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373 results for “Nanopore”
Voltage controlled iontronic switches: a computational method to predict electrowetting in hydrophobically gated nanopores
<p>Folder with numbered names ("0", "0.1", "n0.5") have the data from restrained molecular dynamics (".dat") and some trajectories (".xyz") at the applied voltage corresponding to the name of the folder. "n" in the folder name correspond to the negative voltages.</p> <p>Folders "wetting" and "drying" contain the data used to compute the wetting and drying rates at different applied voltages using Molecular Dynamics.</p>
Oxford Nanopore sequencing for comprehensive, targeted eukaryotic metagenomics analysis of environmental DNA biodiversity
<p><span>The study of symbiotic organisms from different classes or kingdoms, including those previously unknown, is possible with simultaneous and equally efficient metagenomic analysis of these species. A variety of targeted primer sets are used for eukaryotic metagenomic biodiversity, including those that are universal for specific families, classes</span><span>,<span> or kingdoms. The most universal for all existing cellular organisms is the presence of ribosomal RNA encoding gene sequences. For eukaryotic sequences, these are 16S and 23s rDNA, </span>and <span>for eukaryotic sequences of nuclear (18S and 28S) and mitochondrial (12S and 16S) ribosomal RNA. Here, we present the application of the eukaryotic metagenomics approach to the simultaneous, quantitative</span>,<span> and unbiased identification of most eukaryotic species. </span></span></p>
NASTRA: Accurate analysis of short tandem repeat markers by nanopore sequencing with repeat-structure-aware algorithm
<p><span>Forensic short-tandem repeats (STR) genetic markers are multi-allelic and widely utilized for individual identification, kinship testing, and cell-line authentication. Nanopore sequencing, known for its portability, is emerging as a promising approach for STR typing, facilitating real-time and in-field testing. However, its efficacy is often hampered by sequencing noise. Previous methods rely on alignment-based genotyping, necessitating known alleles, which limits their applicability to unknown alleles. Here, we introduced NASTRA, an innovative allele reference-free tool for precise germline analysis of STR genetic markers. NASTRA incorporates a recursive algorithm to infer repeat structures of allele sequences using only known repeat motifs. Our tests, conducted on 80 individual samples and 8 DNA standards, have demonstrated NASTRA's exceptional 100% accuracy in genotyping nearly all diploid STRs across various multiplex kits and flow cells. It surpasses alignment-based methods in accuracy and speed. In a paternity testing case study, NASTRA accurately identified three relationships among six individuals within an 18-minute sequencing duration. These results underscore NASTRA's ability to perform STR analysis on both NGS and nanopore sequencing platforms, significantly enhancing the utility of nanopore sequencing in relevant applications.</span></p>
Data associated to the manuscript "Simulating the charging mechanism of a realistic nanoporous carbon-based supercapacitor using a fully polarizable model"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Simulating the charging mechanism of a realistic nanoporous carbon-based supercapacitor using a fully polarizable model</p> <p>Camille Bacon, Patrice Simon, Mathieu Salanne and Alessandra Serva</p> <p><em>ChemRxiv, </em>10.26434/chemrxiv-2024-9577m, 2024</p> <p>The folder <em>input_files</em> contains typical MetalWalls input files used to perform the simulations.</p> <p>The folder <em>raw_data</em> contains the processed data used to plot all the figures of the paper.</p>
Highly Durable Nanoporous Cu2−xS Films for Efficient Hydrogen Evolution Electrocatalysis under Mild pH Conditions
<div># Dataset of “ Highly Durable Nanoporous Cu2-xS Films for Efficient Hydrogen Evolution Electrocatysis under Mild pH Conditions”</div> <div> </div> <div>---</div> <div> </div> <div>## GENERAL INFORMATION</div> <div>----------------------</div> <div> </div> <div>1. Dataset title: “ Highly Durable Nanoporous Cu2-xS Films for Efficient Hydrogen Evolution Electrocatysis under Mild pH Conditions”</div> <div> </div> <div>2. Authorship: </div> <div> Name: Roser Fernández-Climent </div> <div> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain</div> <div> ORCID: 0009-0003-4184-5579</div> <div> </div> <div> Name: Jesús Redondo</div> <div> Institution: Department of Polymers and Advanced Materials, Centro de Física de Materiales, University of the Basque Country UPV/EHU, 20018 San Sebastián, Spain; Department of Surface and Plasma Science, Faculty of Mathematics and Physics, Charles University, 180 00 Prague 8, Czech Republic</div> <div> </div> <div> </div> <div> Name: Miguel Garcia-Tecedor</div> <div> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain; Photoactivated Processes Unit, IMDEA Energy Institute, Parque Tecnológico de Móstoles, 28935 Móstoles, Madrid, Spain;</div> <div> ORCID: 0000-0002-9664-4665</div> <div> </div> <div> Name: Maria Chiara Spadaro</div> <div> Institution: Catalan Institute of Nanoscience and Nanotechnology (ICN2) and BIST Campus UAB, Bellaterra 08193 Barcelona, Spain;</div> <div> ORCID: 0000-0002-6540-0377</div> <div> </div> <div> Name: Junan Li</div> <div> Institution: Department of Chemistry, Université de Montréal, Montréal, QC H2V 0B3, Canada</div> <div> ORCID: 0000-0002-3660-1049</div> <div> </div> <div> Name: Daniel Chartrand</div> <div> Institution: Department of Chemistry, Université de Montréal, Montréal, QC H2V 0B3, Canada</div> <div> </div> <div> </div> <div> Name: Frederik Schiller</div> <div> Institution: Centro de Física de Materiales and Material Physics Center CSIC/UPV-EHU, 20018 San Sebastián, Spain; Donostia International Physics Center, 20018 San Sebastián, Spain</div> <div> ORCID: 0000-0003-1727-3542</div> <div> </div> <div> Name: Jhon Pazos</div> <div> Institution: Research Cluster on Converging Sciences and Technologies (NBIC), Departamento de Ingeniería Electrónica, Universidad Central, Bogotá 110311, Colombia</div> <div> ORCID: 0000-0001-7570-9047</div> <div> </div> <div> Name: Mikel F. Hurtado</div> <div> Institution: Research Cluster on Converging Sciences and Technologies (NBIC), Departamento de Ingeniería Electrónica, Universidad Central, Bogotá 110311, Colombia; Materials Chemistry Area, Civil Engineering Department, Corporación Universitaria Minuto de Dios, Calle 80, Main Sede Bogotá, Colombia. − Nanotechnology Applications Area, Environmental Engineering Department, Universidad Militar Nueva Granada, Zipaquirá 110311, Colombia</div> <div> </div> <div> </div> <div> Name: Victor de la Peña O’Shea</div> <div> Institution: Photoactivated Processes Unit, IMDEA Energy Institute, Parque Tecnológico de Móstoles, 28935 Móstoles, Madrid, Spain;</div> <div> ORCID: 0000-0001-5762-4787</div> <div> </div> <div> Name: Nikolay Kornienko</div> <div> Institution: Department of Chemistry, Université de Montréal, Montréal, QC H2V 0B3, Canada;</div> <div> ORCID: 0000-0001-7193-2428</div> <div> </div> <div> Name: Jordi Albiol</div> <div> Institution: Catalan Institute of Nanoscience and Nanotechnology (ICN2) and BIST Campus UAB, Bellaterra 08193 Barcelona, Spain; ICREA, 08010 Barcelona, Catalonia, Spain</div> <div> ORCID: 0000-0002-0695-1726</div> <div> </div> <div> Name: Sara Barja</div> <div> Institution: Department of Polymers and Advanced Materials, Centro de Física de Materiales, University of the Basque Country UPV/EHU, 20018 San Sebastián, Spain; Donostia International Physics Center, 20018 San Sebastián, Spain; IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain;</div> <div> Email: sara.barja@ehu.eus</div> <div> </div> <div> </div> <div> Name: Camilo A. Mesa</div> <div> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain; Research Cluster on Converging Sciences and Technologies (NBIC), Departamento de Ingeniería Electrónica, Universidad Central, Bogotá, 110311, Colombia;</div> <div> Email: <cmesa@uji.es> </div> <div> ORCID: 0000-0002-8450-2563</div> <div> </div> <div> </div> <div> Name: Sixto Giménez</div> <div> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain</div> <div> Email: <sjulia@uji.es> </div> <div> ORCID: 0000-0002-4522-3174</div> <div> </div> <div> </div> <div> </div> <div>## FILE DESCRIPTION</div> <div>—————————</div> <div>### Figure 2</div> <div>-Fig2e.txt : XPS analysis for Cu LMM.</div> <div>-Fig2f.txt : XPS analysis for S 2p spectra of the Cu2−xS electrodes. Reference spectra measured on a metallic Cu substrate are shown in red dotted lines.</div> <div> </div> <div>### Figure 3</div> <div>-Fig3a.txt : Chronoamperometric measurement at −1 V vs RHE of the Cu2−xS catalyst for 28 days of continuous operation. The dashed gray line represents the quasi-linear increase in the catalytic current density as a function of operation time. Steady-state currents at −1.0 V vs RHE normalized by the electrochemical surface area (ECSA) are shown as light blue empty dots.</div> <div>-Fig3b.txt : Linear sweep voltammograms (LSV), measured at 20 mV s−1, of the same Cu2−xS electrode as a function of operation time between day 1, i.e., freshly synthesized catalyst (darker blue), and after 28 days (lighter blue) of continuous operation. Inset: zoom between the first and the last LSV to compare the overpotential at −10 mA cm−2 (dashed red line).</div> <div>-Fig3c.txt : Cathodic current densities (|J|) measured at −1.0 V vs RHE,from panel (b) (blue filled dots) compared to the ECSA increase ratio (empty green dots, RECSA) calculated using eq 1. Note that the time is in the log scale.</div> <div>-Fig3d.txt : Series (RS) and charge transfer (RCT) resistances and capacitance, RS (gray dots), RCT (violet dots), and C (green dots) at the 28th day of measurement. The gray area denotes the potential region where RCT < RS.</div> <div>-Fig3f.txt : Tafel slope values as a function of operation time obtained from panel.</div> <div> </div> <div>###Figure 4</div> <div>-Fig4a.txt : Differential optical density spectra of the Cu2−xS (light and dark blue) and reference Cu foil (light and dark red) electrodes as a function of potential. For reference, Cu2−xS differential spectra were measured also in 0.1 M TBAP in acetonitrile.</div> <div>-Fig4b.txt : Operando XRD diffractograms at different potentials from OCP to −1.0 V vs RHE.</div> <div>-Fig4bInset.txt : Inset:LSV.</div> <div>-Fig4c.txt : Reference XPS measurements for (c) Cu LMM and (d) S 2p spectra. </div> <div>-Fig4d.txt : Post electrochemical XPS measurements for (c) Cu LMM and (d) S 2p spectra.</div> <div> </div> <div>### Figure S2</div> <div>-FigS2f.txt : UV-Vis-NIR absorption spectrum of the pristine Cu2-xS films, extracted from diffuse reflectance measurements. The NIR band centered ~1600 nm is tentatively assigned to localized surface plasmon resonance caused by the Cu deficiency as observed in other Cu2-xS electrodes</div> <div> </div> <div>### Figure S4</div> <div>-FigS4a.txt : XPS analysis of the Cu2-xS as-synthetized electrodes before (black) and after (red) Ar+ cleaning. Cu 2p spectra.</div> <div>-FigS4b.txt : XPS analysis of the Cu2-xS as-synthetized electrodes before (black) and after (red) Ar+ cleaning. Normalized Cu 2p spectra.</div> <div>-FigS4c.txt : XPS analysis of the Cu2-xS as-synthetized electrodes before (black) and after (red) Ar+ cleaning. Cu Auger spectra.</div> <div>-FigS4d.txt : XPS analysis of the Cu2-xS as-synthetized electrodes before (black) and after (red) Ar+ cleaning. O 1s spectra.</div> <div>-FigS4e.txt : XPS analysis of the Cu2-xS as-synthetized electrodes before (black) and after (red) Ar+ cleaning. C 1s spectra.</div> <div>-FigS4f.txt : XPS analysis of the Cu2-xS as-synthetized electrodes before (black) and after (red) Ar+ cleaning. S 2p spectra.</div> <div> </div> <div>### Figure S9</div> <div>-FigS9.txt : Real part of the complex capacitance measured as a function of frequency (Bode plots) of our Cu2-xS electrodes a function of operation time measured at -0.1 V vs RHE (non-faradaic region). The Cdl values were taken at ~5 Hz.</div> <div> </div> <div>### Figure S10</div> <div>-FigS10.txt : Normalized linear sweep voltammograms (LSV) by the rECSA values from Figure 3c of the same Cu2-xS electrode as a function of operation time between the day 1, i.e., freshly synthesized catalyst (darker blue) and after 28 days (lighter blue) of continuous operation. LSVs from Figure 3b are displayed in the inset for reference. The LSV were measured at 20 mV s-1 in 0.1 M KHCO3.</div> <div>-FigS10Inset.txt : Linear sweep voltammograms (LSV), measured at 20 mV s−1, of the same Cu2−xS electrode as a function of operation time between day 1, i.e., freshly synthesized catalyst (darker blue), and after 28 days (lighter blue) of continuous operation.</div> <div> </div> <div>### Figure S12</div> <div>-FigS12.txt : Rs values of the Cu2-xS catalysts extracted from electrochemical impedance spectroscopy (EIS) analysis as a function of operation time (indicated by the grey arrow),inset: Rs values of the Cu2S catalysts measured as a function of concentration of KHCO3 electrolyte. A 20-fold increase of the KHCO3 concentration results in a decrease of ~1 order of magnitude in the Rs, thus, the observed ohmic drop decrease can be attributed to an increase in ionic concentration in the electrolyte. Rs can aid understanding the difference between the 8-fold increase in J, compared to the 6.5-fold increase in ECSA shown in Figure 3c. However, this is out of the scope if this paper and is being subject of further analysis.</div> <div> </div> <div>### Figure S13</div> <div>-FigS13a.txt : Charge transfer resistances (Rct)</div> <div>-FigS13b.txt : Capacitances values as a function of operation time from the Cu2-xS electrocatalysts.</div> <div> </div> <div>### Figure S14</div> <div>-FigS14.txt : Steady-state LSVs of the Cu2-xS electrode as a function of operation time between the day 1, i.e., freshly synthesized catalyst (darker blue) and after 28 days (lighter blue) of continuous operation (indicated by the grey arrow). Every data point corresponds to the average current of the last 60 s of a 5-minute chronoamperometric measurement at every measured potential.</div> <div> </div> <div>### Figure S16</div> <div>-FigS16a.txt : Differential spectra of the reference Cu foil as a function of applied potential.</div> <div>-FigS16b.txt : Differential spectra of the Cu2-xS and reference electrodes as a function of applied potential.</div> <div> </div> <div>### Figure S17</div> <div>-FigS17a.txt : XPS analysis of the Cu2-xS electrodes after the electrochemical measurements. Cu Auger spectra when transferred under air (black) and under N2 atmospheres (red).</div> <div>-FigS17b.txt : XPS analysis of the Cu2-xS electrodes after the electrochemical measurements. C 1s spectra when transferred under air (black) and under N2 atmospheres (red).</div> <div>-FigS17c.txt : XPS analysis of the Cu2-xS electrodes after the electrochemical measurements. O 1s spectra when transferred under air (black) and under N2 atmospheres (red).</div> <div>-FigS17d.txt : XPS analysis of the Cu2-xS electrodes after the electrochemical measurements. 2p spectra when transferred under air (black) and under N2 atmospheres (red).</div> <div> </div> <div>### Figure S18</div> <div>-FigS18a.txt : XPS analysis of the Cu2-xS electrodes after the electrochemical measurements with and without washing with water.</div> <div>-FigS18b.txt : XPS analysis of the Cu2-xS electrodes after the electrochemical measurements with and without washing with water.</div> <div>-FigS18c.txt : XPS analysis of the Cu2-xS electrodes after the electrochemical measurements with and without washing with water.</div>
Sensing Performance of Artificially Intelligent Nanopores Developed by Integrating Solid-State Nanopores with Machine Learning Methods
<p>Ionic current-time data obtained from measuring nanoparticles with the diameters of 90, 100, 150, 200, 220, 270, and 300 nm, using nanopores with a diameter of 300nm.</p> <p>Test_100nm_1 means a test data of nanoparticles with a diameter of 100 nm.</p> <p>Train_100nm_1 means a training data of nanoparticles with a diameter of 100 nm.</p>
RSV subgroup A direct RNA seq of infected MRC5 cells and mock infected MRC5 cells mRNA on nanopore
<p>Fastq files of mRNA sequenced by direct RNAseq on Oxford nanopore device and associated data for the paper</p>
Nanopore MinION Run Metrics and genomic DNA fragment size analysis data from automated phenol-chloroform extractions (RBI LabDroid Maholo)
<p>Nanopore MinION run MinKNOW statistical metrics output, Agilent Femto Pulse and Tape Station gDNA fragment size analysis reports of genomic DNA isolated from automated RBI LabDroid Maholo organic extractions.</p>
Sequencing of individual barcoded cDNAs on Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns (repository for Genome Research paper, 2022)
<p>Simulated ONT and PacBio RNA-Seq data for "Sequencing of individual barcoded cDNAs on Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns" paper (Mikheenko et al., Genome Research, 2022). All details can be found in the Methods section of the paper.</p> <p><strong>PacBio.simulated_uniform_coverage.fasta.gz</strong> and <strong>ONT.simulated_uniform_coverage.fasta.gz files</strong> were used in Supplemental Note “Benchmarking of the read-to-isoform assignment algorithm”.</p> <p><strong>ONT.simulated_real_expression.fasta.gz</strong> file and all GTF files were used in the Section "Splice site correction improves transcript discovery precision". <strong>mouse.gencode.M26.spatial.15percent.reduced.gtf</strong> was used as the annotation file for all tools. <strong>mouse.gencode.M26.spatial.15percent.expressed.gtf </strong>contains the set of all expressed isoforms. <strong>mouse.gencode.M26.spatial.15percent.expressed_kept.gtf</strong> contains those of the isoforms that are in presented in the annotation file ("known" transcripts), <strong>mouse.gencode.M26.spatial.15percent.reduced.gtf</strong> contains expressed isoforms that were removed from the annotation ("novel" transcripts).</p>
Data set related to the manuscript "Understanding the chemical shifts of aqueous electrolyte species adsorbed in carbon nanopores"
<p>Graphical files in the agr format for all the figures in the manuscript entitled "Understanding the chemical shifts of aqueous electrolyte species adsorbed in carbon nanopores". Examples of input files for the density functional theory, lattice and molecular dynamics simulations are also provided.</p>
Dataset to "Defining the pressures of a fluid in a nanoporous, heterogeneous medium"
<p>This is the dataset presented in the article "Defining the pressures of a fluid in a nanoporous, heterogeneous medium" [1].</p> <ol> <li>https://www.frontiersin.org/articles/10.3389/fphy.2022.866577/full</li> </ol>
Nanopore sequencing data analysis using Microsoft Azure cloud computing service
<p>Genetic information provides insights into the exome, genome, epigenetics and structural organisation of the organism. Given the enormous amount of genetic information, scientists are able to perform mammoth tasks to improve the standard of health care such as determining genetic influences on outcome of allogeneic transplantation. Cloud-based computing has increasingly become a key choice for many scientists, engineers and institutions as it offers on-demand network access and users can conveniently rent rather than buy all required computing resources. With the positive advancements of cloud computing and nanopore sequencing data output, we were motivated to develop an automated and scalable analysis pipeline utilizing cloud infrastructure in Microsoft Azure to accelerate HLA genotyping service and improve the efficiency of the workflow at lower cost. In this study, we describe (i) the selection process for suitable virtual machine sizes for computing resources to balance between the best performance versus cost-effectiveness; (ii) the building of Docker containers to include all tools in the cloud computational environment; (iii) the comparison of HLA genotype concordance between the in-house manual method and the automated cloud-based pipeline to assess data accuracy. In conclusion, the Microsoft Azure cloud-based data analysis pipeline was shown to meet all the key imperatives for performance, cost, usability, simplicity and accuracy. Importantly, the pipeline allows for the ongoing maintenance and testing of version changes before implementation. This pipeline is suitable for data analysis from MinION sequencing platforms and could be adopted for other data analysis application processes.</p>
Two-dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials
<p>This repo contains the supplementary data sets for the to-be-published paper entitled "Two-dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials".</p> <p> </p> <p>This repo contains the following data sets:</p> <p>1. CIF files for amorphous porous materials (activated carbon, hyper-cross-linked polymers, Kerogen, PIMs).</p> <p>2. Grand canonical Monte Carlo (GCMC) simulation results for single-component adsorption isotherms in ToBaCCo1.0 MOFs and in amorphous porous materials. Gas molecules include Kr, Xe, ethane, propane, butane, n-hexane, and 2,2-dimethylbutane.</p> <p>3. Textural properties of ToBaCCo1.0 MOFs and amorphous porous materials.</p> <p>4. Trained machine learning models. R code that can work with these ML models is hosted on <a href="https://github.com/snurr-group/2D-energy-histogram">GitHub</a>. </p>
Temperature modulates dominance of a superinfecting Arctic virus in its unicellular algal host - Nanopore sequencing reads
<p>Nanopore sequencing reads of two Micromonas polaris viruses, MpoV-45T and MpoV-46T using R9 chemistry.</p>
Experimental data for "An End-to-End Coding Scheme for DNA-Based Data Storage With Nanopore Sequenced Reads"
<p>The experimental dataset used in "An End-to-End Coding Scheme for DNA-Based Data Storage With Nanopore Sequenced Reads."</p> <p>A set of 91,766 150-nt oligos were synthesised with GenScript (oligos.fasta). Each oligo consists of a pseudo-random 110-nt payload flanked by 20-nt primers at each end. The strands are split in three roughly equal groups (two groups of 30,589 and one group of 30,588). Each group has a dedicated primer pair for targeted PCR amplification (the primer pairs used for amplification are provided in primers_synthesis.fasta). The pseudo-random payload was designed to avoid primer-payload collisions.</p> <p>For each file, a sample from the synthesised pool was PCR amplified using the corresponding primer pair and sequenced using Oxford Nanopore Technologies MinION sequencing device following the standard library preparation protocol for amplicon DNA. The raw reads were basecalled using guppy, either in fast- ("acc-false") or high-accuracy ("acc-true") regime. The basecaller generated two groups of reads—"passQ-true" for the reads that passed the quality-score threshold of 8 and "passQ-false" for those that did not. For each group of reads, a BLAST-based fuzzy search for primer sequences was performed and, based on the resulting alignments, the segments containing the correct primer pairs and located at a distance of 150+-15nt were extracted (separately for forward and reverse-complemented reads). The segments are then assigned to the closest synthesized strand based on Levenshtein distance. The resulting clusters are used to estimate the parameters of the end-to-end DNA storage channel model and to test the proposed error-correction scheme.</p> <p>The archive clustered_read_segments.tar.gz contains 12 sub-archives, for each file (0,1,2), accuracy ("acc-true" or "acc-false"), and Q-score ("passQ-true" or "passQ-false"). Within each sub-archive, there are two folders (one for forward read segments and one for backward read segments), and each folder contains two files: one for the reference synthesised (or "transmitted") sequences that correspond to the file in question ("TX__" — e.g., "TX__file=0_accBaCa=true_passQ=true_filter=true_forward_.txt") and another file for the sequenced (or "received") segment clusters ("RX__" — e.g., "RX__file=0_accBaCa=true_passQ=true_filter=true_forward_.txt"). The received clusters in the "RX__" file are ordered in correspondence with the synthesised sequences in the "TX__" file, and a line "===============================" is used as a separator.</p>
Nanopore sequencing assay to detect and diagnose tuberculous meningitis via cerebrospinal fluid
<p>This study aimed to evaluate the efficiency of nanopore sequencing for the early diagnosis of tuberculous meningitis (TBM) using cerebrospinal fluid and compared it with acid-fast bacilli (AFB) smear, mycobacterial growth indicator tube (MGIT) culture, and Xpert MTB/Rifampicin (RIF). We enrolled 64 adult patients with presumptive TBM admitted to our hospital from August 2021 to August 2023. We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of AFB smear, culture, Xpert MTB/RIF, and nanopore sequencing to evaluate their diagnostic efficacy compared with a composite reference standard for TBM. Among these 64 patients, all tested negative for TBM by AFB smear. The sensitivity, specificity, PPV, and NPV were 11.11%, 100%, 100%, and 32.2% for culture, 13.33%, 100%, 100%, and 2.76% for Xpert MTB/RIF, and 77.78%, 100%, 100% and 65.52% for nanopore sequencing, respectively. The diagnostic accuracy of the nanopore sequencing test was significantly higher than that of conventional testing methods used to detect TBM.</p>
Movies of HIV (VLP) particles through 200 nm nanopores (without or with 300mM NaCl)
<p><strong>Soft jamming of viral particles in nanopores</strong><br>Léa Chazot-Franguiadakis, Joelle Eid, Gwendoline Delecourt, Pauline J. Kolbeck, Saskia Brugère,Bastien Molcrette, Marius Socol, Marylène Mougel, Anna Salvetti, Vincent Démery, Jean Christophe Lacroix, Véronique Bennevault, Philippe Guégan, Martin Castelnovo and Fabien Montel.</p> <p>Nature Communications 2024.</p> <p> </p> <p><strong>Description of content:</strong></p> <p>Movies of HIV (VLP) particles through 200 nm nanopores (without or with 300mM NaCl)</p> <p>Dilution of the initial samples (named MM24) by 10, with a resulting concentration of 960.10^5 particles/mL.</p> <p>For each pressure, 4 videos were made (2 of them are shown here). The final sequences are averaged over at least 2 experiments.</p> <p> </p>
Control panel created from 30-40 Nanopore or PacBio HiFi sequencing data from the Human Pangenome Reference Consortium
<p>This is control panel for <a href="https://github.com/friend1ws/nanomonsv">nanomonsv</a> software, which is expected to exclude many false positives as well as improve computational cost. This is made by aligning 30-40 Nanopore or PacBio HiFi sequencing data from Human Pangenome Reference Consortium (HPRC) to the GRCh38 or CHM13 reference genomes with <a href="https://github.com/lh3/minimap2">minimap2</a> version 2.24.</p> <p><strong>When you use these control panels and publish, do not forget to credit to <a href="https://humanpangenome.org/data-use-protocol/">HPRC</a>!</strong></p> <div> <div> <div> <p>Reference genomes:</p> <ul> <li>GRCh38: <a href="https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz">Download GRCh38</a></li> <li>CHM13: <a href="https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0_maskedY_rCRS.fa.gz">Download CHM13</a> <div> <div> <div> <div> </div> </div> </div> </div> </li> </ul> </div> </div> </div>
Nanopore sequence analysis - Galaxy Training Material
<p>Twelve MDR plasmids harboring samples were prepared according to the MinION library construction protocols, followed by library sequencing. After 8 hours of sequencing run, a total of 287 725 reads ranging from dozens to tens of thousands of bases in length were obtained, covering a total of 493 Mbp. The raw data were subjected to several stages of processing, including basecalling, de-multiplexing, fasta sequence extraction. For this tutorial one out of the twelve samples is chosen as example.</p> <p>This dataset is extracted of a project studying the Efficient generation of complete sequences of MDR-encoding plasmids by rapid assembly of MinION barcoding sequencing data (<a href="https://doi.org/10.1093/gigascience/gix132">https://doi.org/10.1093/gigascience/gix132</a>)</p>
Dataset for "Nanopore-based genome assembly and the evolutionary genomics of basmati rice"
<p><strong>Description of uploaded files:</strong></p> <p>Basmati334.basmati.not_scaffolded.fa</p> <p>- Polished genome assembly for Basmati 334 but not scaffolded.</p> <p> </p> <p>Basmati334.basmati.not_scaffolded.sorted.gff</p> <p>- Gene annotation for the assembly Basmati334.basmati.not_scaffolded.fa</p> <p> </p> <p>Basmati334.basmati.ragoo_scaffold.fa</p> <p>- Polished genome assembly for Basmati 334 and scaffolding with RaGOO using the Nipponbare RAPDB1.0 as reference genome.</p> <p> </p> <p>Basmati334.basmati.ragoo_scaffold.sorted.gff</p> <p>- Gene annotation for the assembly Basmati334.basmati.ragoo_scaffold.fa</p> <p> </p> <p>Basmati334.basmati.ragoo_scaffold.repeatmasker.bed</p> <p>- Repetitive DNA coordinates for the assembly Basmati334.basmati.ragoo_scaffold.fa</p> <p> </p> <p>CONSEL.tar.gz</p> <p>- Files used for CONSEL analysis</p> <p> - Folder CONSEL/PHYLOGENY_TEST/ contains the input files for CONSEL</p> <p> - Folder CONSEL/CONSEL_RESULT/ contains the CONSEL test results</p> <p> </p> <p>DADI_ANALYSIS.tar.gz</p> <p>- Input file for dadi analysis and scripts used for dadi modeling</p> <p> </p> <p>DomSufid.sadri.not_scaffolded.fa</p> <p>- Polished genome assembly for Dom Sufid but not scaffolded.</p> <p> </p> <p>DomSufid.sadri.not_scaffolded.sorted.gff</p> <p>- Gene annotation for the assembly DomSufid.sadri.not_scaffolded.fa</p> <p> </p> <p>DomSufid.sadri.ragoo_scaffold.fa</p> <p>- Polished genome assembly for Dom Sufid and scaffolding with RaGOO using the Nipponbare RAPDB1.0 as reference genome.</p> <p> </p> <p>DomSufid.sadri.ragoo_scaffold.sorted.gff</p> <p>- Gene annotation for the assembly DomSufid.sadri.ragoo_scaffold.fa</p> <p> </p> <p>DomSufid.sadri.ragoo_scaffold.repeatmasker.bed</p> <p>- Repetitive DNA coordinates for the assembly DomSufid.sadri.ragoo_scaffold.fa</p> <p> </p> <p>Four_rice_population.vcf.gz</p> <p>- Filtered SNP VCF file used in the basmati population relationship with japonica and aus.</p> <p> </p> <p>MULTIZ_ALIGNMENT.tar.gz</p> <p>- Reference genome alignment using Nipponbare RAPDB1.0 as reference and aligning various Oryza de novo genome assemblies</p> <p> </p> <p>Multi_Oryza_gene_FASTAs.tar.gz</p> <p>- Using the alignments from MULTIZ_ALIGNMENT/ pulled out coding DNA sequences of each Nipponbare RAPDB1.0 gene</p> <p> </p> <p>Obarthii_outgroup_AlignedToBasmatiScaffolded_genome.fa</p> <p>- O. barthii reference genome sequence was aligned to scaffolded Basmati 334 reference genome. For every Basmati 334 genome coordinate was converted into a O. barthii sequence resulting in a basmati-ized O. barthii genome sequence. Not alignable regions were indicated as 'N'. </p> <p> </p> <p>Only_basmati_rice_population.vcf.gz</p> <p>- Filtered SNP VCF file used in the basmati population analysis.</p> <p> </p> <p>Oryza_LTR_DivergenceTime.txt</p> <p>- LTR retrotransposon annotated in various Oryza reference genomes and their estimated insertion time (based on the divergence between the LTRs).</p> <p> </p> <p>TWISST.tar.gz </p> <p>- TWISST input and results file.</p> <p> - Four_rice_population.geno.gz, genotype file generated from the genomic_general from S. Martin and used as input for TWISST analysis.</p> <p> - *.trees.gz phylogenetic trees generated from sliding windows</p> <p> - *.data.tsv sliding window coordinates</p> <p> - *.weights.csv.gz topology weights</p> <p> </p>
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Allen Brain Atlas
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