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42 results for “MinION”
LevSeq epPCR data from ParPgb LQ from MinION sequencer
<p>This is the data published with the LevSeq preprint (https://doi.org/10.1101/2024.09.04.611255), these data provide a test case for users to confirm that they are able to run the pipelines and also the data to reproduce the results in the preprint. Each folder contains the raw fastq files from the MinION sequencer for a protoglobin variant for several plates, along with the required input file to run LevSeq.</p> <p>The data are basecalled reads, generated using the standard Oxford nanopore sequencing protocol. For experimental methods and details please see the paper.</p> <ol> <li><a href="../api/records/13694463/draft/files/20240421-YL-ParLQ-ep1.csv/content" target="_blank" rel="noopener noreferrer">20240421-YL-ParLQ-ep1.csv</a> is the reference file for one set of plates</li> <li><a href="../api/records/13694463/draft/files/20240502-YL-ParLQ-ep2.csv/content" target="_blank" rel="noopener noreferrer">20240502-YL-ParLQ-ep2.csv</a> is the reference file for the second set of plates</li> <li>20240421.zip is the raw data for 20240421-YL-ParLQ-ep1.csv </li> <li>20240502.zip 20240502-YL-ParLQ-ep2.csv</li> <li><a href="13694463" target="_blank" rel="noopener noreferrer">20240502-YL-ParLQ-ep2.fastq.zip</a> is the combined fastq files for the run from <a href="../api/records/13694463/draft/files/20240502-YL-ParLQ-ep2.csv/content" target="_blank" rel="noopener noreferrer">20240502-YL-ParLQ-ep2</a> for ease of use</li> <li><a href="../api/records/13694463/draft/files/20240422-YL-ParLQ-ep1.fastq.zip/content" target="_blank" rel="noopener noreferrer">20240422-YL-ParLQ-ep1.fastq.zip</a> is the combined fastq files for the run from <a href="../api/records/13694463/draft/files/20240421-YL-ParLQ-ep1.csv/content" target="_blank" rel="noopener noreferrer">20240421-YL-ParLQ-ep1</a> for ease of use</li> </ol>
MinION Reads From a Tomato Source: David Eccles' TEDxWellington 2016 Dataset
<p>This is a dump of the data (i.e. MinION reads) that were produced during an on-stage sequencing run for the TEDxWellington 2016 conference. The data dump also includes presentation slides, associated videos, and a script of the talk that I gave during the conference.</p>
simlib_minion
<p> SYNOPSIS<br> Observing strategies for Large Synoptic Survey Telescope (LSST) (1.) are published in terms of an output from the Operations Simulator (2). The simlib files are derived products that approximately summarize the information in such outputs that can be used with codes like SNANA (3). The Operations Simulator output used here is the `minion_1016_sqlite.db` baseline cadence (4). A description of the data product `minion_1016_sqlite.db` is supplied by the LSST project (5). These simlibs may be used with the SNANA code to simulate LSST observations of transient/variable objects.</p> <p>The products contained are :<br> - minion_1016_DDF.simlib : simlib file for the LSST DDF only<br> - minion_1016_WFD.simlib : simlib file for the LSST WFD fields only<br> - minion_1016_DDF.simlib.COADD : simlib file for the LSST DDF with nightly coadds<br> - minion_1016_WFD.simlib.COADD : simlib file for LSST WFD withe nightly coadds</p> <p>## Code and Inputs</p> <p>This simlib was generated using code from OpSimSummary : https://github.com/rbiswas4/OpSimSummary<br> and SNANA</p> <p>The input data is the opsim output `minion_1016_sqlite.db` (4).</p> <p>## Procedure<br> In generating this simlib:<br> - duplicate visits in the summary table with different proposal IDs have been removed<br> - SNR is calculated using the `filtSkyBrightness` and `FWHMeff` columns, and an atmosphere of 1.2 airmass. But the differences in the atmospheric transmission due to airmass differences are neglected.<br> - This does not treat the overlaps between the fields and the dithers. <br> - The observations were coadded using SNANA:</p> <p>```<br> $SNANA_DIR/bin/simlib_coadd.exe minion_1016_DDF.simlib<br> $SNANA_DIR/bin/simlib_coadd.exe minion_1016_WFD.simlib<br> ```</p> <p>## People involved in generating simlibs from the OpSim output:<br> - R. Biswas<br> - D. Cinabro<br> - R. Kessler</p> <p>## Version Release Notes:<br> This release uses OpSimSummary version 1.2.0 : https://github.com/rbiswas4/OpSimSummary/releases/tag/v1.2.0<br> and SNANA version `v10_52j`.</p> <p>Changes: The changes that were necessary for this release are:<br> - precision of MJD for each visit in the simlib is increased in output. This uniquely identifies each visit with a visit in the OpSim database.<br> - A similar issue in `simlib_coadd.exe` for skysigs coadded and then written out with lower precision leading to slightly inaccurate values for coadds. This was fixed in version SNANA `v10_50b`</p> <p>## Acknowledgements<br> RB would like to thank Lynne Jones, Peter Yoachim, Scott Daniel, Zeljko Ivezic and Alex Kim for useful discussions.<br> ## References<br> 1. P.~A.~Abell {\it et al.} [LSST Science and LSST Project Collaborations], LSST Science Book, Version 2.0, http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:0912.0201.<br> 2. F. Delgado et. al., Proc. SPIE 9910, Observatory Operations: Strategies, Processes, and Systems VI, 991013 (July 15, 2016); doi:10.1117/12.2233630, http://adsabs.harvard.edu/abs/2014SPIE.9150E..15D<br> 3. R. Kessler et. al., Publications of the Astronomical Society of Pacific, Volume 121, Issue 883, pp. 1028 (2009). http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:0908.4280<br> 4. http://ops2.lsst.org/runs/minion_1016/data/minion_1016_sqlite.db.gz , description at https://www.lsst.org/scientists/simulations/opsim/opsim-v335-benchmark-survey</p>
MinION 1D² Reads From Mus musculus GL261 Cell Lines
<p>Called FASTQ and raw FAST5 MinION cDNA reads (1D²) from a murine GL261 neuroblastoma cell line, cultured at the Malaghan Institute of Medical Research, sequenced on a R9.5 flow cell in August 2017 using the LSK309 1D² kit for ligating ONT adapters to cDNA generated using strand-switching primers.</p> <p>The called reads for the entire sequencing run are available:</p> <ul> <li>called_reads_1Dsq_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- called reads from both/all runs (1D²-corrected fastq files only).</li> <li>called_reads_uncorrected_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- uncorrected reads from both/all runs.</li> <li>metadata_called_reads_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- metadata associated with all called sequences (e.g. sequencing_summary.txt)</li> </ul> <p>This dataset only includes a subset of the total reads as raw signal / FAST5 files:</p> <ul> <li>Actb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse beta-actin transcript [<a href="http://asia.ensembl.org/Mus_musculus/Transcript/Summary?db=core;g=ENSMUSG00000029580;r=5:142903234-142903654;t=ENSMUST00000100497">ENSMUST00000100497.10</a>].</li> <li>Ubb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse ubiquitin transcript [<a href="http://asia.ensembl.org/Mus_musculus/Transcript/Summary?db=core;g=ENSMUSG00000019505;r=11:62551171-62553213;t=ENSMUST00000019649">ENSMUST00000019649.3</a>].</li> </ul>
MinION sequence data: MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing
<p>MinION sequencing data that was unmapped by minimap2 for the 11 samples using in the "MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing" paper.</p>
Consensus calling of MinION amplicon reads improves metabarcoding results
<p><strong>Background</strong>: Metabarcoding environmental DNA with high-throughput sequencing is a state-of-the-art method to assess biodiversity and to uncover dark taxa. MinION is the first handheld sequencer that can be taken into the field for on-site metabarcoding. This research aims to answer if bioinformatics can solve the issues that arise because of the higher error rate of MinION data.</p> <p><strong>Results</strong>: Biodiverse samples with a presumed large portion of dark taxa were selected from the Dutch Caribbean. The cytochrome oxidase 1 gene (CO1) is used as a barcode for identification at the species level or higher levels. Generating a consensus sequence from closely related sequences resulted in minimized random errors and increased species identification of 175% compared to unclustered MinION data. Additional to the formulation of the workflow, an ecological analysis was conducted that revealed co-occurrence of species in similar habitats, and that the proportion of dark taxa in the sampled region is 81.87%.</p> <p><strong>Conclusion</strong>: Although the workflow did not attain the results that the existing Illumina workflows do, the potential is evident. The high proportion of dark taxa in the sampled region of Statia and the Saba Bank indicates the need for continued barcoding of species in the Dutch Caribbean to resolve database limitations.</p>
High quality genomes produced from single MinION flow cells clarify polyploid and demographic histories of critically endangered Fraxinus (ash) species
<p>With populations of threatened and endangered species declining worldwide, efforts are being made to generate high-quality genomic records of these species before they are lost forever. Here, we demonstrate that data from single Oxford Nanopore Technologies (ONT) MinION flow cells can, even in the absence of highly accurate short DNA-read polishing, produce high-quality <em>de novo</em> plant genome assemblies adequate for downstream analyses, such as synteny and ploidy evaluations, paleodemographic analyses, and phylogenomics. This study focuses on three North American ash tree species in the genus <em>Fraxinus</em> (Oleaceae) that were recently added to the International Union for Conservation of Nature (IUCN) Red List as critically endangered. Our results support a whole genome triplication at the base of the Oleaceae as well as a subsequent whole genome duplication shared by <em>Syringa</em>, <em>Osmanthus</em>, <em>Olea, and Fraxinus</em>. Finally, we demonstrate the use of ONT long-read sequencing data to reveal patterns in demographic history.</p>
MinION sequencing of SAMD00180470
<p>Data from <a href="https://doi.org/10.1128/MRA.01212-19">https://doi.org/10.1128/MRA.01212-19</a> via <a href="https://ddbj.nig.ac.jp/public/ddbj_database/dra/fastq/DRA008/DRA008776/DRX178031/">https://ddbj.nig.ac.jp/public/ddbj_database/dra/fastq/DRA008/DRA008776/DRX178031/</a></p> <p>This is a <em>re-issue</em> of <a href="../records/4534098">https://zenodo.org/records/4534098</a> due to the original files consisting of multiple concatenated bzip2 files, they contain far too many `BZh9` sequences (expected in the bzip compression level 9 header), resulting in a number of tools such as FastQC and Trimmomatic failing to process them with strange errors. </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>
Processed MinION genome sequencing data for strains ILHA G3AG5 and ILHA G3AA5
<p>Strain G3AA5 datasets include:</p> <p>Galaxy2750, Galaxy2754, Galaxy3522, Galaxy3523, Galaxy3524, Galaxy3541</p> <p> </p> <p>Strain G3AG4 datasets include:</p> <p>Galaxy2404, Galaxy2408, Galaxy3517, Galaxy3518, Galaxy3519, Galaxy3539</p>
Sequencing summaries from pfhrp2 detection and characterization using the MinION sequencer
<p>This dataset comprises sequencing summaries generated by the MinKnow software during sequencing runs related to the project "Portable and cost-effective genetic detection and characterization of <em>Plasmodium falciparum hrp2</em> using the MinION sequencer." </p> <p>This dataset was used in the generation of figures for the manuscript related to this project, and can be used with the Jupyter Notebook for figure generation provided in the project's <a href="https://github.com/sjsabin/minion_hrp2_project">github</a>.</p>
Mafuneetal2023_metadata_MinION
<p>These files are associated with the MinION sequence data analyzed and discussed in Mafune et al 2023. The manuscript focuses on root-associated communities in canopy soil and forest floor soil environments in the old-growth temperate rainforests of Washington State, USA. </p>
MinION sequencing data of mtDNA from BH10 cells
<p><span>Mitochondrial DNA (mtDNA) recombination in animals has remained enigmatic because of its uniparental inheritance and subsequent homoplasmic state, which excludes the biological need for genetic recombination, as well as limits tools to study it. However, molecular recombination is an important genome maintenance mechanism for all organisms, most notably being required for double-strand break repair. To demonstrate the existence of mtDNA recombination, we have taken advantage of a cell model with two different types of mitochondrial genomes and impaired ability to turn over broken mtDNA. The resulting excess of linear DNA fragments caused increased formation of cruciform mtDNA, appearance of heterodimeric mtDNA complexes and recombinant mtDNA genomes, detectable by Southern blot. Combining our observations with previously published work, we propose that the mitochondrial replisome can catalyze microhomology-mediated recombination of linear mtDNA ends, thus rendering a specialized mitochondrial recombinase unnecessary. The error-proneness of this system is likely to contribute to the formation of pathological mtDNA rearrangements.</span></p>
MinION plasmid deep long read sequencing for sequence verification
<p>We sequenced four plasmid constructs, each with a whole MinION flowcell, for use in developing and testing a sequence verification procedure. The resulting pipeline can sequence verify plasmid constructs, generating a consensus sequence with associated confidence of each base, adhering to strict acceptance criteria . The data contained herein are a subset of the complete data; 30 fast5 files for each plasmid, to be used as example data.</p> <p> </p> <p>datHL_001519: BCRxV.TF.1<br> datHL_001521: BCRxV.VSVG.1<br> datHL_001617: BCRxV.GagPolRev.1<br> datHL_001620: BCRxV.VSVG.1_mutant</p>
Minion sequencing experiment, Macoma balthica, Vancouver BC
<p>Results from Minion sequencing experiment, for Macoma balthica from Vancouver BC. </p> <p>Pool of 10 individuals. Target : mitochondrial genomes. </p> <p>Vancouver_2023-03-16.tar.gz contains the raw data. </p> <p>fastq_runid_5b77d028c5e2872dda6946458aed8ae808cd60f8_[0to3]_0.fastq.gz are the base-called data (guppy, fast). </p>
High quality genomes produced from single MinION flow cells clarify polyploid and demographic histories of critically endangered Fraxinus (ash) species
Open the record for dataset details and reuse information.
MinION sequencing data of mtDNA from BH10 cells
Open the record for dataset details and reuse information.
Nanopore raw signal data to Benchmarking the MinION: Evaluating long reads for microbial profiling
<p>This dataset contains original raw signal data of a blind study sequencing mock community samples. The samples are differing by nucleic acid quantification technique and composition, and consist of twelve prokaryotic species each. The raw signal data permits future basecalling and may assist in development of applications requiring signal level data.</p> <p>Sample 1 (Barcode 1): heterogenous, adjusted by ddPCR</p> <p>Sample 2 (Barcode 2): heterogenous, adjusted by Qubit</p> <p>Sample 3 (Barcode 3): equimolar, adjusted by ddPCR</p> <p>Sample 4 (Barcode 3): equimolar, adjusted by Qubit</p> <p>FLO-MIN106, SQK-LSK108; Flowcell FAH89600</p> <p>Please consider citing our paper</p> <p>Leidenfrost, R.M., Pöther, D., Jäckel, U. <em>et al.</em> Benchmarking the MinION: Evaluating long reads for microbial profiling. <em>Sci Rep</em> <strong>10, </strong>5125 (2020). https://doi.org/10.1038/s41598-020-61989-x</p> <p>https://doi.org/10.1038/s41598-020-61989-x</p>
A method for determining the origin of crude drugs derived from animals using MinION, a compact next-generation sequencer
<p><span>We evaluated whether MinION, an inexpensive, portable sequencer, can be applied for identifying the origin of crude drugs</span> <span>derived from animals</span><span>.</span><span> Standard and nonstandard crude drugs with different species of origin were examined. In addition, the standards mixed with nonstandard samples </span><span>were used</span><span>.</span><span> As a target gene, cytochrome c oxidase I was amplified and sequenced. The Fast mode results had a slightly lower match ratio than High-accuracy mode, but the animals of origin were correctly determined by BLAST for all samples. For antler velvet derived from <em>Rangifer tarandus</em>, even the sequences were aligned based on <em>Cervus elaphus</em>, the animal of origin was determined correctly. Minor contents could be detected from mixtures of two animals, if the mixtures contained at least 19:1 mtDNA when the coverage allele-fraction threshold was 0.05. By contrast, in Fast mode, two sequences could not be separated due to the low accuracy of the base-calling in each read. For field work, the species of origin of crude drugs could be identified, by only simple DNA extraction and library preparation. Therefore, MinION appears to be a convenient tool for identifying the origin of crude drugs derived from animals.</span></p>
S29: Our little minions, part 2: small tools with major impact
<p>Session Call at https://doi.org/10.5281/zenodo.1420252.</p> <p>Video done by <a href="https://www.fiverr.com/jaxjordon">jaxjordon</a> Experienced Video Editor, Graphic Designer and Photoshop Editor (www.fiverr.com)</p> <p>CAA Logo by <a href="https://caa-international.org/about/logos/">https://caa-international.org/about/logos/</a></p>
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OpenNeuro
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