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11,869 results for “metabolic”
Kuparuk River Whole Stream Metabolism Arctic LTER, Toolik Field Station Alaska 2012-2017
The Kuparuk River has been the central research location on the impact of added phosphorus to arctic streams. Additions of phosphorus occred since 1983. Today, 4 specific reaches show certain characteristics based on the years that they recieved fertilization. Whole Stream Metabolism is a way to quantify primary production of this stream system. Calculations were done using dissolved oxygen, discharge, stage, light and temperature measured by sondes and other equipment strategically deployed in the field at locations to quantify each of the unique stream reaches.
Caribou-Poker Creeks Research Watershed: Input data for calculating stream metabolism from 2021-2022
This dataset contains the input data needed for calculating stream metabolism using the 'streamMetabolizer' R package (i.e., dissolved oxygen, oxygen at 100% saturation, depth, water temperature, light, and discharge) for four sites in the Caribou-Poker Creeks Research Watershed (CPCRW). Data are reported at 15-minute intervals from May to September in 2021 and 2022.
North Temperate Lakes LTER: High Frequency Meteorological and Metabolism Data - Crystal Bog Buoy 2005 - present
The instrumented buoy on Crystal Bog is equipped with a dissolved oxygen sensor, a thermistor chain, light sensor, and in the past, meteorological sensors that provide fundamental information on lake thermal structure and lake metabolism. A surface buoy was used from 2005 - 2014, which included a met station beginning 2006. Since then, only a subsurface buoy as been used (no met station), and includes frequent over-winter deployments under the ice. The thermistor chain data is included in a separate dataset (knb-lter-ntl.119). Hourly and daily averages are provided for the met data, while hourly averages are provided for the dissolved oxygen and light data. To accommodate the under-ice deployments, sensor depths vary somewhat year-to-year. Light data is collected with HOBO pendant light and temperature sensors. The make of the dissolved oxygen sensor has changed over the years: YSI (2005), D-Opto (2006-2014), and PME miniDOT (2015-present).
North Temperate Lakes LTER: High Frequency Meteorological and Metabolism Data - Trout Bog Buoy 2003 - present
The instrumented buoy on Trout Bog is equipped with a dissolved oxygen sensor, a thermistor chain, light sensor, and in the past, meteorological sensors that provide fundamental information on lake thermal structure and lake metabolism. A surface buoy was used from 2003 - 2014, which included a met station (air temp and winds). Since then, only a subsurface buoy as been used (no met station), and includes frequent over-winter deployments under the ice. The thermistor chain data is included in a separate dataset (knb-lter-ntl.70). Hourly and daily averages are provided for the met data, while hourly averages are provided for the dissolved oxygen and light data. To accommodate the under-ice deployments, sensor depths vary somewhat year-to-year. Light data is collected with HOBO pendant light and temperature sensors. The make of the dissolved oxygen sensor has changed over the years: Greenspan DO (2003-2005), D-Opto (2006-2014), and PME miniDOT (2015-present).
Coastal marine megaripple fields are metabolic hotspots - Data for figures and tables
This dataset contains the data used to create figures 2-5 in the paper: Berg, P. in press. Coastal marine megaripple fields are metabolic hotspots with highly dynamic oxygen exchange. L&O Letters (publication expected in 2023). Details on the nature of the data and how it was collected are included in the paper.
Metabolic recovery and compensatory shell growth of juvenile Pacific geoduck Panopea generosa following short-term exposure to acidified seawater
<p><strong>METABOLIC RECOVERY AND COMPENSATORY SHELL GROWTH OF JUVENILE PACIFIC GEODUCK <em>PANOPEA GENEROSA</em> FOLLOWING SHORT-TERM EXPOSURE TO ACIDIFIED SEAWATER</strong></p> <p><strong>Samuel J. Gurr<sup>1*</sup>, Brent Vadopalas<sup>2</sup>, Steven B. Roberts<sup>3</sup>, Hollie M. Putnam<sup>1</sup></strong></p> <p><sup>1 </sup>University of Rhode Island, College of the Environment and Life Sciences, 120 Flagg Rd, Kingston, RI 02881 USA</p> <p><sup>2 </sup>University of Washington, Washington Sea Grant, 3716 Brooklyn Ave NE, Seattle, WA 98105 USA</p> <p><sup>3 </sup>University of Washington, School of Aquatic and Fishery Sciences, 1122 NE Boat St, Seattle, WA 98105 USA</p> <p><strong>*Corresponding author:</strong> Fax: Phone:1-401-874-9510 Email: samuel_gurr@uri.edu</p> <p><strong>Abstract</strong></p> <p>While acute stressors can be detrimental, environmental stress conditioning can improve performance. To test the hypothesis that physiological status is altered by stress conditioning, we subjected juvenile Pacific geoduck, <em>Panopea generosa, </em>to repeated exposures of elevated <em>p</em>CO<sub>2</sub> in a commercial hatchery setting followed by a period in ambient common garden. Respiration rate and shell length were measured for juvenile geoduck periodically throughout short-term repeated reciprocal exposure periods in ambient (~550 µatm) or elevated (~2400 µatm) <em>p</em>CO<sub>2</sub> treatments and in common, ambient conditions, five months after exposure. Short-term exposure periods comprised an initial 10-day exposure followed by 14 days in ambient before a secondary 6-day reciprocal exposure. The initial exposure to elevated <em>p</em>CO<sub>2 </sub>significantly reduced respiration rate by 25% relative to ambient conditions, but no effect on shell growth was detected. Following 14 days in common garden, ambient conditions, reciprocal exposure to elevated or ambient <em>p</em>CO<sub>2</sub> did not alter juvenile respiration rates, indicating ability for metabolic recovery under subsequent conditions. Shell growth was negatively affected during the reciprocal treatment in both exposure histories, however clams exposed to the initial elevated <em>p</em>CO<sub>2</sub> showed compensatory growth with 5.8% greater shell length (on average between the two secondary exposures) after five months in ambient conditions. Additionally, clams exposed to the secondary elevated <em>p</em>CO<sub>2 </sub>showed 52.4% increase in respiration rate after five months in ambient conditions. Early exposure to low pH appears to trigger carry-over effects suggesting bioenergetic re-allocation facilitates growth compensation. Life stage-specific exposures to stress can determine when it may be especially detrimental, or advantageous, to apply stress conditioning for commercial production of this long-lived burrowing clam.</p> <p> </p>
Genome-scale metabolic model of Quercus suber
<p>Genome-scale metabolic model of Quercus suber in SBML Level 3 Version 2 format. This model was reconstructed using <em>merlin</em> (https://merlin-sysbio.org), an open-source software.</p>
Energy metabolic pattern of China and EU28 between 2000-2016 from subsectors to average societal levels.
<p>This repository contains the data needed to reproduce the results in:</p> <p>Velasco-Fernández, R., Pérez-Sánchez, L., Chen, L., Giampietro, M., 2020. A becoming China and the assisted maturity of the EU: Assessing the factors determining their energy metabolic patterns. Energy Strategy Reviews. 32, 100562. https://doi.org/10.1016/j.esr.2020.100562</p>
Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site
<p>Metabolomics dataset used in the publication "Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site"</p> <p>Jaanika Kronberg, Jonathan J. Byrne, Jeroen Jansen, Philipp Antczak, Adam Hines, John Bignell, Ioanna Katsiadaki, Mark R. Viant and Francesco Falciani </p> <p>Metabolomics dataset for metabolic bins 1 to 1045 for 376 mussels as used in the publication.</p> <p>Mussel metadata are described in a separate file (spectrum number, sample label, sex, site, species, month, temperature of water, salinity of water, ADG rate, gonadal stage, parasite load)</p> <p>Species 1: Mytilus edulis, species 2: hybrid, species 3: Mytilus galloprovincialis</p>
Suplementary data, results and scripts: "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer"
<p>This repository contains supplementary data, models and scripts associated with "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer".</p><p>Folders content:</p><p>'data_results_matlabscripts': data, result files and scripts (original python scripts and adapted MATLAB scripts)</p><p>'supplementary_figures': supplementary figures</p><p>'supplementary_tables': supplementary tables</p>
Supplementary Material 4 (Spatio-temporal metabolic rifts in urban construction material circularity)
<p>This video map shows the relative changes in environmental impacts at different locations per year for 2017-2050.</p>
Supplementary Material 1-3 (Spatio-temporal metabolic rifts in urban construction material circularity)
<p>Dataset 1 contains life cycle impact factors used for each stage of the urban metabolism for all spatial levels.</p> <p>Dataset 2 contains the transport distances and modes from supplier locations for all spatial levels.</p> <p>Dataset 3 contains material flow and life cycle impact assessment results for each year (2017-2050) and all spatial levels.</p>
Distributable, Metabolic PET Reporting of Tuberculosis
<p>Tuberculosis remains a large global disease burden for which treatment regimens are protracted and monitoring of disease activity difficult. Existing detection methods rely almost exclusively on bacterial culture from sputum which limits sampling to organisms on the pulmonary surface. Advances in monitoring tuberculous lesions have utilized the common glucoside [18 F]FDG, yet lack specificity to the causative pathogen Mycobacterium tuberculosis (Mtb) and so do not directly correlate with pathogen viability. Here we show that a close mimic that is also positron-emitting of the non-mammalian Mtb disaccharide trehalose – 2-[ 18 F]fluoro-2-deoxytrehalose ([18 F]FDT) – is a mechanism-based reporter of Mycobacteria-selective enzyme activity in vivo. Use of [18 F]FDT in the imaging of Mtb in diverse models of disease, including non-human primates, successfully co-opts Mtb-specific processing of trehalose to allow the specific imaging of TB-associated lesions and to monitor the effects of treatment. A pyrogen-free, direct enzyme-catalyzed process for its radiochemical synthesis allows the ready production of [18 F]FDT from the most globally-abundant organic 18F-containing molecule, [18 F]FDG. The full, pre-clinical validation of both production method and [18 F]FDT now creates a new, bacterium selective, clinical diagnostic candidate for clinical evaluation. We anticipate that this distributable technology to generate clinical-grade [18 F]FDT directly from the widelyavailable clinical reagent [18 F]FDG, without need for either custom-made radioisotope generation or specialist chemical methods and/or facilities, could now usher in global, democratized access to a TB-specific PET tracer.</p>
Systems-level design principles of metabolic rewiring in an animal
<p><strong>Description: </strong></p> <p>This repository contains all source codes, necessary input and output data, and raw figures and tables for reproducing all figures and results published in the following study: </p> <p>Xuhang Li#, Hefei Zhang#, Thomas Hodder, Wen Wang, L. Safak Yilmaz, Chad L. Myers, Albertha J.M. Walhout. Systems-level Worm Perturb-Seq reveals design principles of metabolic rewiring. (2025)<em> Nature, </em>in press (# equal contribution)</p> <p><strong>Files:</strong></p> <p>This repository contains the entire project working directory for this publication. To deposit into Zenodo, we have individually zipped each subfolder of the root directory. To reproduce our study, it is advised that one should download the entire Zenodo repository and unzip every zip file to produce the corresponding subfolders. The "root.zip" should be directly unzipped into the root directory of this project (not your system root!!). </p> <p>The recovered working directory should be like this: </p> <ul> <li><strong>10_revision</strong>/</li> <li><strong>1_QC_dataCleaning</strong>/</li> <li><strong>2_DE</strong>/</li> <li><strong>3_imageAnalysis</strong>/</li> <li><strong>4_networkAnalysis</strong>/</li> <li><strong>5_GSA</strong>/</li> <li><strong>7_FBA_modeling</strong>/</li> <li><strong>data_and_tables_to_publish</strong>/</li> <li><strong>input_data</strong>/</li> <li>generate_all_tables_for_publication.R</li> <li>prepareTbls_met12.R</li> <li>prepareTbls_metVali1_OCC.R</li> <li>prepareTbls_metVali2_b12.R</li> <li>prepareTbls_metVali3_detox.R</li> <li>prepareTbls.R</li> <li>prepareTbls_SPECIAL.R</li> <li>REWIRING_FIGURE_LOOKUP.xlsx</li> </ul> <p>Please be advised that this repository contains raw codes and data that are not directly related to a figure in our paper. However, they may be useful to generate input used in the analysis of a figure, or to reproduce tables in our manuscript. It may also contain unpublished analyses and figures, which we did not intentionally delete and kept for records. </p> <p><strong>Usage: </strong></p> <p>Please refer to the table in below to locate a specific file for reproducing a figure of interest (also availabe in the <em>REWIRING_FIGURE_LOOKUP.xlsx </em>under the root directory). </p> <table> <tbody> <tr> <td>Figure</td> <td>File</td> <td>Lines<sup>a</sup></td> <td>Notes</td> </tr> <tr> <td>Fig. 1b</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>1-190</td> <td> </td> </tr> <tr> <td>Fig. 1c</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-83</td> <td> </td> </tr> <tr> <td>Fig. 1d</td> <td>11_additional analysis/special RNAi.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 1e</td> <td>2_DE/output/metabolic_GRN_layout2.cys</td> <td>open in Cytoscape</td> </tr> <tr> <td>Fig. 1f</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-71</td> <td> </td> </tr> <tr> <td>Fig. 1g</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-145</td> <td> </td> </tr> <tr> <td>Fig. 2a</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>1-235</td> <td> </td> </tr> <tr> <td>Fig. 2b</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>232-287</td> <td> </td> </tr> <tr> <td>Fig. 2c</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>468-765</td> <td>For producing the embedding, see "2_DE/clustering_python/cluster_RNAi_UMAP_DBSCAN.ipynb"</td> </tr> <tr> <td>Fig. 2d</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-181</td> <td> </td> </tr> <tr> <td>Fig. 2e</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-338</td> <td> </td> </tr> <tr> <td>Fig. 2f</td> <td>2_DE/6_2_rewiring_summary_map_PCCtree.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 3b</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2539</td> <td> </td> </tr> <tr> <td>Fig. 3c</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2506</td> <td>the published figure uses level 4 and up direction (2_DE/figures/1_pathway_level_analysis/rxn_and_pathway_rewiring_LEVEL4_up.pdf)</td> </tr> <tr> <td>Fig. 3d</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2183-2235</td> <td> </td> </tr> <tr> <td>Fig. 3e</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>1751-2159</td> <td> </td> </tr> <tr> <td>Fig. 3g</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>803-807</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Fig. 3h</td> <td>11_additional analysis/glycine tracing/glycine RNAi tracing.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Fig. 3j</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>1-601</td> <td> </td> </tr> <tr> <td>Fig. 3k</td> <td>4_networkAnalysis/1_rewiring_network_modules.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 4a</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>853-881</td> <td>run lines 1-100 to load dependency; Euler plot depends on a random seeds; we manually picked one output figure that has best visual representations; see the corresponding Github or "7_FBA_modeling/CR_model_final_run/a1_gene_obj_classification.m" for generating the input matrix (core function matrix)</td> </tr> <tr> <td>Fig. 4b</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_gspd_1_example_objHeatmap.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 4c</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>1-270</td> <td> </td> </tr> <tr> <td>Fig. 4d</td> <td>7_FBA_modeling/CR_model_final_run/3_DEG_modeling_edge_direction_test_randFBA.R, 7_FBA_modeling/CR_model_final_run/3_DEG_modeling_edge_direction_test_randGRN.R</td> <td>entire file</td> <td>Reproducing this figure is complex because it integrates multiple randomization results; search "fitted_CR_interaction_diagram.pdf" or "fitted_CR_interaction_diagram_randGRN.pdf" to locate the code for making the figure (two randomizations, respectively), and track up for a few lines to load the dependency. To fully reproduce the analysis, follow the instructions in the Github and you will need to execute all four files named "3_DEG_modeling_edge_*.R".</td> </tr> <tr> <td>Fig. 4f</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-154</td> <td>numbers are in variable "model_explained"</td> </tr> <tr> <td>Fig. 4g (left)</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 342-345</td> </tr> <tr> <td>Fig. 4g (right)</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_parameter_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 5a</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Fig. 5b</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/3_DEG_modeling_edge_direction_test_randFBA.R</td> <td>entire file and see notes</td> <td>Reproducing this figure is complex because it integrates multiple randomization results; search "fitted_CR_interaction_diagram.pdf" to locate the code for making the figure, and track up for a few lines to load the dependency. To fully reproduce the analysis, follow the instructions in the Github and you will need to execute all two files named "3_DEG_modeling_edge_*.R".</td> </tr> <tr> <td>Fig. 5c</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 343-346</td> </tr> <tr> <td>Fig. 5d</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_DEG_cutoff_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1a</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>193-219</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1b</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>1-91</td> <td>the published figure was remade manually for prettier representations, the plotting function for which is not provided in this code. However, the raw data stays the same.</td> </tr> <tr> <td>Extended Data Fig. 1c</td> <td>2_DE/SUPP_extra_figures_for_rewiring.R</td> <td>280-379</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1d</td> <td>4_networkAnalysis/s2_rewiring_and_network_GPR.R</td> <td>387-555</td> <td>some inputs were generated by "4_networkAnalysis/s2_rewiring_and_network_GPR.m"</td> </tr> <tr> <td>Extended Data Fig. 1e</td> <td>4_networkAnalysis/s2_rewiring_and_network_GPR.R</td> <td>387-555</td> <td>some inputs were generated by "4_networkAnalysis/s2_rewiring_and_network_GPR.m"</td> </tr> <tr> <td>Extended Data Fig. 1f</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>170-257</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1g</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>170-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1h</td> <td>11_additional analysis/Wormsize/plot for publishing.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1i</td> <td>11_additional analysis/Wormsize/plot for publishing.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2a</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-61</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2b</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-180</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2c</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-152</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2d</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>364-397</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2e</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>364-383</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2f</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-264</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2g</td> <td>5_GSA/1_DE_annotation_pipeline.R</td> <td>1389-1522</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 3</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>468-765</td> <td>For producing the embedding, see "2_DE/clustering_python/cluster_RNAi_UMAP_DBSCAN.ipynb"</td> </tr> <tr> <td>Extended Data Fig. 4a</td> <td>4_networkAnalysis/s3_rewiring_and_network_connections.R</td> <td>1-254</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4b</td> <td>4_networkAnalysis/s3_rewiring_and_network_connections.R</td> <td>258-303</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4c</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-283</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4d</td> <td>2_DE/SUPP_extra_figures_for_rewiring.R</td> <td>725-813</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4e</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-589</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4f</td> <td>11_additional analysis/regulators_nhr_sbp_tor_GEP_mGRN.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 5a</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-224</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 5b</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-92</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6a</td> <td>2_DE/6_1_biCluster_heatmaps_PCCtree.R (top row); 2_DE/6_2_rewiring_summary_map_PCCtree.R (bottom row)</td> <td>top row: 210-219; 229-238; bottom row: 1-162; 162-229</td> <td>These two scripts produced the last two figures in the first row and the first two figures in the second row. The remaining two figures are repetitive with other main/extended data figures, whose source code were specified there</td> </tr> <tr> <td>Extended Data Fig. 6b</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>1-184</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6c</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>189-319</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6d</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>326-480</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 7a-e</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-189</td> <td>The annotation bar plot is in 2_DE/SUPP_extra_figures_for_rewiring.R lines 394-715</td> </tr> <tr> <td>Extended Data Fig. 8a</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>649-753</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8b</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>649-785</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8c</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>759-764</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8d</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2506</td> <td>the published figure uses level 4 and up direction (2_DE/figures/1_pathway_level_analysis/rxn_and_pathway_rewiring_LEVEL4_up.pdf)</td> </tr> <tr> <td>Extended Data Fig. 8e</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>803-807</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 8f</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>870-874</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 8g</td> <td>11_additional analysis/uPhe tracing/uphe RNAi.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Extended Data Fig. 9a</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>1-601</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 9b</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>691-714</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 9c</td> <td>11_additional analysis/uGlucose tracing/uGlu rewiring.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Extended Data Fig. 9d</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>607-629</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 9e</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>636-685</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 9f</td> <td>4_networkAnalysis/1_rewiring_network_modules.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 10a</td> <td>2_DE/6_2_rewiring_summary_map_PCCtree.R</td> <td>entire file</td> <td>This figure is a subset of fig. 2f</td> </tr> <tr> <td>Extended Data Fig. 10c</td> <td>7_FBA_modeling/REVISION/1_cluster_CR_model.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11a</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_excludeMultiObjGenes.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11b</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-225</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11c</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 337-340</td> </tr> <tr> <td>Extended Data Fig. 11d</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 352-355</td> </tr> <tr> <td>Extended Data Fig. 11e</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 362-365</td> </tr> <tr> <td>Extended Data Fig. 11f</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>486-742</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12a</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>300-329</td> <td>run lines 1-101 to load dependency; Euler plot depends on a random seeds; we manually picked one output figure that has best visual representations; see the corresponding Github or "7_FBA_modeling/REVISION/human_perturb_seq/a1_gene_obj_classification.m" for generating the input matrix (core function matrix)</td> </tr> <tr> <td>Extended Data Fig. 12b</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/1_EDA_of_human_perturb_seq.R</td> <td>1-176</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12c</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12d</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12e</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-224</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12f</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 337-341</td> </tr> <tr> <td>Extended Data Fig. 12g</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 353-356</td> </tr> <tr> <td>Extended Data Fig. 12h</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 363-366</td> </tr> <tr> <td>Extended Data Fig. 12i</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_parameter_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2a</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>467-572</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2b</td> <td>2_DE/6_4_Wald_heatmap_each_RNAi_Cluster.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2c</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-389</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2d</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-308</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2e</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-234</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2f</td> <td>12_FLEX_benchmark/run_FLEX_eval.sh</td> <td>entire file</td> <td>the bash scripts calls corresponding R scripts for reproducing the figures. You will need to install the FLEX package to run these codes. </td> </tr> <tr> <td>Supplementary Data Fig. 3, 4</td> <td>N/A</td> <td>N/A</td> <td>No coding involved as these figures were made manually</td> </tr> <tr> <td>Supplementary Data Fig. 5</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>1-270</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 6</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>274-578</td> <td>please load the dependency starting from line 1</td> </tr> </tbody> </table> <p><em><strong>a: The lines indicate the chunk of codes to reproduce the corresponding figure; The figure is reproduced at the end of the referred codes. Please note that you may have to run the codes above the referred chunk (i.e., from the first line) to load dependent variables to execute the referred codes. However, you should be able to reproduce the figure only with the codes within the referred script. </strong></em></p> <p><strong>Notice:</strong></p> <p>We advise you download the entire project working directory (including all zip files and unzip them into corresponding folders), for reproducing any analysis. If you only download the zip file relevant to your figure of interest, you may or may not run into issues due to the missing files in another folder. </p> <p><strong>Contact:</strong></p> <p><strong>For any questions, please contact Xuhang (Hang) Li at Xuhang.Li@umassmed.edu. </strong><strong>(<em>we plan to deposit an updated version with better code annotations, but cannot complete that currently due to time restirctions</em>)</strong></p> <p> </p>
Metabolic alterations in Strongyloidiasis stool samples unveil potential biomarkers of infection_dataset
<p>Strongyloidiasis, a parasitosis caused by <em>Strongyloides stercoralis</em> in humans, is a very prevalent infection in tropical or subtropical areas. Gaps on public health strategies corroborates to the high global incidence of strongyloidiasis especially due to challenges involved on its diagnosis. Based on the lack of a gold-standard diagnostic tool, we aimed to present a metabolomic study for the assessment of stool metabolic alterations. Stool samples were collected from 25 patients segregated into positive for strongyloidiasis (n = 10) and negative control (n = 15) and prepared for direct injection high-resolution mass spectrometry analysis. Using metabolomics workflow, 18 metabolites were annotated increased or decreased in strongyloidiasis condition, from which a group of 5 biomarkers comprising caprylic acid, mannitol, glucose, lysophosphatidylinositol and hydroxy-dodecanoic acid demonstrated accuracy over 89% to be explored as potential markers. The observed metabolic alteration in stool samples indicates involvement of microbiota remodeling, parasite constitution, and host response during <em>S. stercoralis</em> infection.</p>
Spatially resolved metabolic composition in seeds of common bean: comparison of the low phytic acid mutant and the wild type
<p>Common bean (Phaseolus vulgaris L.) seeds are a good source of energy, are rich in proteins and carbohydrates, minerals and vitamins (such as Fe, Zn, B-vitamin), and bioactive compounds, such as polyphenols. However, the presence of some antinutritional compounds, such as phytic acid (PA), which decreases mineral bioavailability, can limit the nutritional value of common beans. Therefore, genotypes with low PA concentrations in common beans have been generated. The increased bioavailability of Fe from LPA mutant seeds compared to the wild type common beans was shown in a stable Fe-isotope absorption study in Swiss women, indicating that the seeds of LPA common bean could be used to help remedy the Fe malnutrition in women. Within this TNA project, we spatially resolved molecular composition in LPA mutant and wild-type common beans, particularly the distribution of PA. In total, three replicates of each genotype were analyzed with MeV-SIMS at RBI. Positive and negative modes were operated for analysis of the samples and of the standard (PA). Best spectra were obtained in negative mode, in which three distinct peaks were observed in the standard (PA): 63 m/z: PO2-, 79 m/z: PO3- and 97 m/z: H2PO4-.</p>
Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
Data set related to "Secretory and metabolic determinants of brain metastasis"
<p>This repository includes the data sets related to the publication titled "Secretory and metabolic determinants of brain metastasis"</p>
Universal metabolic model for Fungi
<p>Universal metabolic model for Fungi. It is a combination of enzymatic reactions collected from literature and reaction databases (Kegg, metacyc, Rhea).</p>
Multi-omic analysis of the Arabidopsis clock activator mutant rve 4 6 8 reveals connections to carbohydrate metabolism and proteasome regulation
<p>Plants are able to sense changes in their light environments, such as the onset of day and night, as well as anticipate these changes in order to adapt and survive. Central to this ability is the plant circadian clock, a molecular circuit that precisely orchestrates plant cell processes over the course of a day. REVEILLE proteins (RVEs) are recently discovered members of the plant circadian circuitry that activate the evening complex and PRR genes to maintain regular circadian oscillation. The RVE 8 protein and its two homologs, RVE 4 and 6, have been shown to limit the length of the circadian period, with rve 4 6 8 triple-knockout plants possessing an elongated period along with increased leaf surface area, biomass, cell size and delayed flowering relative to wild-type Col-0 plants. Here, using a multi-omics approach consisting of phenomics, transcriptomics, proteomics, and metabolomics we draw novel connections between RVE8-like proteins and a number of core plant cell processes. In particular, we reveal that loss of RVE8-like proteins results in altered carbohydrate, organic acid and lipid metabolism, including a starch excess phenotype at dawn. We further demonstrate that rve 4 6 8 plants have lower levels of 20S proteasome subunits and possess significantly reduced proteasome activity, potentially explaining the increase in cell-size observed in RVE8-like mutants. Overall, this robust, multi-omic dataset, provides substantial new insights into the far reaching impact RVE8-like proteins have on the diel plant cell environment.<br> <br> This dataset has the raw search outputs for the mass-spec analysis for this manuscript. </p>
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.