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204 results for “molecular network”
From MS/MS Library Implementation to Molecular Networks: Exploring Oxylipin Diversity with NEO-MSMS library
<p>Oxylipins, small polar molecules derived from the peroxidation of polyunsaturated fatty acids (PUFAs), serve as biomarkers for diseases and play crucial roles in human physiology and inflammation. Despite their significance, many non-enzymatic oxygenated metabolites of PUFAs (NEO-PUFAs) remain poorly reported, resulting in a lack of public datasets of experimental data and limiting their dereplication in further studies. To overcome this limitation, we constructed a high-resolution tandem mass spectrometry (MS/MS) database comprising pure NEO-PUFA (both commercial and synthesized) and<em> in vitro</em> free radical-induced oxidations of diverse PUFAs and oxylipins. Furthermore, we conducted various molecular networking analyses on these standards, as well as on in vitro free radical-induced oxidations, serum samples, and algae samples. This was done to demonstrate their potential in derreplication and to provide additional putative oxylipins This dataset contains all the data used in this study including the raw data and the MzMine 3 procesed data used to built the molecular networks (i.e. feature tables and MSMS spectra).</p> <p>The folder ‘libraries’ contained the NEO-MSMS library divided into three .mgf files, i.e., the standards obtained with Q-Exactive Focus (i.e. NEOMSMS_QFocus_lib.mgf), Orbitrap ID-X Tribrid (i.e. NEOMSMS_idX_lib.mgf), and from the Watrous et al. study (Cell Chem Biol., 2019; 26(3):433-442) (NEOMSMS_Watrous_lib.mgf). Moreover, the individual .mgf files from each compound was included in three subfolders following the same syntax. The folder ‘oxPUFAs’ contains the raw .mzML files of the LC-MSMS analysis and the mzMine alignment results (i.e., the feature tables and the extracted .mgf files) of the <em>in vitro</em> oxidized PUFAs. The folder ‘algae_serum’ contains the same type of data from the algae and serum samples. The folder ‘oxOxylipins’ contains the raw .mzML files of the LC-MSMS analysis of the miniaturized oxidation of HETE, rearrangement of PGH<sub>1</sub> and PGH<sub>2</sub>. Finally, the ‘putative_lib´ folder contains the putatively annotated oxylipins in the same structure as ‘libraries’ folder.</p>
Divergent molecular networks program functionally distinct CD8+ skin-resident memory T cells
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Supplementary videos for "Active flow network generates molecular transport by packets: case of the endoplasmic reticulum"
<p>Videos showing simulated motion on the active flow network for different switching timescales. In particular, compare <span class="math-tex">\(\tau_{\text{switch}} = 3 \text{ s}\)</span> to <span class="math-tex">\(\tau_{\text{switch}} = 30 \text{ ms}\)</span>. The red bubbles are proportional to the number of particles present in a node. Initially, all particles are placed in a central source node.</p>
Non-targeted metabolomics-based molecular networking enables the chemical characterization of Rumex sanguineus, a wild edible plant
<p>This dataset delves into the chemical composition of Rumex sanguineus employing non-targeted metabolomics and Feature-Based Molecular Networking (FBMN), with compound annotation facilitated by SIRIUS. Utilizing UHPLC-HRMS, we conducted comprehensive analyses on samples extracted from Rumex roots, stems, and leaves, further enhancing our insights through molecular networking.</p>
From mass spectral features to molecules in molecular networks: MolNotator LDB dataset.
<p>Finding actual molecules in LC-MS/MS experiments can prove challenging due to the considerable amount of redundant ions generated during ionization. In this context, MolNotator was created and validation with this dataset.</p> <p>MolNotator is a Python 3.7 package designed to predict molecules (molecular masses) by combinatorial triangulation in LC-MS/MS experiments after a preprocessing step using MZMine. An MGF and a CSV files output from MZmine are required as input for MolNotator which are placed in the "mzmine_out" folder of the project folder (the uploaded dataset).</p> <p>Instructions for the use of MolNotator are available on GitHub (https://github.com/ZzakB/MolNotator), Pypi (https://pypi.org/project/MolNotator/) and in the associated publication.</p> <p>The dataset consists of 193 LC-MS/MS analyses of lichen pure standards (previously used for the Lichen Database, LDB), 156 of which were detected by manual curation and served to benchmark MolNotator. Results indicated more than 90% of the 156 molecular masses were predicted by MolNotator under 2 ppm error on average.</p> <p>The project folder contains, in addition to the mzmine_out folder, a database and a params folder (see GitHub and Pypi) as well as a styles folder, containing different styles that can be imported on Cytoscape (https://cytoscape.org/) to visualise MolNotator's network output. </p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (parameters, trajectories)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7. <br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p>
OTU table: Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA)
<p>This dataset contains the OTU table and associated metadata from the ENIGMA study which was used in "A Practical Guide to Methods Controlling False Discoveries in Computational Biology" (Korthauer, K. and Kimes, P., et al. 2018; associated github: https://github.com/pkimes/benchmark-fdr/).</p> <p>The original raw data is available on MG-RAST (project mgp8190): https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp8190</p> <p>These data were processed as described in Korthauer & Kimes et al, 2018. The OTU table was provided by Renmao Tian and generated with the following pipeline: <a href="http://zhoulab5.rccc.ou.edu/pipelines/ASAP_web/pipeline_asap.php">http://zhoulab5.rccc.ou.edu/pipelines/ASAP_web/pipeline_asap.php</a>. </p> <p>More information about the ENIGMA project can be found at http://enigma.lbl.gov/</p>
Machine Learning and Network Analyses Reveals Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics
<p>Supplementary information for the thesis chapter: Machine Learning and Network Analyses Reveals Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics</p>
Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking
<p>Comparative analysis of SIRIUS to evaluate our method, Biotransformation-based Annotation Method (BAM). This dataset includes all scripts, data, and results relevant to this analysis. BAM can be found on GitHub (https://github.com/HassounLab/BAM). </p> <p> </p> <p> </p>
SI_III_4_Spatialized metabolomic annotation combining MALDI imaging and molecular network
<p>Ces documents regroupent les données supplémentaires générés lors du développement méthdologique pour la création de réseaux moléculaires par MALDI-FT-ICR IMS. Un .ppt regroupe l'ensemble des cartographies ioniques spécifiques à chaque ion.</p>
Discovery of indole alkaloids crienamides A and B from penicillium citrinum by a simulated MS/MS-guided molecular network strategy
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Molecular evolution of the sex peptide network in Drosophila
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Syk signaling network comparison reveals the potential molecular determinants of its tumor promoter and suppressor functions
<p>Extra data for the comparison of Syk signaling networks including colored KEGG pathways and Cytoscape sessions.</p>
Data from: Plant – herbivorous beetle networks: molecular characterization of trophic ecology within a threatened steppic environment
DNA barcoding facilitates many evolutionary and ecological studies, including the examination of the dietary diversity of herbivores. In this study, we present a survey of ecological associations between herbivorous beetles and host plants from seriously threatened European steppic grasslands. We determined host plants for the majority (65%) of steppic leaf beetles (55 species) and weevils (59) known from central Europe using two barcodes (trnL and rbcL) and two sequencing strategies (Sanger for mono/oligophagous species and Illumina for polyphagous taxa). To better understand the ecological associations between steppic beetles and their host plants, we tested the hypothesis that leaf beetles and weevils differ in food selection as a result of their phylogenetic relations (within genera and between families) and interactions with host plants. We found 224 links between the beetles and the plants. Beetles belonging to seven genera feed on the same or related plants. Their preferences were probably inherited from common ancestors and/or resulted from the host plant's chemistry. Beetles from four genera feed on different plants, possibly reducing intrageneric competition and possibly due to an adaptation to different plant chemical defences. We found significant correlations between the numbers of leaf beetle and weevil species feeding on particular plants for polyphagous taxa, but not for nonpolyphagous beetles. Finally, we found that the previous identifications of host plants based on direct observations are generally concordant with host plant barcoding from insect gut. Our results expand basic knowledge about the trophic relations of steppic beetles and plants and are immediately useful for conservation purposes.
Statistical Analysis of Feature-based Molecular Networking Results from Non-Targeted Metabolomics Data
<p>This folder contains the following used for the publication:</p><ul><li>MASSIVE Repositories: MSV000082312 and MSV000085786. This contains the original data in both .raw and .mzxml formats.</li><li>MZmine 3 files: The feature table (SD_BeachSurvey_GapFilled_quant.csv), the associated mgf file, the batch file (.xml) used for MZmine 3 to obtain the feature table, the mgf file for SIRIUS annotations (SD_BeachSurvey_SIRIUS_fixed.mgf)</li><li>SIRIUS and CANOPUS summary files (.tsv files)</li><li>FBMN Result files</li></ul>
Metadata - Direct C−H Arylation of Dithiophene-Tetrathiafulvalene: Tuneable Electronic Properties and 2D Self-Assembled Molecular Networks at the Solid/Liquid Interface
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Data for "Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition"
<p>Data for "Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition".</p> <p>neutron_data.zip --> neutron data in .nxspe form. S(Q,E) calculated using the DAVE software. Includes logbook spreadsheet. </p> <p>Training_Data.xyz --> xyz file containing training data used to generate Allegro machine learning forcefield in the paper</p> <p>nh3_pimd.deploy --> Trained Allegro model to that can be used in LAMMPS and RXMD software a ML forcefield </p> <p>POSCAR_UNIT_CELL_AMMONIA --> NH3 unit cell in solid phase in POSCAR format that can be read by the VASP software used to perform the DFT simmulations.</p>
Supplementary Table of Mechanism Unravelling of Sodium-glucose Cotransporter-2 Inhibitors against Diabetic Nephropathy via Network Pharma-cology and Molecular Docking
<p>This is the supplementary tables of article named"Mechanism Unravelling of Sodium-glucose Cotransporter-2 Inhibitors against Diabetic Nephropathy via Network Pharmacology and Molecular Docking"</p>
Data for "Investigating molecular transport in the human brain from MRI with physics-informed neural networks"
<p>Data analyzed in Zapf <em>et al.</em> <a href="https://www.nature.com/articles/s41598-022-19157-w">Investigating molecular transport in the human brain from MRI with physics-informed neural networks</a> (Scientific Reports 2022).</p> <p>The data consists of CSF tracer concentrations in the brain subregions analyzed in the article. The data was pre-processed as described in S1.1 in the supplementarty materials. </p> <p>In Python, load the data as numpy arrays using the nibabel package as</p> <pre><code>import nibabel data = nibabel.load("068/concentrations/24h.mgz").get_fdata() domain_mask = nibabel.load("068/masks/roi.mgz").get_fdata().astype(bool)<br>And view slices of the data as:</code></pre> <div> <div><span>plt</span><span>.</span><span>figure</span><span>()</span></div> <div><span>plt</span><span>.</span><span>imshow</span><span>(</span><span>np</span><span>.take(</span><span>data</span><span>, </span><span>150</span><span>, </span><span>0</span><span>), </span><span>vmax</span><span>=</span><span>0.1</span><span>)</span></div> <div><span>plt</span><span>.</span><span>figure</span><span>()</span></div> <div><span>plt</span><span>.</span><span>imshow</span><span>(</span><span>np</span><span>.take(</span><span>data</span><span>, </span><span>100</span><span>, </span><span>1</span><span>), </span><span>vmax</span><span>=</span><span>0.1</span><span>)</span></div> <div><span>plt</span><span>.</span><span>figure</span><span>()</span></div> <div><span>plt</span><span>.</span><span>imshow</span><span>(</span><span>np</span><span>.take(</span><span>data</span><span>, </span><span>100</span><span>, </span><span>2</span><span>), </span><span>vmax</span><span>=</span><span>0.1</span><span>)</span></div> <div><span>plt</span><span>.</span><span>show</span><span>()</span></div> </div> <pre> </pre>
Fig. 9 Single gene trees and haplotype networks. a, b in Two new bioluminescent Henlea from Siberia and lack of molecular support for Hepatogaster (Annelida, Clitellata, Enchytraeidae)
Fig. 9 Single gene trees and haplotype networks. a, b Gene-trees (a COI, b H3) estimated with Bayesian coalescent analysis in BEAST. Numbers above branches are posterior probabilities. Scales show expected numbers of substitution per site. The specimen shaded in red has a conflict between morphology and COI data. c, d Statistical parsimony
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.