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373 results for “Nanopore”
Nanopore Translocation Signal
<p>This dataset contains a set of nanopore translocation current traces. It is divided in two parts.</p> <p><strong>Part I:</strong> This part contains artificially generated traces with different levels of background noise (SNR = 4, 2, 1, 0.5, and 0.25)</p> <p>For each noise level, three parameters are varied in data generation:</p> <p>a. Twenty different concentrations of nanoparticles as the analytes (Cnp):</p> <p>0.013, 0.016, 0.020, 0.025, 0.032, 0.040, 0.050, 0.063, 0.080, 0.1, 0.13, 0.16, 0.20, 0.25, 0.32, 0.40, 0.50, 0.63, 0.80, and 1, with the unit of nano-molar, [nM].</p> <p>b. Fifteen different diameters of the nanoparticles (Dnp):</p> <p>3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17, with the unit of nanometer, [nm].</p> <p>c. Five different translocation durations (Duration):</p> <p>0.5, 1.0, 1.5, 3.0, and 5.0, with the unit of millisecond, [ms].</p> <p>In total we have 20*15*5=1500 current traces for each SNR.</p> <p>There are three datasets: training, validation and test. Traces in training datasets are of 20 seconds, traces in validation and test datasets are 10 seconds long. For SNR = 4, training, validation and test datasets are provided. For other SNRs, only test traces are provided.</p> <p><strong>Pert II</strong>: This part contains real experimental datasets, the translocation of Lambda DNA and Streptavidin with 6 current traces each at different bias voltages. Each Lambda DNA trace has 71 seconds, while each Streptavidin trace has 126 seconds. Two truncated pyramid shape nanopores were used in our experiments, one with a side length of 7.5 nm and another 16 nm, both in a 55 nm-thick silicon layer, for DNA and protein streptavidin translocation, respectively. The DNA and streptavidin were dispersed in 500 mM KCl electrolyte with a concentration of 78 pM and 84 nM, respectively.</p> <p><br> </p> <p>Details of artificially generated data and experimental data can be found in our paper:</p> <p>Dario Dematties, Chenyu Wen, Mauricio David Pérez, Dian Zhou, Shi-Li Zhang. Deep learning of nanopore sensing signals using a bi-path network. arXiv:2105.03660.</p> <p><strong>Trained and Validated Models</strong></p> <p>In this data set we also include all the trained and best validated models evaluated in our paper.</p> <p><strong>Nanopore Translocation Detector Trained and Validated Models </strong></p> <p>In this data set we also include all the trained and best validated models evaluated in our newer paper: <em>A Generalized Transformer-Based Pulse Detection Algorithm</em></p>
Evaluating Illumina-, Nanopore-, and PacBio-based genome assembly strategies with the bald notothen, Trematomus borchgrevinki
<p>For any genome-based research, a robust genome assembly is required. <em>De novo</em> assembly strategies have evolved with changes in DNA sequencing technologies and have been through at least three phases: i) short-read only, ii) short- and long-read hybrid, and iii) long-read only assemblies. Each of the phases has their own error model. We hypothesized that hidden scaffolding errors in short-read assembly and erroneous long-read contigs degrade the quality of short- and long-read hybrid assemblies. We assembled the genome of <em>T. borchgrevinki</em> from data generated during each of the three phases and assessed the quality problems we encountered. We developed strategies such as k-mer-assembled region replacement, parameter optimization, and long-read sampling to address the error models. We demonstrated that a k-mer-based strategy improved short-read assemblies as measured by BUSCO while mate-pair libraries introduced hidden scaffolding errors and perturbed BUSCO scores. Further, we found that although hybrid assemblies can generate higher contiguity, they tend to suffer from lower quality. In addition, we found long-read-only assemblies can be optimized for contiguity by sub-sampling length-restricted raw reads. Our results indicate that long-read contig assembly is the current best choice and that assemblies from phase I and phase II were of lower quality.</p>
Source Data for Baldelli et al., Performance of single nanopore and multi-pore membranes for blue energy
<p>HOW TO read and utilized all the files used to create Figure 2 of the manuscript.<br><br>The files are divided in two folders:<br> - Charge: in the "Charge" folder, two files can be found: rt_2nm.log and rt_5nm.log.<br> In these files, 4 tables are reported. Each table represents the ionic currents<br> (I- and I+) versus the surface charge density, for a zero applied external voltage<br> and for different nanopore geometries.<br> In particular, in the rt_2nm.log file, the charge-current curves for the conical<br> and bullet-shaped nanopores with a tip radius (rt) of 2 nm are reported.<br> In rt_5nm.log, charge-current curve for all the geometries and for rt = 5 nm are reported.<br> In principle, these files can be used to evaluate the transference number t-, Eq.(1) and<br> then Eq.(3) and Eq.(4) of the manuscript it is possible to draw all the<br> panels in Fig.2 maintaining the error low. <br> To compute the total current, that is the osmotic current since no external voltage is<br> applied, sum the second and the third column with their sign.<br> In both the files: use the first table per conical nanopore performance; the second table<br> for BS2 nanopore performance; use the third table for BS4 nanopore performance;<br> use the fourth table for BS6 nanopore performance<br> <br> - Voltage: the "Voltage" folder is divided in other two sub-folders rt_2nm and rt_5nm.<br> In this folder the complete I-V curvers for all the surface charge considered in the<br> manuscrip are reported.<br> In rt_2nm (rt_5nm) subfolder the I-V curves for the nanopores characterized<br> by a tip radius (rt) of 2 nm (5 nm) are reported.<br> In each sub-folder the I-V, one file for each surface charge considered can be found.<br> For instance, the file rt2nm_160mCm2.log contains the I-V curves for all the geometries<br> with a surface charge of 160 mC/m^2.<br> In the following, we explained how to obtain each panels of Fig.2 of the main manuscript.<br> Note that the second row of Fig.2 (panels b,d,f and h)<br> can be drawn in the same way of the first row (panels a,c,e and g), hence<br> in the following we explain only how to draw the first rows and the same approach<br> remains valid for the second one.<br> Let's start using files in the rt_2nm sub-folders.<br> - Panel (a): The osmotic current I_o, is the current for a zero applied<br> external voltage.<br> Therefore, this panel can be drawn using the first row of each column for all the files<br> in the sub-folder. Note that in the first row of a table the current for a zero voltage<br> is reported.<br> The second way to draw this panel is using the Charge/rt_2nm.log file.<br> In fact in this file the current for zero voltage and for all the charges is reported.</p> <p> - Panel (c): The membrane potential E_m is the voltage measured across the membrane<br> at zero current condition. Therefore can be obtained through the I-V curve<br> observing at which voltage value the current curve intersects the voltage axis itself.<br> For those files that contain separate ionic and cationic currents, these should be<br> summed to obtain the total current I_tot. If the file only contains tables with a <br> voltage column and I_tot, then the second column can be used directly.</p> <p> - Panel (e): The transference number t_- can be calculated using the Eq.(1).<br> For those files that contain separate ionic and cationic currents, t_- can be<br> calculated using the second (anionic current I-)<br> and third column (cationic current I+) (and Eq.(1)). <br> For those file that contain only I_tot, t- can be calculated by inverting Eq.(3)<br> (considering the activity coefficients equal to 1), having previously calculated<br> the membrane potential E_m (see the previous point).</p> <p> - Panel (g): From the I-V curve, draw the P-V curve and then estimate the maximum power.<br> If the file contains the anionic and cationic current separately, sum these currents<br> to obtain I_tot and then using it to draw the P-V curve.<br> If only I_tot is reported, use it directly to plot the P-V curve.<br> <br> For the panels (b, d, f and h) use the same approach but working with the data reported<br> in the rt_5nm sub-folder.</p>
AsaruSim: a single-cell and spatial RNA-Seq Nanopore long-reads simulation workflow
Open the record for dataset details and reuse information.
Data used in the study "Application of nanopore sequencing for accurate identification of bioaerosol-derived bacterial colonies"
<p>Data used in the study "Application of nanopore sequencing for accurate identification of bioaerosol-derived bacterial colonies." The datasets contain nanopore and Sanger sequencing data (including the electropherograms) as well as EPI2ME and NGSpeciesID analysis.</p>
Fabrication and Characterization of Molybdenum Disulfide Nanopores
<p>Dataset for the protocol:</p> <p><strong>Fabrication and Characterization of Molybdenum Disulfide Nanopores</strong></p>
Assessment of a multiplex PCR and Nanopore-based method for portable dengue virus sequencing in Indonesia
<p>Multiplex primer sets for amplification of the complete coding region of Indonesian dengue virus.</p>
Blobs form during the single-file transport of proteins across nanopores
<p>Electrophysiology data corresponding to the main text figures and supporting information figures. One representative set was chosen for each triplicate and included in this data set. Data was categorised based on the nanopore that was used.</p>
Data deposit for "Resolving sulfation post-translational modifications on a peptide hormone using nanopores"
<p><strong>Data and code deposit for the <a href="https://www.biorxiv.org/content/10.1101/2024.05.08.593138v1" target="_blank" rel="noopener">pre-print</a> and the <a href="https://doi.org/10.1021/acsnano.4c09872">published</a> manuscript at ACS nano.</strong></p> <p><strong>See README.md for more information.</strong></p>
Controlled translocation of proteins through a biological nanopore for single-protein fingerprint identification
<p>Electrophysiology data corresponding to the main text and supporting information figures. The data subsets were grouped based on the nanopore mutant and the supporting information figure the nanopore corresponds to. For each mutant, one representative data set within the triplicate was included.</p>
Larmor SESANS data of FlexiPor nanoporous alumina
<p>SESANS data measured on the Larmor instrument (at the ISIS Neutron and Muon Source, a spallation neutron source) of SmartMembranes FlexiPor Membrane with 100 nm mean pore diameter.</p> <p>Data are in the current (as of 11 November 2024) SESANS (or *.ses) format. This format includes metadata along with a header with four-column data (spin-echo length in Å, normalized scattering correlation function in Å^{-2} cm^{-1}, error in the normalized scattering correaltion function in the same units, and the neutron wavelength in Å).</p> <p>The names of the files are the run numbers of the measurement, and the magnet poleshoe angles are given in the DataFileTitle block in the file header. All measurements were made using Larmor's 1 MHz RF system.</p> <p>Two files are presented per measurement. One (run_sesans.ses) is the data processed using the instrument polarization directly, and the other (run_sesans_tcorr.ses) is the same data after correction using the measured transmission (as in Li et al. 2019 doi:10.1038/s41598-019-44493-9).</p>
Sequencing summary files for "Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples"
<p>Sequencing summary files for experiments in "Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples". </p>
Supporting Materials for Modeling Multicomponent Gas Adsorption in Nanoporous Materials with Two Versions of Nonlocal Classical Density Functional Theory
<p>This web page archives the simulation input and output used in RASPA related to the publication. Please visit the GitHub repository (https://github.com/MusenZhou/GPU-accelerated-cDFT) and contact Jianzhong Wu (jwu@engr.ucr.edu) and Musen Zhou (mzhou035@ucr.edu) if interested in cDFT code.</p>
Data for Deep Reactive Ion Etching of Cylindrical Nanopores in Silicon for Photonic Crystals
<p>Data for Deep Reactive Ion Etching of Cylindrical Nanopores in Silicon for Photonic Crystals</p>
DATASET: Nanoporous Au Formation on Au Substrates via High Voltage Electrolysis
<p>This folder contains all the data shown in the figures of the manuscript or SI titled<br> "Nanoporous Au Formation on Au Substrates via High Voltage Electrolysis" (doi:10.26434/chemrxiv-2022-mx2qd).<br> For each figure, a folder is created here where the corresponding data can be found.</p>
Code and data from: Influence of heat transfer and wetting angle on condensable fluid flow through nanoporous anodic alumina membranes
<p>Data and matlab code to compute all figures contained in a manuscript submitted to the linked journal.</p>
Data for: Nanopore R10.4.1 LSK114 HG002: subset of 20000 reads in BLOW5 format
<p>HG002 (NA24385) is a reference human genome sample used for benchmarking and comparing bioinformatics applications. This dataset contains a subset of 20,000 reads from the HG002 human reference sample, sequenced using an Oxford Nanopore Technologies PromethION sequencer on an R10.4.1 flowcell. Sheared DNA libraries (~17Kb) were prepared using the ONT LSK114 ligation library prep and an R10.4.1 flow cell was used to generate ~30X genome coverage. The original data in the FAST5 format was converted to BLOW5 format using slow5tools v0.8.0. This is a downsampled subset containing 20,000 reads in BLOW5 format.</p>
Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling
<p>Placeholder data for the Lambda phage data used in: Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling.</p> <p>For the complete dataset see the Sequence Read Archive under the PRJNA926802 bioproject ID.</p>
Nanopore Sequencing of Double-Stranded RNA (dsRNA) for Plant Virus and Viroid Detection
<p>Thi file contain results of 24 grapevines leaf samples analyzed using dsRNA-MiSeq (Illumina Miseq) and dsRNAcD sequencing (ONT nanopore), that were used in the following article ''<strong>Nanopore Sequencing of Double-Stranded RNA (dsRNA) for Plant Virus and Viroid Detection'' </strong> submitted in Frontiers in Microbiology </p>
Ebbert Lab Nanopore PCS111 brain cDNA discovery (12 samples - AD vs Controls)
<p>For more information about this data see the publication: https://doi.org/10.1038/s41587-024-02245-9</p> <p>The publication GitHub is a good source of information: https://github.com/UK-SBCoA-EbbertLab/brain_cDNA_discovery</p> <p>There are also README files in the data that provide more information about what is contained in each directory.</p> <p> </p> <p>Basic information about main directory structure and files:</p> <p>reproducing_RNA_figures - Contains all references/annotations necessary for reproducing the stats, analysis, and figures for the RNA isoform analysis in the article.</p> <p>reproducing_nextflow_pipeline - Contains all references/annotations necessary for reproducing the NextFlow pipeline.</p> <p>proteomics - Fasta reference files files used for proteomics analysis with FragPipe and results output by FragPipe.</p> <p>transcriptomic_analysis_output - Contains output from transcriptomic analysis including: quality control, counts matrices, new RNA isoform annotations, and the Bambu R object.</p> <p>counts_transcript.txt - Transcript level counts (does not include any intronic reads).</p>
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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.