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476 results for “footprints”
Ribosome Footprint Density in Human Lymphoblast Cell Lines (WT vs RPL17 Heterozygous c.217-3C>G)
<p><strong><span>Supplementary Information from: </span></strong></p> <p><span>Fellmann, F., Saunders, C., <span>O’Donohue, M.-F., Reid, D. W., McFadden, K. A., Montel-Lehry, N., Yu, C., Fang, M., Zhang, J., Royer-Bertrand, B., Farinelli, P., Karboul, N., Willer, J. R., Fievet, L., Bhuiyan, Z. A., Kleinhenz, A. L. W., Jadeau, J., Fulbright, J., Rivolta, C., Renella, R., Katsanis, N., Beckmann, J. S., Nicchitta, C. V., Da Costa, L., Davis, E. E., Gleizes,<sup> </sup></span>P.-E. </span></p> <p><span>An atypical form of 60S ribosomal subunit in Diamond-Blackfan anemia linked to RPL17 variants</span></p> <p><span> </span></p> <p><strong><span>Data Type: </span></strong></p> <p><span>Processed RNAseq to Measure Ribosome Footprint Density</span></p> <p><span> </span></p> <p><strong><span>Sample information:</span></strong></p> <p><span>Species: Human</span></p> <p><span>Cell type: EBV-transformed lymphoblast cell line (LCL)</span></p> <p><span>N=3 healthy control individuals; WT</span></p> <p><span>N=3 individuals with heterozygous RPL17 <span>c.217-3C>G (1-IV-2, 1-III-3, and 1-III-5); mutant</span></span></p> <p><span> </span></p> <p><strong><span>Table S6 – Ribosome Footprint Density – Gene Level Data</span></strong></p> <p><span>Abbreviations:</span></p> <p><span>Muttrln: <span> </span>mutant translation (ribosome-associated mRNA)</span></p> <p><span>WTtrln: <span> </span>WT translation (ribosome-associated mRNA)</span></p> <p><span>Mutmrna: <span> </span>mutant mRNA (bulk mRNA)</span></p> <p><span>WTmrna: <span> </span>WT mRNA (bulk mRNA)</span></p> <p><span>Mut eff: <span> </span>mutant effect (ribosome-associated mRNA / bulk mRNA)</span></p> <p><span>Wt eff: <span> </span>WT effect (ribosome-associated mRNA / bulk mRNA)</span></p> <p><span> </span></p> <p><strong><span>Table S7 – Ribosome Footprint Density – Gene Ontology Analysis</span></strong></p> <p><span>Abbreviations:</span></p> <p><span>Mut / WT ribo-seq: <span> </span>mutant effect / WT effect</span></p> <p><span> </span></p> <p><strong><span>Methods:</span></strong></p> <p><span>Ribosome profiling was performed essentially as described </span><span>(1-3)</span><span>. For LCLs, 5.10<sup>6</sup> cells were collected by centrifugation, then lysed in 250 µl 200 mM KOAc, 15 mM MgCl<sub>2</sub>, 25 mM K-HEPES pH 7.2, 4 mM CaCl<sub>2</sub>, 2% dodecylmaltoside. For samples where RNA-seq was performed, 50 µl of the lysate was set aside and RNA was extracted using GT/phenol </span><span><span>(4)</span></span><span>. With the remaining lysate, the sample was diluted 1:1 with water, then micrococcal nuclease (Sigma-Aldrich) was added to a final concentration of 20 µg/ml. The sample was incubated for 30 min at 37 °C. Ribosomes were then pelleted through a 500 mM sucrose cushion at 90,000 rpm for 40 min in a TLA-100.2 (Beckman-Coulter). the resulting ribosome pellet was resuspended in 200 µl 50 mM NaCl, 50 mM K-HEPES pH 7.2, 5 mM EDTA, 0.5% SDS, 200 ug/ml proteinase K. RNA was extracted by phenol/chloroform, then treated with polynucleotide kinase (New England Biolabs). Ribosome footprints were isolated by polyacrylamide gel electrophoresis, and deep sequencing libraries prepared using the NEBNext Small RNA Library Prep Set (New England Biolabs). RNA-seq libraries were generated using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina (New England Biolabs). All sequencing was performed using either Illumina HiSeq 2500 (for ribosome profiling) or Illumina Genome Analyzer (for RNA-seq). Reads were mapped to the RefSeq transcriptome (<span>RefSeq release 60</span>), mapping to the longest transcript derived from each gene. Reads with more than five valid mapped positions were discarded and as many as two valid mappings were allowed. A 20 nt seed region was used. Following mapping, the position of each ribosome was defined by adding 14 nt to the start of each read. The abundance of ribosomes or of mRNA was determined by the number of coding sequence-mapped reads normalized by the length of the coding sequence and the size of each deep sequencing library. Ribosome density was defined as the number of ribosome footprinting read density divided by RNA-seq read density. Statistical significance of differences in ribosome density was determined by Student’s t-test. Gene ontology analysis was performed by bootstrapping, where the mean log<sub>2</sub> difference in ribosome density was calculated, then compared to random permutations to determine p-value.</span></p> <p><strong><span>References</span></strong></p> <p><span>1. Reid DW, et al. The unfolded protein response triggers selective mRNA release from the endoplasmic reticulum. <em>Cell</em>. 2014;158(6):1362–1374.</span></p> <p><span>2. Reid DW, Nicchitta CV. Primary role for endoplasmic reticulum-bound ribosomes in cellular translation identified by ribosome profiling. <em>J Biol Chem</em>. 2012;287(8):5518–5527.</span></p> <p><span>3. Reid DW, Shenolikar S, Nicchitta CV. Simple and inexpensive ribosome profiling analysis of mRNA translation. <em>Methods</em>. 2015;91:69–74.</span></p> <p><span>4. Stephens SB, et al. Analysis of mRNA partitioning between the cytosol and endoplasmic reticulum compartments of mammalian cells. <em>Methods Mol Biol</em>. 2008;419:197–214.</span></p>
Dataset of global gridded monthly crop coefficient, yearly and monthly blue-to-total water footprint ratio, and national unit blue and green water footprints of maize (2000-2021)
<p>The data includes monthly <span><span>crop coefficient</span></span>, yearly and monthly blue-to-total water footprint ratio at a 5 arcminute spatial scale, and the unit water footprint at an annual national (regional) scale of global maize.</p>
RBP Footprint Grand Challenge: An evaluation of novel computational approaches to RNA-binding protein target prediction from structural data
GEO Series GSE227455. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
Low-input RNase footprinting to profile RNA translation
GEO Series GSE151986. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing; Other.
Phylogenetic footprinting and transcriptome profiling reveal new roles for two Bacillus cereus two-component systems
GEO Series GSE18523. Bacillus cereus ATCC 14579. 4 samples. Type: Expression profiling by array.
DNaseI Digital Genomic Footprinting from ENCODE/University of Washington [Mouse]
GEO Series GSE40869. Mus musculus. 22 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
The macrophage response to LPS is marked by dynamic nucleosome positioning and increases in nucleosome sensitivity corresponding with immune regulatory factor footprints
GEO Series GSE279622. Homo sapiens. 24 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Data for "Human footprint dominates the distribution, sources, and ecological risk of microplastics in lakes across China"
Open the record for dataset details and reuse information.
Ribosome Footprinting (RiboSeq) analysis of mRNA translation in MEF Wt cells overexpressing METTL1+WDR4
GEO Series GSE149970. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
EnChAMP-seq genomic localization and footprinting of RNA Pol II and NELF-A
GEO Series GSE311646. Homo sapiens. 26 samples. Type: Other.
MESSENGER H XRS 5 REDUCED DATA RECORD (RDR) FOOTPRINTS V1.0
Abstract ======== This data set consists of the MESSENGER XRS reduced data record (RDR) footprints which are derived from the navigational meta-data for each calibrated data record (CDR) whose FOV_STATUS is 1 or 3; that is, when the field of view intersects the planet and is either partially or entirely sunlit. Each XRS observation results in four X-ray spectra. When an X-ray interacts with one of the four detectors, a charge or voltage pulse is generated. This signal is converted into one of 2^8 (256) channels, which are correlated to energy. Over a commanded integration time period a histogram of counts as a function of energy (channel number) is recorded. The EDRs are the number of events in each channel of the four detectors accumulated over the integration period. Channels above or below the useful energy range of the detectors are not transmitted. The result is three 244-channel GPC histograms and one 231-channel solar monitor histogram, each of which is designated as a single X-ray spectrum. Each observation is calibrated and processed into the CDR data set. For each CDR whose field of view is contained or partially contained on the planetary surface, a footprint is computed that corresponds to the perimeter of the planetary region within the instrument field of view during the integration time of the observation.
Supplemental data and code for Material Footprints for Investment and Consumption Reveal a Different Development Path in China
<p>Supplemental data and code for Material Footprints for Investment and Consumption Reveal a Different Development Path in China</p>
Waveform data for 'Seismic Footprints Monitoring and Trajectory Tracking of Moving Aircrafts'
<p>Data of waveform and flights for 'Seismic Footprints Monitoring and Trajectory Tracking of Moving Aircrafts'</p>
GEDI L1B Geolocated Waveform Data Global Footprint Level V001
GEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved [GEDI01_B Version 2](https://doi.org/10.5067/GEDI/GEDI01_B.002) data product.The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station and collects data globally between 51.6 degrees N and 51.6 degrees S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date.The GEDI Level 1B Geolocated Waveforms product (GEDI01_B) provides geolocated corrected and smoothed waveforms, geolocation parameters, and geophysical corrections for each laser shot for all eight GEDI beams. GEDI01_B data are created by geolocating the GEDI01_A raw waveform data. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI01 B data product contains 83 variables for each of the eight beams including the geolocated corrected and smoothed waveform datasets and parameters and the accompanying ancillary, geolocation, and geophysical correction. Additional information can be found in the GEDI L1B Product Data Dictionary.Known Issues* Known Issues: Section 6.1 of the User Guide provides additional information on known issues.* Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
GEDI L2A Elevation and Height Metrics Data Global Footprint Level V001
GEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved [GEDI02_A Version 2](https://doi.org/10.5067/GEDI/GEDI02_A.002) data product.The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station and collects data globally between 51.6 degrees N and 51.6 degrees S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date.The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters. The GEDI02_A data product contains 156 variables for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (describing canopy vertical structure, for example), and many other interpreted products from the return waveforms. Additional information for the variables can be found in the GEDI Level 2A Dictionary.Known Issues* Known Issues: Section 7 of the User Guide provides additional information on known issues.* Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V001
GEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved GEDI02_B Version 2 (https://doi.org/10.5067/GEDI/GEDI02_B.002) data product.The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station and collects data globally between 51.6 degrees N and 51.6 degrees S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date.The purpose of the GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B) is to extract biophysical metrics from each GEDI waveform. These metrics are based on the directional gap probability profile derived from the L1B waveform. Metrics provided include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). The GEDI02_B product is provided in HDF-5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI02_B data product contains 96 variables for each of the eight-beam ground transects (or laser footprints located on the land surface). Variables provided include precise latitude, longitude, elevation, height, canopy cover, and vertical profile metrics. Additional information for the variables can be found in the GEDI Level 2B Data Dictionary.Known Issues* Known Issues: Section 7 of the User Guide provides additional information on known issues.* Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
ScienceDex guides
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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.