory displacements<br> along principal/quasiharmonic modes</p> <p>Each ZIP archive contains a "0readme" file with brief instructions, and also the <br> results of the calculations<br> </p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 80 wt% C12E5, T=298K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Supplementary Material: Conformational Ensemble of the Poliovirus 3CD Precursor Observed by MD Simulations and Confirmed by SAXS: A Strategy to Expand the Viral Proteome?
<p>Supplementary video for <em>Viruses</em> <strong>2015</strong>, <em>7</em>(11), 5962-5986; doi:10.3390/v7112919; http://www.mdpi.com/1999-4915/7/11/2919.</p> <p><strong>Movie S1.</strong> Dynamic interface between 3C and 3D domains revealed by accelerated MD. The 3C and 3D domains are colored cyan and blue, respectively. The active-site residues of the protease (His-40, Glu-71, Cys-147) and the polymerase (Asp-416, Asp-511, Asp-512) domains are represented by red spheres to help identifying the relative orientations of two domains.</p>
Figure 4. Canonical Variates Analysis head conformation diagram for 136 in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 4. Canonical Variates Analysis head conformation diagram for 136 Triatoma maculata specimens and thin-plate deformation grids. A. V instar–Adults. B. I instar–Adults. C. II instar–III instar.
Figure 3. Canonical Variates Analysis head conformation diagram for 140 in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 3. Canonical Variates Analysis head conformation diagram for 140 Rhodnius prolixus specimens and thin-plate deformation grids. A. V instar–Adults. B. I instar–Adults. C. II instar–III instar.
Dataset to manuscript entitled "Non-fluorescent transient states of tyrosine - a basis for label-free protein conformation and interaction studies" submitted to Scientifc Reports
<p><strong>This folder contains all raw data underlying the results presented in a manuscript, submitted to</strong><strong> </strong><strong><em>Scientifc Reports, </em>and entitled</strong><strong>:</strong></p> <p><strong><em>Non-fluorescent transient states of tyrosine - a basis for label-free protein conformation and interaction studies</em></strong></p> <p> </p> <p><strong>Authored by:</strong></p> <p><strong>Niusha Bagheri <sup>1</sup>, Hongjian Chen <sup>1</sup>, Mihailo Rabasovic <sup>2</sup>, Jerker Widengren <sup>1,*</sup></strong></p> <p><sup>1 </sup>Royal Institute of Technology (KTH), Experimental Biomolecular Physics, Dept. Applied Physics, Albanova University Center 106 91 Stockholm, Sweden</p> <p><sup>2 </sup>Laboratory for Biophysics, Institute of Physics Belgrade, Pregrevica 118<br>11080 Zemun-Belgrade, Serbia</p> <p>* Corresponding author (jwideng@kth.se)</p> <p><strong> </strong></p> <p><strong>The data files are grouped into the different techniques used to generate them, and refer to the figures/tables in the manuscript where the extracted results are presented. </strong></p> <p> </p> <p><strong>ABSTRACT</strong></p> <p>The amino acids tryptophan, tyrosine, and phenylalanine have been extensively used for different label-free protein studies, based on the intensity, lifetime, wavelength and/or polarization of their emitted fluorescence. Like most fluorescent organic molecules, these amino acids also tend to undergo transitions into dark meta-stable states, such as triplet and photo-radical states. While this may be perceived as a problem, these transitions are also highly environment-sensitive and can be used as an additional set of parameters, reflecting interactions, folding states, and immediate environments around the proteins. In this work, we applied the transient state monitoring (TRAST) technique, analyzing the average intensity of tyrosine emission under different excitation modulations, to characterize the photo physics of tyrosine for such readout purposes. By investigating how the dark state transitions of tyrosine varied with excitation intensity and solvent conditions we established a photophysical model for tyrosine. Next, we studied Calmodulin (containing two tyrosines), which upon calcium binding takes a more folded conformation. From these TRAST experiments, performed with 280nm time-modulated excitation, we show that tyrosine dark state transitions clearly change with the calmodulin conformation, and may thus represent a useful source of information for (label-free) analyses of protein conformations and interactions.</p>
Molecular Insights into the Effects of F16L and F19L Substitutions on the Conformation and Aggregation Dynamics of Human Calcitonin
<p><a name="_Hlk145518059"></a><span>Human calcitonin (hCT) regulates calcium-phosphorus metabolism, but its amyloid aggregation disrupts physiological activity, increases thyroid carcinoma risk, and hampers its clinical use for bone-related diseases like osteoporosis and Paget’s disease. Improving hCT with targeted modifications to mitigate amyloid formation while maintaining function holds promise as a strategy. Understanding how each residue in hCT's amyloidogenic core affects its structure and aggregation dynamics is crucial for designing effective analogs. Mutants F16L-hCT and F19L-hCT, where Phe residues in the core are replaced with Leu as in non-amyloidogenic salmon calcitonin, showed different aggregation kinetics. However, the molecular effects of these substitutions in hCT are still unclear. Here, </span><a name="OLE_LINK4"></a><span><span>we systematically investigated the folding and self-assembly conformational dynamics of hCT, F16L-hCT, and F19L-hCT through multiple long-timescale independent atomistic discrete molecular dynamics (DMD) simulations. </span></span><span><span>Our results indicated that the hCT monomer primarily assumed unstructured conformations with dynamic helices around residues 4-12 and 14-21. During self-assembly, the amyloidogenic core of hCT<sub>14-21</sub> converted from dynamic helices to β-sheets. However, substituting F16L did not induce significant conformational changes, as F16L-hCT exhibited similar characteristics to wild-type hCT in both monomeric and oligomeric states. In contrast, F19L-hCT exhibited substantially more helices and fewer β-sheets than hCT, irrespective of their monomers or oligomers. The substitution of F19L significantly enhanced the stability of the helical conformation for hCT<sub>14-21</sub>, thereby suppressing the helix-to-β-sheet conformational conversion. Overall, our findings elucidate the molecular mechanisms underlying hCT aggregation and the effects of F16L and F19L substitutions on the conformational dynamics of hCT, highlighting the critical role of F19 as an important target in the design of amyloid-resistant hCT analogs for future clinical applications.</span></span></p>
Data for Identifying Knot Types of Polymer Conformations by Machine Learning
<h1>Training Data and Generalizability Testsets</h1> <h2>For the publication PhysRevE.101.022502</h2> <pre>@article{PhysRevE.101.022502, title = {Identifying knot types of polymer conformations by machine learning}, author = {Vandans, Olafs and Yang, Kaiyuan and Wu, Zhongtao and Dai, Liang}, journal = {Phys. Rev. E}, volume = {101}, issue = {2}, pages = {022502}, numpages = {10}, year = {2020}, month = {Feb}, publisher = {American Physical Society}, doi = {10.1103/PhysRevE.101.022502}, url = {https://link.aps.org/doi/10.1103/PhysRevE.101.022502} }</pre> <h2>GitHub source code demo using this dataset: </h2> <p><a href="https://github.com/CompSoftMatterBiophysics-CityU-HK/Identify-Knot-Types-by-ML-PRE2020"><strong>🥨 https://github.com/CompSoftMatterBiophysics-CityU-HK/Identify-Knot-Types-by-ML-PRE2020</strong></a></p> <p><strong>The above GitHub repo provide <strong>a docker, training code, best model with weights, and two showcases of generalizability</strong>.</strong></p>
Dataset: 800 QM/MM minimum energy pathway conformations for the acylation reactions of Toho-1/ampicillin and Toho-1/cefalexin
<p>This dataset consists of 800 coordinate files (in the CHARMM psf/cor format) for the QM/MM minimum energy pathways of the acylation reactions between a Class A beta-lactamases (Toho-1) and two beta-lactam antibiotic molecules (ampicillin and cefalexin).</p> <p>These files are:</p> <ul> <li>toho_amp.r1-ae.zip: The R1-AE acylation pathways for Toho-1/Ampicillin (200 pathways);</li> <li>toho_amp.r2-ae.zip: The R2-AE acylation pathways for Toho-1/Ampicillin (200 pathways);</li> <li>toho_cex.r1-ae.zip: The R1-AE acylation pathways for Toho-1/Cefalexin (200 pathways);</li> <li>toho_cex.r2-ae.zip: The R2-AE acylation pathways for Toho-1/Cefalexin (200 pathways);</li> <li>energies.zip: the replica energies at B3LYP-D3/6-31+G**/C36 level;</li> <li>chelpgs.zip: the ChElPG charges of all reactant replicas at B3LYP-D3/6-31+G**/C36 level;</li> <li>farrys.zip: the featurzied NumPy arrays for model training;</li> <li>peephole.zip: an example file for how the optimized MEPs look like; </li> <li>dftb3_benchmark.zip: the reference calculations to justify the use of DFTB3/3OB-F/C36 in MEP optimizations, the reference level of theory is B3LYP-D3/6-31G**/C36. </li> </ul> <p>The R1-AE pathways are the acylation uses Glu166 as the general base; the R2-AE pathways uses Lys73 and Glu166 as the concerted base. </p> <p>All QM/MM pathways are optimized at the DFTB3/3OB-f/CHARMM36 level of theory. </p> <p>Z. Song et al Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways. <em>ACS Phys. Chem Au</em> 2022, <strong>2</strong>, 4, 316–330. DOI: <a href="https://doi.org/10.1021/acsphyschemau.2c00005">10.1021/acsphyschemau.2c00005</a></p>
CH3CH2OCH3 conformer molecule 200 ps MD trajectory with energies and forces
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p> </p> <p>SOAP params:</p> <p>species=["H", "C", "O"],</p> <p>periodic=False,</p> <p>rcut=5.0,</p> <p>sigma=0.5,</p> <p>nmax=5,</p> <p>lmax=5,</p> <p>average="outer" / "inner",</p> <p>crossover=True,</p> <p>dtype="float64",</p> <p>------------------------------------------------</p> <p>SOAP invariants were calculated with DScribe library (https://pypi.org/project/dscribe/1.2.1/)</p> <p> </p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
API Description-Based Conformance Assessment of Architectural Design Decision: Dataset and Code
<p>This is the dataset and related code artifact for the article "API Description-Based Conformance Assessment of Architectural Design Decision".</p> <p><strong>Abstract of the article:</strong></p> <p>Microservice APIs are often designed based on Domain-Driven Design. It can be challenging to judge the quality<br> of the relation between such a design and the implemented API, especially when facing frequent releases and changes in an API and its design models. Conformance assessment aims to improve software quality by bridging the gap between design and implementation. However, manual conformance assessment is time-intensive and error prone. This paper proposes a novel approach for automated conformance assessment of API designs in relation to API design decisions. Our approach aims to provide<br> the first fully automated conformance assessment approach based solely on the code of API Descriptions such as OpenAPI. We<br> applied and exemplified our approach for checking conformance to the patterns and practices in an API design decision for mapping links in a system’s domain model to API representations. The total accuracy score (F1-measure) for decision option identification in our multi-case study for this decision is 97.22%.</p>
Conformation and structural features of diuron and irgarol: insights from quantum chemistry calculations.
<p>A set of conformational relevant structures of Diuron and Irgarol (two biocides) obtained from conformational analyses carried out using Density Functional Theory (DFT). The geometries are given in the mol2 format.</p>
Characterisation of the conformations of amyloid beta42 in solution that may mediate its initial hydrophobic aggregation
<p>General: This is a data repository comprising all the bias-exchange metadynamics simulation files performed to characterise the structural ensemble of Amyloid-ß 42 peptide in solution.<br> The simulation was performed as an explicit model however the solvent was removed while uploading the trajectory to reduce file size.</p> <p>Files:<br> 1. XTCs : Cat19r0tp.xtc, Cat19r1tp.xtc, Cat19r2tp.xtc<br> 2. TPR : 19-mdrun0.tpr, 19-mdrun1.tpr,19-mdrun2.tpr<br> 3. HILLS and COLVAR files <br> 4. Others: Input.gro, PLumed input file, and topol.top</p> <p>for any further information<br> 1. Krushna Sonar : k.sonar@postgrad.curtin.edu.au<br> 2. Ricardo L. Mancera: <a href="mailto:R.Mancera@curtin.edu.au">R.Mancera@curtin.edu.a</a>u</p>
Supplementary Simulation Data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation"
<p>Additional simulation data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation", which has been submitted as a preprint to ChemRxiv.</p>
Conformer datasets for "Equivariant Graph Neural Networks for Toxicity Prediction"
<p>Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum-mechanical properties of molecules. Inspired by this, we investigated the performance of EGNNs to construct reliable ML models for toxicity prediction. We used the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity data sets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most data sets comparable to state-of-the-art models. We also test a physicochemical property, namely, the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests that these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and thus increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse data sets, EGNNs will be an essential tool in this domain.</p> <p>PAPER</p> <p>https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.3c00032</p> <p>CODE and MODELS:</p> <p>The conformer data sets and trained toxicity models will be published upon acceptance of this work. The code has been made available at <a href="https://github.com/jule-c/ET-Tox">https://github.com/jule-c/ET-Tox</a>, and the processed data as well as pretrained models for training and testing can be downloaded from <a href="../record/7942946">https://zenodo.org/record/7942946</a>. We can provide the full list of conformers as XYZ files upon request.</p>
modelforge curated dataset: QM9 1000 conformer test dataset
<h1>Curated QM9 1000 Conformer Test Dataset:</h1> <p>This provides a curated hdf5 file for a subset of the QM9 dataset to be used for testing purposes, designed to be compatible with <a href="https://github.com/choderalab/modelforge">modelforge</a>, an infrastructure to implement and train NNPs. This dataset contains in total 1000 conformers (1 conformer per unique molecule).</p> <p>When applicable, the units of properties are provided in the datafile, encoded as strings compatible with the <a href="https://github.com/openforcefield/openff-units/">openff-units</a> package. For more information about the structure of the data file, please see the following:</p> <ul> <li><a href="https://github.com/choderalab/modelforge/wiki/Dataset-and-curation#curation-module">https://github.com/choderalab/modelforge/wiki/Dataset-and-curation#curation-module</a></li> </ul> <p>This curated dataset was generated using the modelforge software at commit c5c7153:</p> <ul> <li> Link to the source code at this commit: <a href="https://github.com/choderalab/modelforge/tree/c5c7153e06172fe8e6f25015250ecb5db05655cc">https://github.com/choderalab/modelforge/tree/c5c7153e06172fe8e6f25015250ecb5db05655cc</a></li> <li>Link to the script file used to generate the dataset: <a href="https://github.com/choderalab/modelforge/blob/c5c7153e06172fe8e6f25015250ecb5db05655cc/modelforge/curation/qm9_curation.py">https://github.com/choderalab/modelforge/blob/c5c7153e06172fe8e6f25015250ecb5db05655cc/modelforge/curation/qm9_curation.py</a></li> </ul> <p> </p> <h2>Original QM9 Dataset:</h2> <p>The QM9 dataset includes 133,885 organic molecules with up to nine total heavy atoms (C,O,N,or F; excluding H) original published by Ramakrishnan, et al. Properties in the QM9 dataset were calculated at the B3LYP/6-31G(2df,p) level of quantum chemistry.</p> <h3>Citations:</h3> <p><em>Original publication:</em></p> <ul> <li>Ramakrishnan, R., Dral, P., Rupp, M. et al."Quantum chemistry structures and properties of 134 kilo molecules." Sci Data 1, 140022 (2014). <a href="https://doi.org/10.1038/sdata.2014.22">https://doi.org/10.1038/sdata.2014.22</a></li> </ul> <p><em>Source dataset, released with CCO 1.0 Universal license:</em></p> <ul> <li>Ramakrishnan, Raghunathan; Dral, Pavlo; Rupp, Matthias; Anatole von Lilienfeld, O. (2014). Quantum chemistry structures and properties of 134 kilo molecules. figshare. Collection. <a href="https://doi.org/10.6084/m9.figshare.c.978904.v5">https://doi.org/10.6084/m9.figshare.c.978904.v5</a></li> </ul>
Supporting data for "Chromatin conformation and histone modification profiling across human kidney anatomic regions"
<p>Here we deposit supporting data for our manuscript entitled "Chromatin conformation and histone modification profiling across human kidney anatomic regions".</p> <ul> <li>donor_info.pdf: Additional clinical information of the donor involved in the study</li> <li>Large zip files with names starting as "hic": Juicer Hi-C outputs aligned with hg38 genome <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>hg19_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg19 genome</li> <li>hg38_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg38 genome</li> <li>CUTRUN_peak_hg19.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg19 alignment</li> <li>CUTRUN_peak_hg38.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg38 alignment</li> <li>CUTRUN_bigwig_hg38.tar.gz: CUT&RUN bigwig outputs generated by hg38 alignment <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>CUTRUN_overlapped_peaks.xlsx: Overlaps between H3K27me3 and H3K4me3 in each anatomical region. Peaks were loaded from ‘.peaks.stringent.bed’ files in R and converted to GRanges objects using package ‘GenomicRanges’. R function ‘intersect’ was used to calculate the overlap between two GRanges objects, represented as each row in the table. <ul> <li>Here we list the details of overlaps between H3K27me3 and H3K4me3 in each anatomical region, as well as overlaps for each of the histone markers across anatomical regions.</li> </ul> </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.
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.
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.
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.
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.