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373 results for “Nanopore”
High-resolution ptychotomography dataset of nanoporous glass
<p>This repository contains nanoporous glass tomography dataset, acquired at the cSAXS beamline at the Swiss Light Source at the Paul Scherrer Institute in Villigen (Switzerland). Details about the data acquisition can be found in Ref. [1].</p> <p><strong>Citation and acknowledgements</strong></p> <p>For use of the ptychographic X-ray computed tomography dataset on nanoporous glass:</p> <p><em>[1] M. Holler, A. Diaz, M. Guizar-Sicairos, P. Karvinen, E. Färm, E. Härkönen, M. Ritala, A. Menzel, J. Raabe and O. Bunk. Scientific Reports, 4, 3857 (2014).</em></p> <p>Alignment and tomographic reconstruction provided in "<em>aligned_phase_sinogram" and "tomogram_delta" </em> were performed by a method described in</p> <p><em>[2] M. Odstrcil, M. Holler, J. Holler, M. Guizar-Sicairos,"Alignment methods for nanotomography with deep sub-pixel accuracy", Opt. Express, (2019).</em></p> <p><strong>Dataset description</strong></p> <p>The provided dataset is saved in Matlab MAT v7.3 format.</p> <p><em>stack_object</em> - complex valued unaligned projections with dimensions [Npix_vertical, Npix_horizontal, number_of_angles]</p> <p><em>rotation_angle</em> - vector of corresponding projection angles in degrees</p> <p><em>lambda</em> - wavelength of the illumination beam [m]</p> <p><em>pixel_size</em> - size of the reconstruction pixel [m]</p> <p><em>probe_size</em> - size of the reconstruction probe in pixels</p> <p><em>aligned_phase_sinogram</em> - aligned and unwrapped phase projections</p> <p><em>tomogram_delta</em> - reconstructed tomogram of real part of refractive index delta, n = 1-delta-i*beta</p>
Snakemake workflow for Nanopore-only bacterial assembly and QC
<p>This repository contains a Snakemake workflow tailored for assembling bacterial genomes from long-read data generated with R10.4.1 simplex reads from Oxford Nanopore Technologies and includes several QC steps on the resulting assemblies. The latest version of the scripts can be found at https://gitlab.ilvo.be/genomics/wgs/nanopore-only-bacterial-assembly-snakemake.</p>
Block Copolymer-Assembled Nanopores Enable Ultra-Sensitive Label-Free DNA Detection
<p>Relevant datasets for scientific manuscript: </p> <p><strong>Block Copolymer-Assembled Nanopores Enable Ultra-Sensitive Label-Free DNA Detection </strong></p> <p>Maximiliano Jesus Jara Fornerod <span>a</span>, Alberto Alvarez-Fernandez <span>a</span>, Mate Furedi <span>a</span>, Anandapadmanabhan A Rajendran <span>b</span>, Beatriz Prieto-Simon <span>c,d</span>, Nicolas H. Voelcker <span>e,f*</span>, Stefan Guldin <span>a,g*</span></p> <p> </p> <p><em>a Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK. </em></p> <p><em>b Department of Electronic Engineering, Universitat Rovira i Virgili, 43007, Tarragona, Spain </em></p> <p><em>c Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology, Av. Països Catalans, 16, 43007, Tarragona, Spain </em></p> <p><em>d ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain </em></p> <p><em>e Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia </em></p> <p><em>f Melbourne Centre for Nanofabrication, Victorian Node of the Australian National Fabrication Facility, Clayton, Victoria, 3168, Australia </em></p> <p><em>g Technical University of Munich, Department of Life Science Engineering, Gregor-Mendel-Straße 4, 85354 Freising, Germany </em></p> <p>*corresponding authors. E-mail addresses: nicolas.voelcker@monash.edu, s.guldin@ucl.ac.uk, guldin@tum.de</p>
NanoBaseLib: A Multi-Task Benchmark Dataset for Nanopore Sequencing
<p>NanoBaseLib is a multi-task benchmark dataset for Nanopore Sequencing. We compile and preprocess publicly available datasets using a unified pipeline to ensure consistency and quality across all tasks. The dataset is benchmarked for four key Nanopore sequencing tasks: base calling, polyA detection, segmentation and event alignment, and RNA modification detection. NanoBaseLib is available at <a href="https://nanobaselib.github.io/">https://nanobaselib.github.io</a>. </p>
NPIP: A Comprehensive Analysis Pipeline for Rapid Pathogen Detection in Clinical Samples Based on Nanopore Sequencing
<p>Background: Rapid and accurate pathogen detection is important for effective control of infectious diseases. However, traditional pathogen culture methods have a very long detection time, as well as high rates of false-positive and false-negative results. Third generation sequencing (TGS) technology brings the new possibility of being used as a pathogen detection method. However, the practicability of a pathogen detection report based on TGS is still lacking. There is also a lack of professional and accurate report interpretation.</p> <p>Results: Here, we report on the development of a pathogen detection and analysis tool (NPIP) based on third generation nanopore sequencing technology. We also prove the practicability of nanopore sequencing and NPIP analysis tools in emergency and clinical pathogen detection by demonstrating its use in a practical case.</p> <p>Conclusions: This platform provides an effective, convenient, and fast analysis tool for clinicians and public health personnel to more successfully apply TGS in pathogen detection.</p>
Protein Identification by Nanopore Peptide Profiling
<p>This dataset belongs to “Protein Identification by Nanopore Peptide Profiling” and describes the raw data and analysis of tryptic digested peptides translocating through a mutant Fragaceatoxin C nanopore. A jupyter notebook describing the analysis and structure is added to this dataset.</p> <p> </p> <p><strong>Data description:</strong></p> <p><strong>Protein Identification by Nanopore Peptide Profiling.ipynb</strong></p> <p> Jupyter notebook contained data analysis of data contained in data_0.zip and data_1.zip (Python 3.7)</p> <p><strong>python_scripts.zip</strong></p> <p> Supplementary scripts belonging to “Protein Identification by Nanopore Peptide Profiling.ipynb”. See explanation of custom classes in the jupyter notebook.</p> <p><strong>data_0.zip</strong> - Folder containing raw electrophysiology data and result after analysis with “Protein Identification by Nanopore Peptide Profiling.ipynb”, with each folder containing the following:</p> <p> Alpha casein: Tryptic digest of alpha casein</p> <p> Beta casein: Tryptic digest of beta casein</p> <p> BSA: Tryptic digest of bovine serum albumin</p> <p> Control: Tryptic digest of water (no protein, control measurement)</p> <p> Cytochrome c: Tryptic digest of cytochrome c</p> <p> DHFR_His6: Tryptic digest of dihydropholate reductase (His6 tagged)</p> <p> EFP: Tryptic digest of elongation factor P</p> <p> HMW1Act: Tryptic digest of high molecular weight adhesin protein</p> <p><strong>data_1.zip</strong> - Folder containing raw electrophysiology data, comma-separated MS peptide masses, and result after analysis with “Protein Identification by Nanopore Peptide Profiling.ipynb”, with each folder containing the following:</p> <p> Lysozyme: Tryptic digest of lysozyme </p> <p> PAN: Tryptic digest of proteasome-activating nucleotidase</p> <p> TbpA_Y27A: Tryptic digest of periplasmic binding protein</p> <p> Trypsin: Tryptic digest of bovine trypsin</p> <p> Mass_spec: csv files containing measured ESI-MS peptides</p> <p> Lysozyme synthetic peptides: Synthetic peptides:</p> <p> Lys1: TPGSR</p> <p> Lys2alk: C(+57.02)ELAAAMK</p> <p> Lys3: HGLDNYR</p> <p> Lys4alk: WWC(+57.02)NDGR</p> <p> Lys5: GTDVQAWIR</p> <p> Lys6alk: GYSLGNWVC(+57.02)AAK</p> <p> Lys7: FESNFNTQATNR</p> <p>The structure of the data files is registered data_1.zip in <strong>'index.csv'</strong> (digested proteins) and <strong>'index</strong><strong>_peptides</strong><strong>.csv'</strong> (synthetic peptides) contained in the data folder. In this file, we describe the protein that was measured as well as the folder location and the expected baseline / standard deviation.<br> <br> <strong>Structure of <em>./data/index.csv</em></strong></p> <p><strong>Protein (string) | Folder (string) | Baseline (pA) (float) | Baseline Error (pA) (float)</strong></p> <p> </p> <p>In each <strong>Folder</strong>, there is another <strong>'index.csv'</strong>, explaining which files are with protein and which are without (blank).<br> <br> <strong>Structure of <em>./data/[protein]/[repeat]/index.csv</em></strong></p> <p><strong>blank (boolean) | fname (string)</strong></p> <p> </p> <p>Each folder in data_0.zip and data_1.zip contains a folder for each measure protein, which contains a folder for each repeat. The repeats contain raw axon binary files (.abf), each file contains measurement conditions as follows:</p> <p> [Date of measurement]_[Pore type]_[Buffer conditions]_[added analyte(s)]_[operator initials]</p> <p> <em>e.g</em>: 20200312_1M_KCl_50mM_Citricacid_50mM_BTP_pH_38_FraC_G13F_neg70mV_20ul_CytC_TrypsinGold_FL_0000</p> <p> Measured on 12-03-2020, in 1M KCl buffered with Citricacid (50 mM) adjusted using bis-tris-propane to pH 3.8, using Fragaceatoxin C mutant G13F at a negatively applied potential of 70 mV. 20 µL cytochrome c was added to the cis compartment.</p> <p>The total volume of the container used for all electrophysiology experiments was 400 µL, all samples were prepared at a 1 g/L concentration. A prefix “perf” before analyte description indicates that the chamber was flushed with approximately 2 mL fresh buffer prior to analysis. The buffer condition "BTP" means bis-tris-propane, which is used to titrate to the exact pH of 3.8.</p> <p>Each analysed folder contains<strong> results.pkl</strong> file, containing the analysis result as provided by “Protein Identification by Nanopore Peptide Profiling.ipynb” - see the jupyter notebook</p> <p>Each analysed folder contains <strong>results_analysis.xlsx</strong>, which contains sheets with excluded currents, standard deviations, dwell time and beta value for the pore without analyte added “Blank” and results from the analyte added in “Results”. Parameters used for fitting are contained in “Parameters”. The “Histograms” tab shows the raw data of the excluded current spectra.</p> <p> </p> <p><strong>mass_spec_peaks.zip</strong> – Folder containing mass spectrometry files as analysed by PEAKS Studio</p> <p>The folder contains an subfolder for each protein measured using electrospray ionisation mass spectrometry (ESI-MS).</p> <p> acasein: alpha casein protein</p> <p> b_casein: beta casein protein</p> <p> BSA: bovine serum albumin</p> <p> CytC: cytochrome C digested</p> <p> DHFR: dihydropholate reductase</p> <p> HMW1_Act: high molecular weight adhesin protein</p> <p> PAN: proteasome-activating nucleotidase</p> <p> ThBP: periplasmic thiamine binding protein</p> <p> Trypsin: bovine trypsin</p>
Nanoporous carbon structures of different densities generated through GAP molecular dynamics
<p>These nanoporous (NP) carbon atomic structures, in extendend XYZ format, have been generated using a melt-graphitization-quench molecular dynamics (MD) protocol using a Gaussian interatomic potential (GAP) for amorphous carbon [1]. Simulation details and characterization of structural and mechanical properties will follow shortly in a scientific paper.</p> <p><strong>References</strong></p> <p>[1] M.A. Caro. GAP interatomic potential for amorphous carbon (2.0) [Data set]. Zenodo, 10.5281/zenodo.5243184 (2021).</p>
The impact of secondary channels on the wetting properties of interconnected hydrophobic nanopores
<p>Data from Molecular Dynamics simulations.</p> <p>Files ending in .xyz are trajectory files and relate to total or partical simulation trajectories. The normal sintax is eqX.xyz where X is the imposed filling of the main cavity. </p> <p>Files ending in .dat are the "measurement" files and are the files required to compute the free energy from the simulation data. The normal sintax is eqX.dat where X is the imposed filling of the main cavity. </p> <p>Files ending in .data are LAMMPS output files. The normal sintax is eqX.data where X is the imposed filling of the main cavity. </p> <p>Other auxiliary files exist, but they are part of the analysis of the data and can be reconstructed with just the above mentioned files.</p> <p>The simulation data for specific configurations of the system is separated in different zipped files (either .tar.gz or .zip). 2A.tar.gz, 4A.tar.gz, 6A.tar.gz, 10.zip and 12.zip correspond to the configurations of lateral channels corresponding to 0.2, 0.4, 0.6,1 and 1.2 nm respectively. empty.zip and no.zip correspond to the configurations where the lateral channels where forcefully left empty (by evacuating the 6 lateral pores) and to the simple cylindrical pore with no lateral channels. all.zip, edges.zip and middle.zip correspond to the configurations of cavities with water molecules on all channel sites, only the external rings and in the middle ring, as described in the supplementary note 7.</p>
Direct nanopore sequencing of human cytomegalovirus ge-nomes from high-titre clinical samples
<p>This archive contains the nanopore generated HCMV genomes from the urine and lung clinical samples. The illumina derived genome sequences have been uploaded to GenBank as they are of higher quality, the nanopore sequences are uploaded here to avoid duplication on GenBank. Raw FASTQ reads from the virus are uploaded to NCBI SRA. The full paper abstract is given below. Nanopore sequencing is becoming increasingly commonplace in clinical settings, particularly for diagnostics and outbreak investigations. Its portability, low cost and ability to operate in near real-time has propelled it to the forefront of the recent SARS-CoV-2 pandemic. Although high sequencing error rates initially hampered its wider implementation, improvements have continually been made with each iteration of the nanopore flow cells and base calling software. Here, we assess the feasibility of using nanopore sequencing to determine the complete genome of human cytomegalovirus (HCMV) present in high-titre clinical samples without viral DNA enrichment, PCR amplification or prior knowledge of the sequences. We utilised a hybrid bioinformatic approach that involved assembling the reads<em> de novo</em>, improving the <em>de novo</em> consensus through alignment of reads to the best-matching genome from a collated set of published genomes, and polishing the improved consensus. The final consensus genome sequences from a urine and a lung sample, the latter with an HCMV to human DNA load approximately 50 times lower than the former, achieved 99.97 and 99.93% identity, respectively, to the bench-mark consensuses obtained independently by Illumina sequencing. Thus, we demonstrate that nanopore sequencing is capable of determining HCMV genomes directly from high-titre clinical samples with high accuracy. </p>
An atomistically informed multiscale approach to the intrusion and extrusion in hydrophobic nanopores
<p>Data from Molecular Dynamics simulations.</p> <p>Files ending in .dat are the "measurement" files and are the files required to compute the free energy and diffusivity from the simulation data. The normal sintax is eqX.dat where X is the imposed filling of the pore. The names of the folders represent the pressure at which the filling was taken (0 MPa, -20 MPa, 60 MPa)</p> <p>We also attach one trajectory file, 0.xyz and one LAMMPS output file, 0.log, which should be enough to reproduce the simulation script, in conjuction with 0.data, the initial condition.</p>
Translocation of linearized full-length proteins through an engineered nanopore under opposing electrophoretic force
<p>This database contains raw electrophysiology data and MD data, organised in two parts: part 1 corresponds to electrophysiology traces and part 2 corresponds to MD files. For detailed information see below.</p> <p><strong>Part 1: electrophysiology data </strong></p> <p>Data separated in two main categories: main text data and SI (only) data. The electrophysiology data is named in the following format: </p> <p>main_FnX_CytK mutant_buffer_substrate_cis_applied potential, where n = figure number and X = panel</p> <p>Data from the main text:</p> <p><strong>Figure 2</strong>:</p> <p>--> C) CytK WT + S1 substrate (file names start with main_F2C_CytK WT)</p> <p>--> D) CytK 2E-4D (K128D K155D Q145D S151D) + S1 substrate (main_F2D...)</p> <p>--> E) CytK 2E-4D (K128D K155D Q145D S151D) + tzatziki substrate (main_F2E...)</p> <p>--> F) CytK 2E-4D (K128D K155D Q145D S151D) + mujdei substrate (main_F2F...)</p> <p><strong>Figure 4</strong>:</p> <p>--> CytK 2E-4D (K128D K155D Q145D S151D/ 4D) + malE219a substrate (main_F4A...)</p> <p>--> CytK 2E-4D (K128D K155D Q145D S151D/ 4D) + H152A-GBP substrate (main_F4B...)</p> <p>--> CytK 2E-4D (K128D K155D Q145D S151D/ 4D) + W30G-W133L-DHFR substrate (main_F4C...)</p> <p> </p> <p><strong>Supporting information figures </strong></p> <p>general name: SI_Sx_CytK mutant_buffer_substrate_cis_applied potential, where x = number figure from supporting information</p> <p> </p> <p>Figure S4. S1 translocation through the K128D K155D CytK mutant nanopore --> SI_S4...</p> <p>Figure S5. S1 translocation through the K128D K155D Q145D CytK mutant nanopore --> SI_S5...</p> <p>Figure S6. S1 translocation through the K128D K155D T147D CytK mutant nanopore --> SI_S6...</p> <p>Figure S7. Translocation of S1 through the 2E-1D-1Q-Q122D-CytK nanopore --> SI_S7...</p> <p>Figure S8. Translocation of S1 through 2E-4D-CytK nanopores --> see F2D main text (main_F2D...)</p> <p>Figure S9. Tzatziki and the CytK 2E-2D nanopore --> SI_S9...</p> <p>Figure S10. Tzatziki translocation through the K128D Q145D S151D K155D CytK nanopore --> see F2D main text (main_F2E...)</p> <p>Figure S11. Tzatziki translocation through the K128D K155D Q145D CytK nanopore --> SI_S11...</p> <p>Figure S12. Tzatziki translocation through the K128D K155D T147D CytK mutant nanopore --> SI_S12...</p> <p>Figure S14. Translocation of mujdei through 2E-4D-CytK nanopores --> see F2D main text (main_F2F...)</p> <p>Figure S22. Translocation of malE219a through 2E-4D-CytK nanopores in 2 M urea --> see F4A main text (main_F4A...)</p> <p>Figure S23: Translocation GBP H152A through the 2E-4D CytK nanopore in 2.4 M urea --> see F4B main text (main_F4B...)</p> <p>Figure S24. Translocation of W30G-W133L-DHFR through 2E-4D-CytK nanopore in 2.6 M urea --> see F4B main text (main_F4B...)</p> <p>Figure S26. MalE219a translocation through the 2E-4D CytK mutant in 1 M and 1.8 M Gu.HCl --> SI_S26... (1 M GuHCl and 1.8 M GuHCl are included in the file name)</p> <p>Figure S27: WT-CytK tested with the malE219a and malE219aD10ssrA proteins in 1.5 M Gu.HCl --> SI_S27...</p> <p>Original SDS-PAGE gels of the substrates in a powerpoint file</p> <p> </p> <p><strong>Part 2: MD data</strong></p> <p>The data corresponding to the MD simulations is bundled in a zip file named MD_files.zip</p> <p>This file contains the following subfiles linked to <strong>main text Figure 3</strong>:</p> <p><em>panel A</em>:</p> <p>- main_F3A_CytK_E2-D2 </p> <p>- main_F3A_CytK_E2-D3</p> <p>- main_F3A_CytK_E2-D4</p> <p>- main_F3A_CytK_WT</p> <p><em>panels C, D and E</em>:</p> <p>- main_F3CDE_CytK_E2-D4_rep1</p> <p>- main_F3CDE_CytK_E2-D4_rep2</p> <p>- main_F3CDE_CytK_E2-D4_rep3</p> <p>and files linked to the supporting information <strong>Figure S20</strong>:</p> <p>- SI_S20_ABC_CytK_E2-D4_TZA11_rep1</p> <p>- SI_S20_ABC_CytK_E2-D4_TZA11_rep2</p> <p>- SI_S20_ABC_CytK_E2-D4_TZA11_rep3</p>
Simulated Nanopore metagenomic reads
<p>We simulated metagenomic Nanopore sequencing reads to a mixture ratio that approximates that found in patient sputa, albeit with a slightly higher mycobacterial component. In total, 4.5 gigabases were generated, at proportions: 46% each for bacteria and human, 6\% <em>Mycobacterium tuberculosis </em>complex (MTBC), and 1% each for virus and non-tuberculous mycobacteria (NTM).</p> <p>The reference genomes that reads were simulated from for these groups were gathered as follows. The references for the virus group were obtained using kraken's (v2.1.2) --download-library functionality. The viral library was downloaded on June 15 2023. The human genome from which the reads were simulated was KOREF_S1v2.1 (RefSeq accession GCA_020497085.1), with contigs shorter than 10kbp removed. The bacterial references were obtained by first downloading the bacteria library through kraken, followed by a subsampling due to the size (166Gb) of the resulting FASTA file. We subsampled the file by first removing sequences with a length <50kbp. We then extracted each sequence into its own FASTA file under a directory for the genus of the sequence - excluding the <em>Mycobacterium</em> genus. Genera were randomly subsampled to contain a maximum of 1000 assemblies. Each genus was then reduced to a representative subset using Assembly Dereplicator (commit 2dfcb14; https://github.com/rrwick/Assembly-Dereplicator) by keeping only 10% of the assemblies for each genus (-f 0.1). The NTM references selected were <em>M. abscessus</em> (accession GCF_017190695.1), <em>M. avium</em> (GCF_020735285.1), <em>M. kansasii</em> (GCA_014701265.1), <em>M. ulcerans</em> (GCF_000013925.1), <em>M. intracellulare</em> (GCF_016756075.1), <em>M. terrae</em> (GCF_010727125.1), and <em>M. fortuitum</em> (GCF_001307545.1). The MTBC reference is a lineage 1 assembly (GCF_932530395.1).</p> <p>We used Badreads (v0.4.0) to produce the simulated Nanopore reads for each group, specifying the number of bases in the appropriate proportions mentioned above. For all groups we specified no junk or random reads and 0.5% chimeric reads. In addition, for the MTBC, virus, and NTM groups we used a non-default length option --length 4000,3000 to produce reads with mean length 4000bp and a standard deviation of 3000. Defaults were used for all other options (the default error model is trained on real R10.4.1 Nanopore reads from 2023).</p> <p>We filtered the simulated Nanopore reads to remove any read with a length <500bp or an ambiguous nucleotide (non-ACGT).</p>
Nanopore signal compression benchmark data in BLOW5 format
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Data from: Barcoding 100K specimens in a single nanopore run
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Nanopore sequencing genomic DNA from Stentor pyriformis and its endosymbiont, Chlorella variabilis
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Data from: The preparation and characterization of uniform nanoporous structure on glass
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Data for: Sensitive and specific detection of tumor-derived exosomes using nanopore-crystal microchips
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Using cerebrospinal fluid nanopore sequencing assay to diagnose tuberculous meningitis: a retrospective cohort study in China
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Nanopore sequencing data analysis using Microsoft Azure cloud computing service
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De novo genome assembly of human cell line CHM13 nanopore ultra-long reads using Shasta
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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.