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34 results for “TSS”
Stream sampling for total suspended solids (TSS), volatile suspended solids (VSS), and chemistry during storm events at the Coweeta LTER intensive and hillslope sites in Macon County, NC.
Stream storm samples were collected at 21 streams and rivers in Macon County, NC. Nine intensive sites were monitored in 2010-2011, nine hillslope sites were monitored in 2012-2013, and three river sites were monitored from 2010-2013. An ISCO water sampler was used to collect stream water samples during storm events. Water samples were analyzed at the Coweeta Analytical Lab.
ENCODE LR-RNA-seq TSS BED files
<p>Included are lapa (files just end in .bed) and Cerberus (files end in _cerberus.bed) TSSs for GM12878 and K562 libraries. Also included is a metadata file that goes from experiment ID to dataset name.</p> <p> </p> <p>Lapa TSSs have been filtered for reproducibility between biological replicates</p> <p> </p> <p>Cerberus TSSs are comprised of a subset of Lapa TSSs and have been filtered for expression >= 1 TPM.</p> <p> </p> <p>* Updated 12/19/23 for recompute</p> <p> </p>
Ensembl TSS dataset for GRCh38
<p>We used the human genome reference sequence in its GRCh38.p13 version in order to have a reliable source of data in which to carry out our experiments. We chose this version because it is the most recent one available in Ensemble at the moment. However, the DNA sequence by itself is not enough, the specific TSS position of each transcript is needed. In this section, we explain the steps followed to generate the final dataset. These steps are: raw data gathering, positive instances processing, negative instances generation and data splitting by chromosomes.</p> <p>First, we need an interface in order to download the raw data, which is composed by every transcript sequence in the human genome. We used Ensembl release 104 (Howe et al., 2020) and its utility BioMart (Smedley et al., 2009), which allows us to get large amounts of data easily. It also enables us to select a wide variety of interesting fields, including the transcription start and end sites. After filtering instances that present null values in any relevant field, this combination of the sequence and its flanks will form our raw dataset. Once the sequences are available, we find the TSS position (given by Ensembl) and the 2 following bases to treat it as a codon. After that, 700 bases before this codon and 300 bases after it are concatenated, getting the final sequence of 1003 nucleotides that is going to be used in our models. These specific window values have been used in (Bhandari et al., 2021) and we have kept them as we find it interesting for comparison purposes. One of the most sensitive parts of this dataset is the generation of negative instances. We cannot get this kind of data in a straightforward manner, so we need to generate it synthetically. In order to get examples of negative instances, i.e. sequences that do not represent a transcript start site, we select random DNA positions inside the transcripts that do not correspond to a TSS. Once we have selected the specific position, we get 700 bases ahead and 300 bases after it as we did with the positive instances.</p> <p>Regarding the positive to negative ratio, in a similar problem, but studying TIS instead of TSS (Zhang135<br> et al., 2017), a ratio of 10 negative instances to each positive one was found optimal. Following this136<br> idea, we select 10 random positions from the transcript sequence of each positive codon and label them137<br> as negative instances. After this process, we end up with 1,122,113 instances: 102,488 positive and 1,019,625 negative sequences. In order to validate and test our models, we need to split this dataset into three parts: train, validation and test. We have decided to make this differentiation by chromosomes, as it is done in (Perez-Rodriguez et al., 2020). Thus, we use chromosome 16 as validation because it is a good example of a chromosome with average characteristics. Then we selected samples from chromosomes 1, 3, 13, 19 and 21 to be part of the test set and used the rest of them to train our models. Every step of this process can be replicated using the scripts available in https://github.com/JoseBarbero/EnsemblTSSPrediction.</p>
CONUS Riverine Data Fusion TSS Concentration 2000-2023 Dataset
<div> <div> <h1><strong>Overview</strong></h1> <h3><strong>Available here are the datasets producing 1.8M large river sediment concentrations derived from fused (previously MODIS) and non-fused (Landsat-5,7,8,9 & Sentinel-2) image reflectances made over 1,253 CONUS sites, as validated with over 25,000 in situ measurements across these same sites using machine learning. Satellite super-resolution data fusion ‘teaches’ a coarse resolution sensor (500m) what it would have seen if it were a fine resolution satellite (30m). Datasets include:<br></strong></h3> <p><strong>trainingMatchups_2000-2023.csv -</strong> All Fusion and LS2 matchups within +/- 1 day of Water Quality Portal TSS measurements used to train a machine learning model between 2000-2023.</p> <p><strong>fusion_reflectance_raw.csv -</strong> All raw median reflectance, standard deviation, pixel count, date, and siteID data for fusion images generated between 2000-2023.</p> <p><strong>LS2_reflectance_raw.csv -</strong> All raw median reflectance, standard deviation, pixel count, date, and siteID data for LS2 images generated between 2000-2023.</p> <p><strong>fusionSed_all.csv -</strong> All TSS estimates from the RF model including matchup LS2, matchup Fusion, predicted LS2, and predicted Fusion between 2000-2023.</p> </div> </div>
ML intro course - TSS exercise FASTA file
<p>The input file needed to go through the TSS exercise in Day 3 of the ML intro course hosted by HeaDS at the University of Copenhagen, Denmark.</p> <p>You can find the course material here: <a title="here" href="https://github.com/Center-for-Health-Data-Science/IntroToML/tree/main">https://github.com/Center-for-Health-Data-Science/IntroToML/tree/main</a></p>
Datasets used for TSS-Captur
<p>Three processed datasets that can be used as an input for the pipeline TSS-Captur, containing a MasterTable computed using TSSpredator, the corresponding FASTA file and the annotation file (in form of a GFF file). A short description of the datasets follows:</p> <ul> <li>scoelicolor.zip: Contains the input data used for the report presented in the manuscript for TSS-Captur. The study uses the data by Jeong, <em>et al</em>. (2016), where <em>Streptomyces coelicolor</em> analyzed under different conditions. For more information, refer to the original study. </li> <li>cjejuni.zip: A further dataset for usage in TSS-Captur. Contains the data of a study comparing the transcriptome of four different strains of <em>Campylobacter jejuni</em> (Dugar,<em> et al.</em>, 2013) using TSSpredator to compute a genome-wide TSS map.</li> <li>bacteroides_plasmid.zip: A small dataset based on the study by Ryan, <em>et al</em>. (2020), where the transcriptome of <em>Bacteroides thetaiotaomicron</em> was analyzed. The MasterTable was produced using TSSpredator and very sensitive parameters. </li> </ul> <p> </p> <h2>References: </h2> <p>Dugar, G., Herbig, A., Förstner, K. U., Heidrich, N., Reinhardt, R., Nieselt, K., & Sharma, C. M. (2013). High-resolution transcriptome maps reveal strain-specific regulatory features of multiple Campylobacter jejuni isolates. <em>PLoS genetics</em>, <em>9</em>(5), e1003495.</p> <p>Jeong, Y., Kim, J. N., Kim, M. W., Bucca, G., Cho, S., Yoon, Y. J., ... & Cho, B. K. (2016). The dynamic transcriptional and translational landscape of the model antibiotic producer Streptomyces coelicolor A3 (2). <em>Nature communications</em>, <em>7</em>(1), 11605.</p> <p>Ryan, D., Jenniches, L., Reichardt, S., Barquist, L., & Westermann, A. J. (2020). A high-resolution transcriptome map identifies small RNA regulation of metabolism in the gut microbe Bacteroides thetaiotaomicron. <em>Nature communications</em>, <em>11</em>(1), 3557.</p>
TSS and TSVSR dataset
<p>This is the dataset for 'Prediction of Transition State Scaling Relationships and Universal Transition State Vibrational and Entropic Correlations for Dehydrogenations'. </p>
Detection of PitNET Tissue During TSS Using Bevacizumab-800CW
ClinicalTrials.gov study NCT04212793. IPD Sharing: NO. Countries: 1. Publications: 2.
EMC data of non-point source pollution in key cities in China from 2005 to 2022 (mainly including four pollution indicators: TSS, TN, TP, COD)
Open the record for dataset details and reuse information.
Data for "Relative contribution rates of influencing factors of urban non-point source pollutants (TSS, TN, TP, COD)"
Open the record for dataset details and reuse information.
EMC data of non-point source pollution (TSS, TN, TP, COD) in 41 cities in China from 2005 to 2022
<p>This is a synthesis of existing research data based on meta analysis. It contains event mean concentration (EMC) data on nonpoint source pollution in 41 Chinese cities from 2005 to 2022.</p> <p>If you use the EMC dataset for related scientific research, please cite the corresponding reference (Wang et al., EF, 2025):</p> <ul> <li> <p>Wang, Y., Li, C., Qiao, J., Hu, Y., Zhang, Q., Yin, J., & Slater, L. (2025). Meta‐analysis of urban non‐point source pollution from road and roof runoff across China. Earth's Future, 13, e2024EF005296. <a href="https://doi.org/10.1029/2024EF005296">https://doi.org/10.1029/2024EF005296</a></p> </li> </ul>
Delta-X: AVIRIS-NG L3-derived Water Quality, TSS, and Turbidity, MRD, V3
This dataset includes estimates of total suspended solids (TSS) concentration and turbidity for waters of the Atchafalaya River and Terrebonne Basins of the Mississippi River Delta (MRD) in coastal Louisiana. Estimates were derived from Level 2 (L2) BRDF-corrected imagery from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). AVIRIS-NG imagery was collected from March 27-April 6 (spring) and August 20-25 (fall), 2021, as part of the 2021 Delta-X campaign. Algorithms for TSS and turbidity estimation were developed using in-situ remote-sensing reflectance measured at field sampling stations paired with in-situ measures of turbidity from a water quality probe and TSS from water samples. Using the in-situ data, a partial least squares regression (PLSR) model was developed for each AVIRIS-NG wavelength. A subset of the in-situ data, collected during relatively clear AVIRIS-NG overflights, was held out to validate the PLSR model. The PLSR algorithm was then applied to AVIRIS-NG imagery to retrieve TSS and turbidity across the study area. The measurement units for TSS and turbidity estimates are mg L-1 and Formazin Nephelometric Units (FNU), respectively, and the spatial resolution is 3.8 to 5.4 m as determined by the AVIRIS-NG imagery. The dataset includes binary cloud and water masks. These data quantify the mesoscale (i.e., on the order of 1 ha) patterns of soil accretion that control land loss and gain and predict the resilience of deltaic floodplains under projected relative sea-level rise. Gridded estimates are provided in netCDF format, and regression coefficients are included in a comma-separated values (CSV) file. This is Version 3 of this dataset. All previously released data were updated to the latest available versions.
Comparative TSS-seq analysis among Arabidopsis natural variations revealed genetic stability of genic and non-genic promoters
GEO Series GSE298979. Arabidopsis thaliana. 24 samples. Type: Expression profiling by high throughput sequencing.
TSS-EMOTE, a refined protocol for a more complete and less biased global mapping of transcription start sites in bacterial pathogens.
GEO Series GSE85110. Acinetobacter baumannii; Staphylococcus aureus; Staphylococcus epidermidis; Klebsiella aerogenes. 30 samples. Type: Expression profiling by high throughput sequencing; Other.
Combining TSS-MPRA and sensitive TSS profile dissimilarity scoring to study the sequence determinants of transcription initiation
GEO Series GSE202564. Homo sapiens. 16 samples. Type: Other.
Sequence determinants of human gene regulatory elements, TSS experiments
GEO Series GSE180155. Homo sapiens. 2 samples. Type: Other.
scRCAT-seq: simultaneously profile RNA TSS and TES at single-cell level
GEO Series GSE134311. synthetic construct; Homo sapiens; Mus musculus. 116 samples. Type: Expression profiling by high throughput sequencing.
Transfusion Safety Study (TSS)
ClinicalTrials.gov study NCT00005301. IPD Sharing: Not stated. Countries: 0. Publications: 55.
dDsk2 stabilizes dHP1c binding at TSS [ChIP-Seq]
GEO Series GSE49102. Drosophila melanogaster. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Comparative analysis of regulatory elements between Escherichia coli and Klebsiella pneumoniae by genome-wide transcription start site profiling [TSS-Seq]
GEO Series GSE35821. Klebsiella pneumoniae; Escherichia coli K-12. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
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