Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
812
datasets available to search
ShareScore release 0.9.0
Dataset results
812 results for “Target identification”
Extended data for Manuscript: Identification of potential biological targets of oxindole scaffolds via in silico repositioning strategies
<p>This is the Extended Data for the manuscript "<strong>Identification of potential biological targets of oxindole scaffolds via <em>in silico</em> repositioning strategies" </strong>submitted to F1000 Research.</p> <p>Extended Data include a list of all the accession codes as mentioned in the text, the results of 2D fingerprint-based similarity analyses and ligand-protein complexes predicted by rigid docking and Induced Fit Docking calculations.</p>
SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae
<p><strong>Parameters</strong></p><p>Force field: AMBER ff19SB</p><p>Water type: TIP3P</p><p>Ions: NaCl </p><p>Ligand topology force field: GAFF2</p><p>Temperature: 298k</p><p>Pressure: 1 bar</p><p>minimization step: 20000 on 5 nanoseconds</p><p>initial velocity is changed by changing "ntx" and "ig"</p><p>C2: ntx = 5 , ig = 8</p><p>C3: ntx = 2 , ig = 5</p><p> </p><p><strong>Uploads</strong>- </p><p>1. Zip file of all 3 main files</p><p>2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>3. Zip file of C1</p><p>4. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>5. Zip file of C2</p><p>6. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>7. Zip file of C3</p><p>8. Zip and unzip file of <strong>Initial</strong> PDB of complex prior to MD simulation with <strong>Post</strong> MD PDB (C1, C2, C3)</p><p>9. Zip file of <strong>topology</strong> files for C1, C2, and C3</p>
Identification and characterization of ATOH7-regulated target genes and pathways in human neuroretinal development
<p>The files presented here represent the original data collected during the study presented in the Cells (MDPI) publication "Identification and characterization of ATOH7-regulated target genes and pathways in human neuroretinal development" (<a href="https://doi.org/10.3390/cells13131142">https://doi.org/10.3390/cells13131142</a>). The data include:</p> <ul> <li>scRNA sequencing data (matrix, features and barcodes)</li> <li>RNA sequencing data (fastq)</li> <li>CUT&RUN sequencing data (raw data: fastq; coverage: bigWig)</li> <li>RNA sequencing alignments, with custom built reference (RNA_alignments)</li> </ul>
Supporting data for "Quality control for the target decoy approach for peptide identification"
<p>Supporting data for the manuscript "Quality control for the target decoy approach for peptide identification". Input files are raw mass spectrometry runs, to be downloaded from the PRIDE Archive (https://www.ebi.ac.uk/pride/), which can be processed with the parameter files, Nextflow workflow, and Python scripts in "workflow-scripts-parameters.zip". The resulting output files that were used for the manuscript are provided in search-results.zip.</p>
Supplementary materials to: Nano-Strainer: a workflow for identification of single-copy nuclear loci for plant systematic studies, using target capture kits and Oxford Nanopore long reads
<p>In the paper associated with this dataset, a workflow is presented which enables the identification of single-/low-copy nuclear molecular markers for a plant group of interest, by mining data from a small representative target capture experiment done using a commercial probe kit and Oxford Nanopore long-read sequencing. The proposed pipeline first assesses sequence variability contained in the data from targeted loci and assigns reads to their respective genes, via a combined BLAST/clustering procedure. Cluster consensus sequences are then examined based on four pre-defined criteria presumably indicative for absence of paralogy. This is done by calculating four specialized indices; loci are ranked according to their performance in these indices, and top-scoring loci are considered putatively single- or low-copy. The approach can be applied to any probe set. As it relies on long reads, the contribution also provides template workflows for processing Nanopore-based target capture data. Identified loci can be used for NGS amplicon sequencing. For detection of possibly remaining paralogy in these data, which might occur in groups with rampant paralogy, the long-read assembly tool CANU is employed. The presented workflow can be useful for researchers dealing with reticulate or polyploidization phylogenetic histories in plants.</p> <p>The present dataset contains several documents supplementing the original paper. Its most important elements are a detailed description (alongside two graphical workflow figures) of all methods employed in the study, suitable for reproducing the steps of the workflow and also the wet-lab work. The workflow employs a collection of BASH, Python and R scripts which is available here, together with a detailed account on command line use in Linux. Also, reference sequences for the identified markers can be found as well as sequence alignments derived from the amplicon sequencing.</p>
Supplementary materials to: Nano-Strainer: a workflow for identification of single-copy nuclear loci for plant systematic studies, using target capture kits and Oxford Nanopore long reads
Open the record for dataset details and reuse information.
BIOMETSCO progress presentation 2022: Identification of Novel Biomarkers and Drug Targets for the Detection and Elimination of Occult Metastases in Colon Cancer
<p>This is a recorded talk with head of the BIOMETSCO project - Lasse Sommer Kristensen - where he presents the latest project progress.</p> <p>The talk was given at one of ODIN's (the Open Discovery Innovation Network) Knowledge Sharing Events in May 2022.</p> <p> </p> <p> </p>
Data for: Targeted anatomical and functional identification of antinociceptive and pronociceptive serotonergic neurons that project to the spinal dorsal horn
<p>Spinally-projecting serotonergic neurons play a key role in controlling pain sensitivity and can either increase or decrease nociception depending on physiological context. It is currently unknown how serotonergic neurons mediate these opposing effects. Utilizing virus-based strategies, we identified two anatomically separated populations of serotonergic hindbrain neurons located in the lateral paragigantocellularis (LPGi) and the medial hindbrain, which respectively innervate the superficial and deep spinal dorsal horn and have contrasting effects on pain perception. Our tracing experiments revealed an unexpected high selectivity of serotonergic neurons of the LPGi for transduction with spinally injected AAV2retro vectors while medial hindbrain serotonergic neurons were largely resistant to AAV2retro transduction. Taking advantage of this selectivity, we employed intersectional chemogenetic approaches to demonstrate that activation of the LPGi serotonergic projections decreases thermal sensitivity, whereas activation of medial serotonergic neurons increases sensitivity to mechanical von Frey stimulation. Together these results suggest that there are functionally distinct classes of serotonergic hindbrain neurons that differ in their anatomical location in the hindbrain, their postsynaptic targets in the spinal cord, and impact on nociceptive sensitivity. At least the LPGi neurons give rise to rather global and bilateral projections throughout the rostrocaudal extent of the spinal cord suggesting that they contribute to widespread systemic pain control.</p>
Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease
<p>In the present research, we propose a novel approach, termed the Machine Learning (ML)-Based q-RASAR (quantitative read-across structure-activity relationship) method, for the identification of potential multi-target inhibitors against AD. The q-RASAR effectively combines the principles of both read-across and 2D QSAR approaches. As a result, it is imperative to take into account similarity-related aspects in the process of developing q-RASAR models. In this investigation, we have implemented ML-based q-RASAR modeling against seven major targets (AChE, BuChE, BACE1, 5-HT6, CDK-5 enzymes, Amyloid precursor protein, and Tau aggregation) of AD using the initially selected features in 2D QSAR models for the identifications of novel multitarget inhibitors. The models were individually used to check the applicability domain of a pool of 407270 natural products (NPs) obtained from the COCONUT database (<a href="https://coconut.naturalproducts.net/download">https://coconut.naturalproducts.net/download</a>) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drugs. Furthermore, we have also developed the q-RASAAR (quantitative read-across structure-activity-activity relationship) and selectivity-based q-RASAR models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have applied seven distinct machine learning algorithms to enhance the predictive abilities of q-RASAR and q-RASAAR models. Moreover, we have also developed the univariate q-RASAR model, with the RA function as the primary independent variable. Moreover, molecular docking experiments have been conducted to gain insights into the atomic-level molecular interactions between ligands and enzymes. These observations are then juxtaposed with the structural characteristics obtained from models that elucidate the mechanistic aspects of binding events. These proposed models may serve as valuable tools for pinpointing crucial molecular attributes when designing potential drugs for Alzheimer's therapy through the rational design of multi-target inhibitors.</p>
Liraglutide in Obesity and Diabetes: Identification of CNS Targets Using fMRI
ClinicalTrials.gov study NCT01562678. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Identification of druggable targets from the interactome of the Androgen Receptor and Serum Response Factor pathways in prostate cancer
Open the record for dataset details and reuse information.
Data for: Targeted anatomical and functional identification of antinociceptive and pronociceptive serotonergic neurons that project to the spinal dorsal horn
Open the record for dataset details and reuse information.
Dataset for Identification of small-molecule antagonists targeting the Growth Hormone Releasing Hormone Receptor (GHRHR)
<p><span>Active compound dockings, MD systems’ starting conformations, conformations after 1000ns, topology files and parameter files.</span></p>
Identification of TRP Channels as New Potential Therapeutic Targets in Primary and Secondary Raynaud's Phenomenon.
ClinicalTrials.gov study NCT03211325. IPD Sharing: UNDECIDED. Countries: 1. Publications: 25.
Identification of Biomarkers and Molecular Targets Involved on Intervertebral Disc Degeneration and Discogenic Pain
ClinicalTrials.gov study NCT06349226. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Deep Phenotyping of Hearing Instability Disorders: Cohort Establishment, Biomarker Identification, Development of Novel Phenotyping Measures, and Discovery of Therapeutic Targets
ClinicalTrials.gov study NCT04806282. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Integrins and Protocadherins in Glutamatergic Circuits: Identification of Common Signaling Pathways and Molecular Targets in Anxiety and Major Depressive Disorders (GAPsy)
ClinicalTrials.gov study NCT06167590. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Genotype Expression and Phenotype of Endothelial Cells, Carrying an ACVRL1, ENG or SMAD4 Mutation, in Response to BMP9 for the Identification of New Therapeutic Targets in Hereditary Haemorrhagic Tela
ClinicalTrials.gov study NCT05632484. IPD Sharing: NO. Countries: 1. Publications: 2.
CXCR4 Targeted PET Imaging in the Diagnosis and Identification of Primary Aldosteronism
ClinicalTrials.gov study NCT06183554. IPD Sharing: NO. Countries: 1. Publications: 1.
Toremifene in Desmoid Tumor: Prospective Clinical Trial and Identification of Potential Molecular Targets
ClinicalTrials.gov study NCT02353429. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.