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Dataset results
373 results for “Nanopore”
BeerDEcoded Nanopore Sequencing workshop of la trappe Dez. 2019
<p>The BeerDEcoded workshops are a project of the Street Science Community. https://streetscience.community/projects/beerdecoded/<br> We are extracting DNA from different Beers and sequence them using the Nanopore sequencing technique. The data contains the reads extracted from the beer of the brand la trappe and was performed on 08.12.2019. <br> The sequencing was performed on a MinION using a Flow Cell and the MinKNOW software. The Software collects sequencing data in real-time and has a base-calling integrated to convert the fast5 files into fastq files.</p>
Mosquito Tagging Using DNA-Barcoded Nanoporous Protein Microcrystals
<p>Contains raw data for the publication titled 'Mosquito Tagging Using DNA Barcoded Nanoporous Protein Microcrystals'.</p>
BeerDEcoded Nanopore Sequencing Run of Chimay Now. 2019
<p>The BeerDEcoded workshops are a project of the Street Science Community. https://streetscience.community/projects/beerdecoded/<br> We are extracting DNA from different Beers and sequence them using the Nanopore sequencing technique. The data contains the reads extracted from the beer of the brand Chimay and was performed on 26.11.2019. <br> The Sequencing was performed on a MinION using a Flow Cell and the MinKNOW software. The Software collects sequencing data in real-time and has a base-calling integrated to convert the fast5 files into fastq files.</p>
BeerDEcoded Nanopore Sequencing Run of Chimay Now. 2019 Flongle
<p>The BeerDEcoded workshops are a project of the Street Science Community. https://streetscience.community/projects/beerdecoded/<br> We are extracting DNA from different Beers and sequence them using the Nanopore sequencing technique. The data contains the reads extracted from the beer of the brand Chimay and was performed on 26.11.2019. <br> The Sequencing was performed on a MinION using a flongle Flow Cell and the MinKNOW software. The Software collects sequencing data in real-time and has a base-calling integrated to convert the fast5 files into fastq files.</p>
DeepSelectNet: Deep Neural Network Based Selective Sequencing for Oxford Nanopore Sequencing
<p>Curated dataset for the manuscript named "DeepSelectNet: Deep Neural Network Based Selective Sequencing for Oxford Nanopore Sequencing".</p> <p>Five publicly available datasets sequenced on ONT MinION/GridION were used for the experiments (see below for original sources). These datasets contained raw signal data in single-FAST5 format (one file per each read), which were converted to BLOW5 format using slow5tools to enable convenient and efficient file manipulation. Then, 40,000 reads containing at least 4500 signal samples were extracted from each dataset. From each dataset, 20,000 reads are for training (<species>/train-<species>.blow5) and the rest for testing (<species>/test-<species>.blow5). Basecalled reads for the dataset used for testing are also available (test-<species>.fastq). Guppy version 6.1.3 under dna_r9.4.1_450bps_hac mode was used. The reference genomes are also given (<species>/<species>-ref.fasta)</p> <p>Original datasets are from the following sources:<br> SARS-CoV-2: https://community.artic.network/t/links-to-raw-fast5-fastq-data-for-artic-protocol/17<br> Zymo Metagenome: https://github.com/LomanLab/mockcommunity<br> Chlamydomonas: https://sra-download.ncbi.nlm.nih.gov/traces/era20/ERZ/003237/ERR3237140/Chlamydomonas_0.tar.gz<br> Saccharomyces cerevisiae: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA510813</p>
DATASET: Facet-Dependent Formation and Adhesion of Au Oxide and Nanoporous Au on Poly-Oriented Au Single Crystals
<p>This project contains all the data shown in the manuscript or SI titled 'Facet-Dependent Formation and Adhesion of Au Oxide and Nanoporous Au on Poly-Oriented Au Single Crystals' (doi:10.26434/chemrxiv-2023-nf7v3). For each figure, a folder was created in which the corresponding data can be found.</p>
Nanotiming: single-molecule based, telomere-to-telomere DNA replication timing profiling by nanopore sequencing
<p>Dataset for the manuscript "Nanotiming: telomere-to-telomere DNA replication timing profiling by nanopore sequencing" by Theulot et al ,2024 (<span>https://doi.org/10.1038/s41467-024-55520-3</span>) related to the github repository (https://github.com/LacroixLaurent/NanoTiming)</p> <ul> <li>WT_rep3.tar.gz contains fast5 file from an experiment where yeast BT1 strain was grown for one doubling time with 5µM BrdU then DNA was sequenced on R9.4.1 ONT flowcell</li> <li>mod_mapping.bam contains the bam file resulting from the BrdU base calling with megalodon (v2.2.9) using our BT1 reference genome and our BrdU aware model for base-calling</li> <li>WT_rep3_nanoT.bed.gz contains the reads coordinates from the mod_mappings file</li> <li>WT_rep3_nanoT_alldata.rds contains the BrdU profiles for each reads of the mod_mappings file, with the BrdU signal binned in 1kb non overlaping windows</li> <li>WT_rep3_nanoT.rds contains the genomic BrdU signal profiles by 1kb non overlaping windows</li> <li>TeloLengthDataNanoT.rds contains all the telomeric sequences extracted from the experiments reported in the Figure 4 and S19 to S23 of the manuscript with the associated filtering information and nanotiming signal.</li> </ul> <p> </p>
Nanopore data for Gecarcoidea natalis
<p>Unsheared DNA was processed using Nanopore LSK108 kit and sequenced on a used MIN-FLO106 flowcell for approximately 16 hours (overnight run). Basecalling used Albacore version 2.3.1.</p>
Nanopore Data for the blacklip abalone (Haliotis rubra)
<p>Each nanopore fasta file represents data output from a single Nanopore run. Metadata.xls file provides aAdditional details on how the DNA was extracted, library prep kits used and sequencing metrics.</p>
Dataset for "Progessive improvement of the Australian blacklip abalone (Haliotis rubra) genome assembly with Nanopore long reads, hybrid meta assembly and haplotig purging
<p>This Zenodo archive contains the blacklip abalone genome assemblies and and their BUSCO completeness calculations. Genome annotation (gff3 format), CDS, protein sequences and Orthofinder2 output were also included.</p>
Dataset of "Synaptic Response of Fluidic Nanopores: The Connection of Potentiation with Hysteresis"
<p>This dataset supports the article published in <em>ChemPhysChem</em>.</p> <p>"Synaptic Response of Fluidic Nanopores: The Connection of Potentiation with Hysteresis"</p> <p> </p> <p>Raw data for the article "Synaptic Response of Fluidic Nanopores: The Connection of Potentiation with Hysteresis". For further details see the readme.txt file.</p>
Nanopore deep sequencing as a tool to characterize and quantify aberrant splicing caused by variants in inherited retinal dystrophy genes
Open the record for dataset details and reuse information.
Hieracium alpinun PAI33838 (2n = 2x = 18) Oxford Nanopore Technology sequences library
<p>Sample 1 000 000 reads (trimmed).</p>
Gas-Induced Drying of Nanopores
<p>Research data associated with the publication: </p> <p><em>J. Phys. Chem. Lett.</em> 2020, 11, 21, 9171–9177 <a href="https://doi.org/10.1021/acs.jpclett.0c02600">https://doi.org/10.1021/acs.jpclett.0c02600</a></p> <p>Description of the data format can be found inside the folders.</p>
Code and Data for "Multiple re-reads of single proteins at single-amino-acid resolution using nanopores"
<p>The primary structures containing data and analysis products are peptidereads_fig2.mat (for figure 2) and peptiderereads_fig3.mat (for figure 3). The main analysis scripts for these data structures are callvariants_fig2.m and reread_analysis_fig3.m respectively. Data for figures S6 (S6_reread_data.dat) and S8 (S8_hetero_data.dat), and the analysis script used to produce figure S6 (S6_reread_analysis.m) are also included. Other files are dependencies of these main scripts.</p> <p> </p> <p>The fields in peptidereads_fig2 are as follows:</p> <p> </p> <p> </p> <p><strong>folder, eventnum, reducedStart, reducedEnd, suspicious, hasreread:</strong> notes for internal use</p> <p><strong>variant:</strong> the true identity of the single-amino-acid substitution variant</p> <p><strong>data: </strong>the ion current data for each read downsampled to 5 kHz.</p> <p><strong>omit: </strong>whether the read was omitted from analysis due to length</p> <p><strong>relativeDNAend</strong>: the index in the data where the DNA portion of the read ends.</p> <p><strong>relativeLinkerEnd:</strong> the index in the data where the linker portion of the read ends.</p> <p><strong>DNAlevels, Peplevels, Alllevels:</strong> extracted ion current levels for the DNA region, the peptide region, and everything.</p> <p><strong>cal: </strong>the multiplicative and additive constants applied to calibrate the read</p> <p><strong>caldata: </strong>the data with calibration constants applied</p> <p><strong>cons0D, cons0W, cons0G, cons0DNA: </strong>initial guesses for consensuses based on hand curation of data.</p> <p><strong>pepDcons0, pepWcons0, pepGcons0:</strong> the portion of the handmade consensus with the variant levels.</p> <p><strong>pepDcons, pepWcons, pepGcons:</strong> the portion of the iterated consensus with the variant levels.</p> <p><strong>inhandconsensus: </strong>whether the read was used in generation of the inital guess consensuses.</p> <p><strong>inconsensus</strong>: whether the read was used in generation of either the initial guess or iterated consensuses.</p> <p><strong>confidence:</strong> the relative likelihood of each variant assigned to the read</p> <p><strong>incalls:</strong> whether the data was used in variant calling (i.e., not used in consensus generation)</p> <p><strong>params</strong>: the analysis parameters used</p> <p> </p>
BeMAGIC_Nanoporous films and ultra-thin films for magnetoionics and surface charging experiments
<p>BeMAGIC ITN (GA861145)_Nanoporous films and ultra-thin films for magnetoionics and surface charging experiments. Results from UAB, IFW, TUC, KIT, SPIN-ION, TTS</p>
Nanopore sequencing of plasmid cleavage fragments produced with type III CRISPR-associated nucleases NucC, Can1 and Can2
<p>Included datasets were generated in the study "<strong>Sequence-specific capture and concentration of viral RNA </strong><strong>by type III CRISPR system enhances diagnostic"</strong> by Nemudraia et al., 2022</p> <p> </p> <p>For questions contact: Artem Nemudryi (artem.nemudryi@gmail.com) or Blake Wiedenheft (bwiedenheft.com)</p>
Simultaneous profiling of histone modifications and DNA methylation via nanopore sequencing
<p>Datasets that contain a minimum of nanopore reads sufficient for hidden Markov model training and for evaluating the performance of our computational tool - nanoHiMe at simultaneously calling CpG and/or adenine methylation on individual nanopore reads.<em> Ecoli</em>_PCR_amplicons_100k.tgz, <em>Ecoli</em>_PCR_MSssI_100k.tar.gz and <em>Ecoli</em>_PCR_pA-Hia5_100k.tar.gz are used for training new parameters of the emission distributions of individual <em>k</em>-mers from DNA template without modification, with fully methylated CpGs, and with partially methylated adenines, respectively. nanoHiMe_H3K27me3.fast5.tgz are the nanopore sequencing reads from H3K27me3 nanoHiMe-seq experiments in GM12878 cells and used for evaluating the performance of nanoHiMe at jointly calling CpG and adenine methylation.</p>
Ultra-stable self-standing Au nanowires/TiO2 nanoporous membrane system for high-performance photoelectrochemical water splitting cells - Dataset
<p>Dataset of results presented in <em><strong>Mater. Horiz.</strong></em>, 2022,<strong>9</strong>, 2797-2808: Ultra-stable self-standing Au nanowires/TiO<sub>2</sub> nanoporous membrane system for high-performance photoelectrochemical water splitting cells.</p> <p>E. W. would like to acknowledge Alexander von Humboldt Foundation, Bonn, Germany, for funding the postdoctoral fellowship, and the Polish National Agency For Academic Exchange, Polish Returns Programme (Project no. BPN/PPO/ 2021/1/00002), and the National Science Centre, Poland (Project no. 2022/01/1/ST5/00019) for financial support of the project. G. S. would like to acknowledge the National Science Centre, Poland (Project no. 2016/23/B/ST5/00790). The authors thank C. Erdmann for performing the transmission electron microscopy measurements.</p>
Dataset for WGS of TiLV using Nanopore
<p>This zip file contains scripts, initial fastq files, assembled genomes (public and from this study) as well as bioinformatics intermediate files used for this study.</p> <p>File Structure and Descriptions</p> <p>├── 01.Filter.sh : Script to perform read filtering from raw fastq.gz<br> ├── 02.RefGenome_Assembly.sh : Script to perform reference-mapping genome assembly from the trimmed fastq file<br> ├── 03.Cleanup.sh : Script to reorganize folder and clean-up intermediate files<br> ├── 04.GapAnalysis.sh : Script to extract individual viral segment and perform QUAST analysis to calculate number of gaps<br> ├── 05.Phylogenetic.sh : Script to combine TiLV genome from public database (availabel in Phylogenetic folder) and generate Maximum likelihood tree<br> ├── backup : Raw Fastq (basecalled with Guppy super accuracy mode)<br> ├── Consensus : Assembled genome in fasta format<br> ├── Coverage: Contig coverage information<br> ├── Filtered_Fastq: Quality and length-filtered fastq used for generating the genome assembly<br> ├── Filtered_Segment.fasta: Viral segments from samples that have been filtered for high completeness ( > 80% genome without gap)<br> ├── GapAnalysis.tsv: Table containing the gap information for each assembled viral segment of each sample<br> ├── Normalised_Bam: BAM alignment file used for variant calling<br> ├── Phylogenetic: Contains crucial whole genome sequences of publicly available virus downloaded from NCBI<br> ├── primer-schemes: Contains reference sequence used for reference-based mapping<br> ├── Raw_Bam: Raw BAM alignment file prior to normalisation. Used to estimate the read depth observed for each sample and each viral segment<br> ├── ReadDepth.tsv: Table file showing the read depth of each sample and its viral segment<br> ├── Sequencing_Stat.tsv: Sequencing statistics of samples before and after length/quality filter with NanoFilt<br> └── TILV.tre: Newick file containing the maximum likelihood tree generated from fasttree (-gtr -nt)</p> <p>8 directories, 10 files</p> <p> </p>
ScienceDex guides
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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.