Find research datasets worth reusing

opencc-by-4.0Jan 2020View details →

Deep splicing plasticity of the human adenovirus type 5 transcriptome as a driver of virus evolution nanopore data 48hpi

Adenovirus infected MRC5 cells direct RNA sequencing of the mRNA using nanopore. From the paper Deep splicing plasticity of the human adenovirus type 5 transcriptome as a driver of virus evolution. Both the uncorrected fastq files and the lordec corrected files together with the normalised illumina data used to correct the nanpore data are here.

opencc-by-4.0Mar 2021View details →

xPore: Identification of differential RNA modifications from nanopore direct RNA sequencing

xPore is a Python package for identification and quantification of differential RNA modifications from direct RNA sequencing. The detailed usage is documented at <a href="https://xpore.readthedocs.io/en/latest/">https://xpore.readthedocs.io/en/latest</a>, while all scripts and source code are available at <a href="https://github.com/GoekeLab/xpore">https://github.com/GoekeLab/xpore</a>. All the preprocessed datasets used in the paper are provided here.  Please cite our paper below when using these data. Ploy N. Pratanwanich et al. "Detection of differential RNA modifications from direct RNA sequencing of human cell lines." bioRxiv (2020).

opencc-by-4.0Feb 2023View details →

Supplementary dataset to publication: Oxford nanopore technologies - a valuable tool to generate whole-genome sequencing data for in silico serotyping and the detection of genetic markers in Salmonella, Thomas et al 2023

Bacteria of the genus Salmonella pose a major risk to livestock, the food economy, and public health. Salmonella infections are one of the leading causes of food poisoning. The identification of serovars of Salmonella achieved by their diverse surface antigens is essential to gain information on their epidemiological context. Traditionally, slide agglutination has been used for serotyping. In recent years, whole-genome sequencing (WGS) followed by in silico serotyping has been established as an alternative method for serotyping and the detection of genetic markers for Salmonella. Until now, WGS data generated with Illumina sequencing are used to validate in silico serotyping methods. Oxford Nanopore Technologies (ONT) opens the possibility to sequence ultra-long reads and has frequently been used for bacterial sequencing. In this study, ONT sequencing data of 28 Salmonella strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the in silico serotyping tools SISTR and SeqSero2 compared to traditional slide agglutination tests. Moreover, the detection of genetic markers for resistance against antimicrobial agents, virulence, and plasmids was studied by comparing WGS data based on ONT with WGS data based on Illumina. Based on the ONT data from flow cell version R9.4.1, in silico serotyping achieved an accuracy of 96.4 and 92% for the tools SISTR and SeqSero2, respectively. Highly similar sets of genetic markers comparing both sequencing technologies were identified. Taking the ongoing improvement of basecalling and flow cells into account, ONT data can be used for Salmonella in silico serotyping and genetic marker detection.

opencc-by-4.0May 2021View details →

Structure and dynamics of water confined in cylindrical nanopores with varying hydrophobicity

Supporting data for Phil. Trans. R. Soc. A 379: 20200403 (2021) <a href="https://doi.org/10.1098/rsta.2020.0403">http://doi.org/10.1098/rsta.2020.0403</a>

opencc-by-4.0Oct 2023View details →

Tracking Down Chimeric Assemblies In The TrackIt DNA Ladder Using Nanopore Sequencing

<h2>Dataset Description</h2>These files represent two different LSK114 sequencing runs on a TrackIt 1kb Plus DNA Ladder sample, and associated data analysis.<ul><li>July 20 2023 Flongle Run (191 Mb; 465k reads)<ul><li>pod5_files_2023-Jul-20_DAE_DNA_Ladder.tar.gz - raw POD5 format files</li><li>called_2023-Jul-20_DAE_DNA_Ladder_duplex.bam - duplex called reads, called using dorado v4.0 with the 2023-09-22 bacterial methylation model</li><li>sequence_QC_2023-Jul-20_DAE_DNA_Ladder.pdf - sequence length / quality QC plots</li><li>LAST_2023-Jul-20_DAE_DNA_Ladder_reads_vs_reference.tar.gz - Alignment summary statistics from LAST mapping of reads to their associated reference</li><li>lengths_summary_2023-Jul-20_DAE_DNA_Ladder.txt - Length / QC summary statistics</li></ul></li><li>October 12 2023 P2 Solo Run (1.95 Gb, 1.11M reads)<ul><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_fail.tar.gz - raw POD5 format files (all failed reads)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_000-059.tar.gz - raw POD5 format files (passed reads, bundle #000-059)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_060-119.tar.gz - raw POD5 format files (passed reads, bundle #060-119)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_120-179.tar.gz - raw POD5 format files (passed reads, bundle #120-179)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_180-222.tar.gz - raw POD5 format files (passed reads, bundle #180-222)</li><li>called_2023-Oct-12_DNA-Ladder-1kbplus_duplex.bam - duplex called reads [October 12, 2023], called using dorado v4.0 with the 2023-09-22 bacterial methylation model</li><li>sequence_QC_2023-Oct-12_DNA-Ladder-1kbplus.pdf - sequence length / quality QC plots</li><li>LAST_2023-Oct-12_DNA-Ladder-1kbplus_reads_vs_reference.tar.gz - Alignment summary statistics from LAST mapping of reads to their associated reference</li><li>lengths_summary_2023-Oct-12_DNA-Ladder-1kbplus.txt - Length / QC summary statistics</li><li>ladder_seqs.fa - assembled DNA ladder sequences, based on simplex reads</li></ul></li></ul><h3>Methods</h3><h3>Sample preparation</h3>Preparation of DNA for sequencing was carried out following the ONT Ligation Sequencing DNA V14 (SQK-LSK114) protocol, with modifications to exclude DNA repair, and keeping the sample in the same 1.5ml tube to reduce sample loss.<h4>Tris-buffered Saline (TBS) buffer preparation</h4><ol><li>1M stock of NaCl was made by adding 2.922g of NaCl into a 50 ml Falcon tube, then made up to 50 ml with MilliPore water</li><li>A 50 mM TBS stock was created by adding 750 μl 1M NaCl solution to a 15 ml Falcon tube, then made up to 15 ml using Qiagen Elution Buffer (EB, i.e. 10 mM Tris-HCl at pH 8.0)</li><li>The pH was confirmed to be 7.9-8.1 using a pH indicator strip (e.g. MColorpHast 6.5 - 10.0; MER1095430001)</li></ol><h4>End prep</h4><ol><li>1 μg DNA ladder (i.e. 10 μl of 0.1 μg / μl DNA ladder) was transferred into a 1.5ml Eppendorf DNA LoBind tube</li><li>The volume was topped up to 43.5 μl with TBS (i.e. 33.5 μl TBS)</li><li>3.5 μl Ultra II End-prep Reaction Buffer and 3 μl Ultra II End-prep Enzyme Mix was added</li><li>After mixing by gentle pipetting, the mixture was incubated at RT for 5 minutes, then 65 \degrees for 5 minutes</li></ol><h4>Bead cleanup</h4><ol><li>The mixture was combined with 60 μl Ampure XP beads, and incubated on a rotator mixer at RT for 5 minutes</li><li>The tube was transferred to a magnetic rack [https://www.printables.com/model/532085-open-walled-magnetic-rack]</li><li>After the supernatant became clear and colourless, supernatant was pipetted off</li><li>The magnetic beads were washed twice with 150 μl of an 80% ethanol solution</li><li>The sample was dried briefly for 30s, then eluted in 60 μl TBS</li></ol><h4>Adapter ligation and final bead cleanup</h4><ol><li>To the sample tube was added 25 μl ONT Ligation buffer (LNB), 5μl NEBNext Quick T4 DNA Ligase (reduced from the protocol-suggested 10μl because that was all that was left in the tube), and 5μl ONT Ligation Adapter (LA)</li><li>The tube was mixed by gentle pipetting, spun down for 1-3s on a mini centrifuge, then incubated for 10 minutes at RT</li><li>The mixture was combined with 40 μl Ampure XP beads (100μl Ampure XP beads were used for the Flongle sample), and incubated on a rotator mixer at RT for 5 minutes</li><li>The tube was transferred to a magnetic rack [https://www.printables.com/model/532085-open-walled-magnetic-rack]</li><li>After the supernatant became clear and colourless, supernatant was pipetted off</li><li>The magnetic beads were washed twice with 250 μl of ONT Long Fragment Buffer (LFB) for the P2 Solo run, and 250μl ONT Short Fragment Buffer  (SFB) for the Flongle run</li><li>The sample was dried briefly for 30s, then eluted for 10 minutes at 37 \degrees in 15 μl ONT Elution buffer (EB)</li></ol><h4>Addition of sequencing library buffers</h4><ol><li>A flow cell was prepared by flushing with ONT Flow Cell Flush (FCF) mixed with ONT Flow Cell Tether (FCT). For the P2 Solo, I used 500 μl of a 1170μl FCF solution that had 30μl FCT added to it; for the Flongle I used 60μl of a 117μl FCF solution that had 3 μl FCT added to it</li><li>1 μl of the eluted library was quantified on a Quantus Fluorometer, and approximately 50 fmol (assuming 1kb average length) was transferred to a new 1.5μl tube</li><li>For the P2 Solo run, the volume was topped up to 32 μl TBS; for the Flongle run, the volume was topped up to 12 μl TBS</li><li>To the sample tube was added ONT Sequencing Buffer (SB; P2 Solo - 100μl; Flongle - 30μl) and ONT Library Beads (LIB; P2 Solo - 68μl; Flongle - 20μl)</li><li>The flow cell was re-flushed with additional FCF/FCT mixture (500 μl for the P2 Solo; 30 μl for the Flongle)</li><li>The sequencing library was then added to the flow cell (200 μl for the P2 Solo; 30 μl for the Flongle)</li><li>The prepared flow cell was left for 10 minutes to allow the library to settle before starting sequencing</li></ol><h4>DNA Sequencing and basecalling</h4><ol><li>Sequencing was carried out using MinKNOW v23.04.6, sequencing in fast mode at 400 bases per second with a 20bp minimum sequence length and   5 kHz sampling rate, with reads output as POD5 files</li><li>The Flongle flow cell was run for a full standard run length (24h), whereas the PromethION flow cell was run for 1.5 hours (after which the  counts of 15kb reads exceeded 200)</li><li>Sequenced reads were recalled in standard (simplex) mode using Dorado v0.4.0 and the 2023-09-22 bacterial methylation model [res_dna_r10.4.1_e8.2_400bps_sup@2023-09-22_bacterial-methylation]</li></ol><h3>Bioinformatics Analysis of Ladder Sequences </h3><h4>Sequence assembly</h4>Assembly process for bands that are 3k in length and greater (done on LFB-depleted P2 Solo sequences): <ol><li>Filter >q20 reads for a 100bp region around the target length (e.g. 4950-5050bp for the 5k band) [High quality reads were not sufficient for the 15kb band; all reads were needed]</li><li>Chop the reads up with a 1000bp overlap (e.g. 3000bp for the 5k band). This works around a Canu expectation that any read overlaps should be less than X% of the read.</li><li>Assemble the reads with Canu v2.2 [#REF], treating them as "pacbio" reads (for correction and homopolymer compression), with the GenomeSize parameter set to the expected band length (e.g. GenomeSize=5000).</li><li>Extract the first reported assembled contig.</li><li>Map the contig to the nanopore adapter sequences, and trim to exclude any matching sequence.</li></ol>[Canu has a default genome size and read length cutoff of 1kb, and performs poorly on sequences shorter than this]  Assembly process for bands under 3k in length (done on LFB-depleted P2 Solo sequences):<ol><li>Filter >q20 reads for a 100bp region around the target length (e.g. 4950-5050bp for the 5k band) [High quality reads were not in sufficient abundance for the 100bp band; all reads were needed]</li><li>Assemble using a<a href="https://gitlab.com/gringer/bioinfscripts/-/blob/master/fastx-kassembler.pl"> kmer-based de-bruijn assembler</a>, trimming off low-count kmers</li><li>Extract the first reported trimmed assembled chain</li><li>Map the assembled chain to the nanopore adapter sequences, and trim to exclude any matching sequence</li><li>Use web BLASTn [#REF] to help trim any additional trailing non-matching sequence</li></ol><h4>Mapping</h4><ol><li>Use a <a href="https://gitlab.com/gringer/bioinfscripts/-/blob/master/fastx-kmapper.pl">kmer-based lightweight mapper</a> to map reads to assembled bands</li><li>Created LAST mismatch matrix using `last-train` on the 5k reads together, using the full assembled ladder sequences as a reference: last-train -Q 1 ladder_seqs.fa 5k_reads.fq.gz</li><li>Mapped all reads to the assembled ladder sequences (only the reference corresponding to the most likely band source), retaining (for each read) the mapping that had the longest combined proportion of read and reference sequence mapped: lastal -p bacterial.mat -P 10 ladder_seqs.fa reads_2023-Oct-12_DNA-Ladder-1kbplus_called_all.fq.gz | \     ~/scripts/maf2csv.pl | \     awk -F ',' '{print $0","($8/100 * $13/100)}' | \     sort -t ',' -k 16rg,16 | sort -t ',' -k 1,1 -u | sort -t ',' -k 1r,1 | \     perl -pe 's/,[^,]*$/\n/' > LAST_reads_vs_ladder_longestMatch.csv.gz</li></ol>

opencc-by-4.0Feb 2022View details →

Quantum Monte Carlo data for 4He inside Ar-Preplated MCM-41 Nanopores

Raw quantum Monte Carlo data for argon pre-plated MCM-41 nanopores with L = 50 Å. Details of the simulations are published here: https://journals.aps.org/prb/abstract/10.1103/PhysRevB.102.144505

opencc-by-4.0Aug 2022View details →

Control panel created from 30 Nanopore sequencing data from the Human Pangenome Reference Consortium

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 Nanopore sequencing data from Human Pangenome Reference Consortium to the GRCh38 reference genome (obtained from <a href="https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0;tab=objects">here</a>) with <a href="https://github.com/lh3/minimap2">minimap2</a> version 2.24. When you use these control panels and publish, do not forget to credit to <a href="https://humanpangenome.org/data-use-protocol/">HPRC</a>!

opencc-by-4.0Nov 2017View details →

Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces: ftDFT code

This is the complete ftDFT code used to generate the results reported in our Nov 2017 Nanoscale article "Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces",DOI: 10.1039/C7NR07218J.

opencc-by-4.0Jul 2024View details →

Composite Hedges Nanopores

Nanopore sequencing FASTQ data of Composite Hedges Nanopores: A High INDEL-Correcting Codec System for Rapid and Portable DNA Data Readout. It includes a text and an image file data encoded by CHN codec.

opencc-by-4.0May 2018View details →

Fly through a 2.3Mb Nanopore Sequence

A spiral representation of the longest sequence found by <a href="https://doi.org/10.1101/312256">Alex Payne et al.</a>, which at the time of upload was the longest observed public single-pore nanopore sequence. This video shows about 30kb of the sequence at once, and zooms through the sequence at about 20kb per second in the animated GIF, or about 48kb per second in the AVI file.

opencc-by-4.0Oct 2024View details →

Dataset related to publication: Nanoporous Film Layers to Enhance the Performance of Passive Radiative Cooling Paint Mixtures

Dataset related to the publication: Giuseppe Emanuele Lio, Sara Levorin, Atakan Erdoğan, Jérémy Werlé, Alain J. Corso, Luca Schenato, Diederik S. Wiersma, Marco Santagiustina, Lorenzo Pattelli, Maria Guglielmina Pelizzo. Nanoporous Film Layers to Enhance the Performance of Passive Radiative Cooling Paint Mixtures, accepted and in publishing on International Journal of Thermophysics. DOI: 10.1007/s10765-024-03439-8 The repository contains the temperature, relative humidity, irradiance, sky temperature and net cooling power measurements in the file named "DATA_IJT_Nanoporous_Film_ Layer.txt" and the python script (Data_analizer.py) to analyze them as done in the paper.   The repository also contain the Matlab workspace (IJT_data.mat)and code (Data_R_ATR_analyzer)to evaluate all the sample reflectance and FTIR measurements. Moreover, the extra measurements performed on the Celgard thin film are available in the workspace "Celgard_UV_VIS_NIR_T.mat" and "R_Celgard.mat". Moreover in Version 2 all the used data for Reflectance and Transmittance are also available in "txt" format for a simple and fast usage.    <h2>Notes (English)</h2> <div> <table> <tbody> <tr> <td> This work was partly supported by <ul> <li>European Project 21GRD03 PaRaMetriC, which received funding from the European Partnership on Metrology, co-financed by the European Union's Horizon Europe Research and Innovation Programme, and from the Participating States.</li> <li>European Union - PON Research and Innovation 2014-2020 in accordance with Article 24, paragraph 3a), of Law No. 240 of December 30, 2010, as amended, and Ministerial Decree No. 1062 of August 10, 2021</li> </ul> </td> </tr> </tbody> </table> </div>

opencc-by-4.0Mar 2021View details →

xPore: Identification of differential RNA modifications from nanopore direct RNA sequencing - SGNEx data

opencc-zeroJun 2020View details →

Draft genome assembly of a Japanese Oikopleura dioica male individual (O3), using Nanopore long reads.

This draft assembly was used to validate the chrY scaffolds of the OSKA2016 reference genome in the publication  “A genome database for a Japanese population of the larvacean Oikopleura dioica”, Development Growth and Differentiation, Wang and coll., 2020 (in press). It is provided as supplemental data for the reproducibility of this work; please note that no further polishing has been done to correct sequencing errors. Genome sequence reads were produced on a MinION sequencer (Oxford Nanopore Technologies) using high-molecular weight DNA from a male individual of the Oikopleura dioica species of zooplankton. The individual was related to the laboratory strain established from a western Japanese population that was used to produce the OSKA2016 reference genome. The raw reads were basecalled with the Guppy software version 3.3.0 using its dna_r9.4.1_450bps algorithm, and deposited in the European Nucleotide Archive (Study ID: PRJEB38559). The draft assembly was made with the Flye software version 2.7 with the options --genome-size 65m and --min-overlap 3000.

opencc-by-4.0Jun 2020View details →

CyclomicsSeq: Accurate detection of circulating tumor DNA using nanopore consensus sequencing

CyclomicsSeq is a protocol designed to produce and sequence long DNA concatemers with a linear repetition to acquire high accuracy consensus reads. In this dataset, we used CyclomicsSeq for sequencing TP53 in cell-free DNA of healthy individuals and of head and neck cancer patients and for sequencing synthetic TP53 DNA sequences that mimic the length of cell-free DNA. This dataset contains data (mainly base calls of the backbone and the insert) of 32 nanopore sequencing runs.

opencc-by-4.0Nov 2023View details →

Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry

This data publication is based on the metadata and datasets underlying the manuscript "Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry" (<a href="https://doi.org/10.1021/acsanm.3c04330">https://doi.org/10.1021/acsanm.3c04330</a>) Included are the datasets used, raw and processed data of Adsorption measurements (Water, Ar 87K, N2 77K), Water Intrusion measurements, NMR Relaxometry and solid state MAS NMR measurements. More information can be found in the Readme file.

opencc-by-4.0Dec 2024View details →

Local grafting heterogeneities control water intrusion and extrusion in nanopores

Datasets and scripts used to generate data.

opencc-by-4.0Oct 2021View details →

Alignment files for coverage benchmarks: Illumina and Nanopore sequencing datasets

<ul> <li>cpara-illumina-noseq.bam and cpara-ont-noseq.bam: BAM files produced aligning the raw reads produced respectively by Illumina NextSeq and ONT Nanopore sequencing of an isolate of C. parapsilosis to evaluate the coverage calculations using real datasets.*</li> <li>HG00258.bam: Exome sequencing from the 1000 Genomes Project (Clarke et al 2016 <a href="https://doi.org/10.1093/nar/gkw829">https://doi.org/10.1093/nar/gkw829</a>).</li> <li>panel_01.bam: targeted sequencing of a Human gene panel of 16 genes.*</li> </ul> * Sequences and qualities have been removed

opencc-by-4.0Feb 2022View details →

Genome-wide mapping of individual replication fork velocities using nanopore sequencing

These data are related to the "Genome-wide mapping of individual replication fork velocities using nanopore sequencing" manuscript by Theulot, Lacroix et al. (https://doi.org/10.1038/s41467-022-31012-0) and to the GitHub repository NanoForkSpeed (https://github.com/LacroixLaurent/NanoForkSpeed). BT1_run4.tar.gz contains the nanopore reads from a typical experiment as fast5 files. BT1_run4_mega.zip contains the result of the BrdU basecalling as described on the GitHub page in a modified bam file format as described on the GitHub page NanoForkSpeed/BrdU_Basecalling.. BT1_run4_Megalodon_00_smdata.rds contains the result of the parsing function for the modified bam file as described on the GitHub page NanoForkSpeed/BrdU_Basecalling. BT1_run4_Megalodon_00_NFS_data.rds contains the result of the fork detection procedure on the BT1_run4_Megalodon_00_smdata.rds file as described on the GitHub page NanoForkSpeed/Forks_Detection. BT1_run4_merged_NFS_data.rds contains the result of the NFS_merge function as described on the GitHub page NanoForkSpeed/Forks_Detection.

opencc-by-4.0May 2022View details →

Chenopodium Oxford Nanopore libraries

Oxford Nanopore libraries of four diploid Chenopodium species (2n=2x=18), C. acuminatum, C. iljinii, C. pamiricum and C. suecicum. The 50,000 longest ON reads in each library.