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Dataset results
294 results for “data discovery”
High-Throughput Drug Discovery for a Rare Neurological Disorder: Uncovering a Novel Therapeutic Opportunity for the 19q12 Autism Spectrum Disorder [19q12 neurons baseline data]
GEO Series GSE292758. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Data-Driven Domain Discovery (D4) - Evaluation Datasets
<p>Data used for the evaluation of our data-driven domain discovery algorithm (D4). In our evaluation, we used four different datasets from two repositories - <a href="https://opendata.cityofnewyork.us/">NYC Open Data</a> and <a href="https://opendata.utah.gov/">State of Utah Open data</a>. Both repositories were downloaded using the <a href="http://api.us.socrata.com/api/catalog/v1">Socrata Open Data API</a> on Nov. 22nd 2016 and on Sep. 27th 2019, respectively. The downloads contained 1114 datasets and 1953 datasets, respectively.</p> <p>NYC Open Data contains datasets from NYC agencies such as Department of Education and Department of Finance. Each dataset is labeled using 13 different labels. Based on these labels, we obtained three datasets: (a) Education, (b) Finance (using labels Economy and Finance) and (c) Services which includes all tables that are not in (a) or (b).</p> <p>The published data has been pre-processed using D4 (as described in the <a href="https://github.com/VIDA-NYU/domain-discovery-d4">README</a> file). For our evaluation we only considered columns where the majority of distinct terms are text. The published datasets contain the column metadata file (columns.tsv), the index of unique terms across all datasets in the two repositories (term-index.txt.gz), and the set of equivalence classes derived from the term index (compressed-term-index.txt.gz). We also include files containing terms for 25 ground truth domains that we used to evaluate our algorithm.</p> <p> </p>
Data from: Comprehensive transcriptome analysis of Crocus sativus for discovery and expression of genes involved in apocarotenoid biosynthesis
Background: Crocus sativus stigmas form rich source of apocarotenoids like crocin, picrocrocin and saffranal which besides imparting color, flavour and aroma to saffron spice also have tremendous pharmacological properties. Inspite of their importance, the biosynthetic pathway of Crocus apocarotenoids is not fully elucidated. Moreover, the mechanism of their stigma specific accumulation remains unknown. Therefore, deep transcriptome sequencing of Crocus stigma and rest of the flower tissue was done to identify the genes and transcriptional regulators involved in the biosynthesis of these compounds. Results: Transcriptome of stigma and rest of the flower tissue was sequenced using Illumina Genome Analyzer IIx platform which generated 64,604,402 flower and 51,350,714 stigma reads. Sequences were assembled de novo using trinity resulting in 64,438 transcripts which were classified into 32,204 unigenes comprising of 9853 clusters and 22,351 singletons. A comprehensive functional annotation and gene ontology (GO) analysis was carried out. 58.5 % of the transcripts showed similarity to sequences present in public databases while rest could be specific to Crocus. 5789 transcripts showed similarity to transcription factors representing 76 families out of which Myb family was most abundant. Many genes involved in carotenoid/apocarotenoid pathway were identified for the first time in this study which includes zeta-carotene isomerase and desaturase, carotenoid isomerase and lycopene epsilon-cyclase. GO analysis showed that the predominant classes in biological process category include metabolic process followed by cellular process and primary metabolic process. KEGG mapping analysis indicated that pathways involved in ribosome, carbon and starch and sucrose metabolism were highly represented. Differential expression analysis indicated that key carotenoid/apocarotenoid pathway genes including phytoene synthase, phytoene desaturase and carotenoid cleavage dioxygenase 2 are enriched in stigma thereby providing molecular proof for stigma to be the site of apocarotenoid biosynthesis. Conclusions: This data would provide a rich source for understanding the carotenoid/apocarotenoid metabolism in Crocus. The database would also help in investigating many questions related to saffron biology including flower development.
Data from: Discovery of BbX transcription factor in the patagonian blennie: Exploring expression changes following combined bacterial and thermal stress exposure
<p>Abstract:</p><p>High-Mobility Group (HMG) proteins are involved in different processes such as transcription, replication, DNA repair, and immune response. The role of HMG proteins in the immune response of fish has been studied mainly for HMGB1, where its expression can be induced by the stimulation of viral/bacterial PAMPs and can act as a proinflammatory mediator and as a global regulator of transcription in response to temperature. However, for BbX this role remains to be discovered. In this work, we identified the BbX of <i>E. maclovinus</i> and evaluated the temporal expression levels after simultaneous challenge with <i>P. salmonis</i> and thermal stress. Phylogenetic analysis does not significantly deviate from the expected organismal relationships suggesting orthologous relationships and that BbX was present in the common ancestor of the group. BbX mRNA expression levels were very high in the intestinal tissue of <i>E. maclovinus </i>(foregut, midgut, and hindgut). Nevertheless, the protein levels analyzed by WB showed the highest levels of BbX protein in the liver (constitutive expression). On the other hand, the mRNA expression levels of BbX in the liver of <i>E. maclovinus</i> injected with<i> P. salmonis</i> and subjected to thermal stress showed an increase at days 16 and 20 in all treatments applied at 12 °C and 18 °C. Meanwhile, the protein levels quantified by WB showed a statistically significant increase in the HMG-Bbx at all experimental times (4, 8, 12, 16, and 20 dpi). However, at 4 dpi the HMG-Bbx protein levels were much higher than the other days evaluated. The results suggest that BbX protein may be implicated in the response mechanism to temperature and bacterial stimulation in the foregut, midgut, hindgut, and liver, according to our findings at the level of mRNA and protein. Furthermore, our WB analysis suggests an effect of <i>P. salmonis</i> on the expression of this protein that can be observed in condition C+ 12 °C compared to C- 12 °C. Then, there is an effect of temperature that can be evidenced in the condition AM 18 °C and SM 18 °C, compared to AB 18 °C and SB 18 °C at 4, 8, and 12 dpi. We found not differences in the levels of this protein if the thermal stress is achieved through acclimatization or shock. More research is necessary to clarify the importance of this type of HMG in the immune response and thermal tolerance in fish.</p>
Figures 18 from: Xiudan W, Luan L, Yang Z (2024) Discovery of a new species of Synergus (Hymenoptera, Cynipidae, Synergini) based on morphology and molecular data. ZooKeys 1193: 81-94. https://doi.org/10.3897/zookeys.1193.105756
Figures 18 General appearance of Synergus dilatatus sp. nov. galls on twigs of Quercus variabilis.
Data from: Gene discovery in Gelsemium highlights conserved gene clusters in monoterpene indole alkaloid biosynthesis
[No abstract filled]
Data from: Topological materials discovery by large-order symmetry indicators
Crystalline symmetries play an important role in the classification of band structures, and their richness leads to various topological crystalline phases. On the basis of our recently developed method for the efficient discovery of topological materials using symmetry indicators, we explore topological materials in five space groups, which are diagnosed by large-order symmetry indicators and support the coexistence of several kinds of gapless boundary states in a single compound. We predict many candidate materials; some representatives include Pt3Ge, graphite, XPt3, Au4Ti, and Ti2Sn. As by-products, we also find that AgXF3 and AgAsX are good Dirac semimetals with clean Fermi surfaces. The proposed materials provide a good platform for studying the novel properties emerging from the interplay between different types of boundary states.
Data from: Discovery of potential urine-accessible metabolite biomarkers associated with muscle disease and corticosteroid response in the mdx mouse model for Duchenne
Urine is increasingly being considered as a source of biomarker development in Duchenne Muscular Dystrophy (DMD), a severe, life-limiting disorder that affects approximately 1 in 4500 boys. In this study, we considered the mdx mice—a murine model of DMD—to discover biomarkers of disease, as well as pharmacodynamic biomarkers responsive to prednisolone, a corticosteroid commonly used to treat DMD. Longitudinal urine samples were analyzed from male age-matched mdx and wild-type mice randomized to prednisolone or vehicle control via liquid chromatography tandem mass spectrometry. A large number of metabolites (869 out of 6,334) were found to be significantly different between mdx and wild-type mice at baseline (Bonferroni-adjusted p-value < 0.05), thus being associated with disease status. These included a metabolite with m/z = 357 and creatine, which were also reported in a previous human study looking at serum. Novel observations in this study included peaks identified as biliverdin and hypusine. These four metabolites were significantly higher at baseline in the urine of mdx mice compared to wild-type, and significantly changed their levels over time after baseline. Creatine and biliverdin levels were also different between treated and control groups, but for creatine this may have been driven by an imbalance at baseline. In conclusion, our study reports a number of biomarkers, both known and novel, which may be related to either the mechanisms of muscle injury in DMD or prednisolone treatment.
Data for "Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions"
<p>Data used in "<em>Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions</em>"<br> by F. Pellicani, D. Dal Ben, A. Perali, S. Pilati</p> <p>If you use these data or the python script for your research or other activities, please cite the corresponding journal article.</p> <p> </p> <p>====================</p> <p>Uncompressing the zipped file <em>DataSFUnicam.zip</em> provies the following files and folders:</p> <p><br> <strong>DataSFUnicam/</strong></p> <p> </p> <p> ExperimentalDataPDBFiles/<br> <em>This folder contains 2408 .pdb files of experimental complex structures. The files are named with a univocal code corresponding to the protein-ligand complex.</em></p> <p> </p> <p> ExperimentalDataXLSXFile.xlsx<br> <em>This Excel file reports the experimental protein-ligand chemical information. In the sheet named “Foglio1”, the first column contains the univocal code of the protein-ligand complex, the second column contains the experimentally measured pK_d.</em></p> <p> </p> <p> SyntheticDataPDBFiles/<br> <em>This folder contains the .pdb files of the synthetic complex structures. The .pdb files are grouped in 17 folders according to just as many target proteins. The folders are named after the corresponding protein. Each folder contains the .pdb files for the best position of each protein-ligand pair according to the MOE docking score. The files are named with a univocal code.</em></p> <p> </p> <p> SyntheticDataXLSXFiles/<br> <em> The folder contains 17 Excel files with the chemical information of the synthetic protein-ligand complexes. The files are named after the corresponding target protein. In the sheet named “Foglio1” of each .xlsx file, the first column contains a univocal code of the protein-ligand complex in each conformation, the second column contains an auxiliary numerical code corresponding to the protein-ligand pair, the third column contains the experimentally measured pK_i, and the fourth column contains the docking score provided by the MOE software.</em></p> <p>====================</p> <p>USER GUIDE FOR THE PYTHON SCRIPT</p> <p>Download and uncompress the zipped file "<em>SFUnicam.zip</em>" with a command like "<em>unzip SFUnicam.zip</em>". </p> <p>The following file structure is created:</p> <p><em>SFUnicam/</em></p> <p> <em>ComplexToBePredictedFolder/4ey5_30.pdb <br> MaxAssMatrix.npy<br> my_model<br> devStndSynt.npy<br> mediaSynt.npy<br> UnicamSF13prot.py<br> README.txt</em><br> <br> The subfolder "<em>ComplexToBePredictedFolder/</em>" contains the example PDB file "<em>4ey5_30.pdb</em>".</p> <p>-) To execute the script "<em>UnicamSF13prot.py</em>", Python 3 should be installed with the following libraries and sublibraries:<br> <em>Keras:<br> Regularizers<br> Sequential (keras.models)<br> Conv1D, Dense, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D, AveragePooling1D (keras.layers)<br> Adam (keras.optimizers)<br> Numpy</em><br> <em>Tensorflow</em></p> <p>Operation:<br> -) Copy the .pdb file related to the protein-ligand complex whose affinity is to be predicted in the subfolder “<em>ComplexToBePredictedFolder/</em>”.<br> -) Make sure the following files are in the same folder where the python script is:<br> <em>MaxAssMatrix.npy<br> mediaSynt.npy<br> devStndSynt.npy<br> my_model</em><br> -) Run the code using Python 3 with a command like "<em>python3.x UnicamSF13prot.py</em>".<br> -) Enter the name of the protein-ligand PDB file whose affinity is to be predicted (excluding the extension ".pdb").<br> -) Read the predicted affinity from screen.<br> </p> <p> </p>
Supplementary Data for 'Systematic differences in discovery of genetic effects on gene expression and complex traits'
<p>This repository contains data used for or generated by Mostafavi et al.: <a href="https://doi.org/10.1038/s41588-023-01529-1">https://doi.org/10.1038/s41588-023-01529-1</a></p>
A Biospecimen and Clinical Data Study on Patients for Drug & Biomarker Discovery
ClinicalTrials.gov study NCT01592552. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Characteristics and Treatment Patterns of Patients With Sickle Cell Disease in Globin Research Network for Data and Discovery Registry
ClinicalTrials.gov study NCT06662812. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Gene discovery in Gelsemium highlights conserved gene clusters in monoterpene indole alkaloid biosynthesis
Open the record for dataset details and reuse information.
Data from: Topological materials discovery by large-order symmetry indicators
Open the record for dataset details and reuse information.
Data from: Comprehensive transcriptome analysis of Crocus sativus for discovery and expression of genes involved in apocarotenoid biosynthesis
Open the record for dataset details and reuse information.
Data from: Discovery of potential urine-accessible metabolite biomarkers associated with muscle disease and corticosteroid response in the mdx mouse model for Duchenne
Open the record for dataset details and reuse information.
Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655 (RNA-seq data set)
GEO Series GSE111094. Escherichia coli str. K-12 substr. MG1655. 28 samples. Type: Expression profiling by high throughput sequencing.
High-Throughput Drug Discovery for a Rare Neurological Disorder: Uncovering a Novel Therapeutic Opportunity for the 19q12 Autism Spectrum Disorder [post treatment entrectinib patient data]
GEO Series GSE292760. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Discovery of the p53 targetome in MCF7 cells from RNA-seq and ChIP-seq data
GEO Series GSE47043. Homo sapiens. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Discovery and validation of expression data for the Genomics of Acute Myeloid Leukemia Program at Washington University
GEO Series GSE10358. Homo sapiens. 304 samples. Type: Expression profiling by array.
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.