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19,345 results for “Sequencing”
Comparative plot about two sequences of integers very similar to each other: A348960 vs A127034
<p>This is the behavior comparison graph between the integer sequences registered in OEIS (The On-Line Encyclopedia of Integer Sequences) with codes: A348960 & A127034 respectively.</p> <p><strong>1)</strong> The sequence A348960 obeys the formula: </p> <p> <span class="math-tex">\(a_{n }=\lfloor(log(\pi n!)\rfloor. \)</span> For any non-negative integer such that <span class="math-tex">\(n\geq0\)</span></p> <p><strong>2) </strong>The sequence A127034 obeys the formula:</p> <p><span class="math-tex">\(a_{n}=\lfloor log(n!)/log(11)\rfloor.\)</span> For any non-negative integer such that <span class="math-tex">\(n\geq0\)</span></p> <p>In this particular case we're conducting the study for the following n-values: i<span class="math-tex">\(1\leq n\leq 60.\)</span> </p> <p> </p>
Raman spectra of the Adenoma-Carcinoma-Sequence in a mice model
<p>In the following, a short desciption for each csv files:</p> <ol> <li>Meta data: includes information about mice ID, scans collected from each mouse, location of extracted scans, activity of P53 gene, mouce gender, tissue type.</li> <li>MSpectra: contains mean spectra of tissue types with respect to each extracted scan.</li> <li>TissueLabels: describes different divisions of tissue types;e.g. normal vs abnormal, normal vs HB vs Karzinom, normal vs HB vs adenoma vs carcinoma</li> <li>Wavenumbers: includes Raman spectra wavenumbers. </li> </ol>
(Fastq Files) Amplicon sequencing of ama1 and mdr1 to track within-host P. falciparum diversity in Kilifi, KENYA
<p>These data were generated from amplicon sequencing of <em>Plasmodium falciparum</em> <em>ama1 </em>and<em> </em><em>mdr1</em> genes in samples collected from Kilifi, at the coast of Kenya.</p> <p>The two papers that reference these data will soon be included here:</p> <ol> <li> The Journal of Infectious Diseases - https://doi.org/10.1093/infdis/jiac144</li> <li>Wellcome Open Research - https://wellcomeopenresearch.org/articles/7-95</li> </ol> <p>Two objectives were explored:</p> <ol> <li>To determine temporal changes in the genetic diversity of malaria parasites in asymptomatic and febrile infections.</li> <li>To track within-host parasite diversity, throughout treatment in a clinical drug trial.</li> </ol>
Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.
<p><span>This repository contains the supplementary data used in the publication Roche et al., 2024 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences), including (1) the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia, modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016), and (2) the pictures series used to build isochrone contour maps.</span></p> <p><span><span>1.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The seismicity catalogues from Cahuilla, Yellowstone and West Bohemia are modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue and filtering and modifications can be found in the associated publication.</span></p> <p><span> Dataset list:</span></p> <ul> <li><span>Cahuilla Catalogues (modified from Ross et al., 2019): </span></li> <ul> <li><span>Original data: File name: VR_sup_0021_Cah_All</span></li> <li><span>Filtered data: File name: VR_sup_0022_Cah_Filter</span></li> </ul> <li><span>Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0023_Boh_08_All</span></li> <li><span>Filtered data: File name: VR_sup_0024_Boh_08_Filter</span></li> </ul> <li><span>Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0025_Boh_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0026_Boh_14_Filter</span></li> </ul> <li><span>Yellowstone Catalogs (modified from Shelly et al., 2013): </span></li> <ul> <li><span>Original data: File name: VR_sup_0027_Yell_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0028_Yell_14_Filter</span></li> </ul> </ul> <p><span>The files are text files tab-delimited, with the following headers:</span></p> <ul> <li><span>Index: 1 by default</span></li> <li><span>Easting(m): hypocenter Easting in meters </span></li> <li><span>Northing(m): hypocenter Northing in meters </span></li> <li><span>Depth(m): hypocenter depth in meters </span></li> <li><span>Mw: magnitude</span></li> <li><span>Relative Time(s): date of the origin time in the format </span></li> </ul> <p><span><span>2.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The pictures series are images of seismicity at a regular time interval for each studied step.</span></p> <p><span>Dataset list:</span></p> <ul> <li><span>Step C1: File name: VR-sup-0012-Pictures_C1.</span></li> <li><span>Step C2: File name: VR-sup-0013-Pictures_C2.</span></li> <li><span>Step C3: File name: VR-sup-0014-Pictures_C3.</span></li> <li><span>Step C4: File name: VR-sup-0015-Pictures_C4.</span></li> <li><span>Step Y1: File name: VR-sup-0016-Pictures _Y1.</span></li> <li><span>Step B1I: File name: VR-sup-0017-Pictures _B1I.</span></li> <li><span>Step B1II: File name: VR-sup-0018-Pictures _B1II.</span></li> <li><span>Step B2: File name: VR-sup-0019-Pictures _B2.</span></li> <li><span>Step B3: File name: VR-sup-0020-Pictures _B3.</span></li> </ul> <p><span>Each file contains a series of pictures in JPEG format. For each picture, events in the overlying and underlying segments are indicated in blue and red. The full circles represent the events occurring during the last interval. The empty circles represent the events occurring in the previous intervals.</span></p> <p><span>If you find these data useful in your research, please cite Roche et al. (2024), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).</span></p>
RNA sequencing data for bleomycin exposed THP-1 macrophages
<p>This dataset contains normalized counts matrices, from dds_deseq objects, from DeSeq2 analysis of RNA sequencing data, from THP-1 macrophages exposed to multiple doses of bleomycin in the range of 0-100µg/ml for 24H, 48H or 72H.</p>
Deep learning-based earthquake catalog of the 2022 MW 6.9 Chihshang, Taiwan, earthquake sequence
<p>On 18 September 2022, the MW 6.9 Chihshang earthquake struck the southern Longitudinal Valley, Taiwan. We use SeisBlue, a deep-learning platform/package, to extract the two-month earthquake sequence from September to October 2022, including the MW 6.5 Guanshan foreshock, the MW 6.9 mainshock, over 14,000 aftershocks, and 866 focal mechanisms from two sets of broadband networks. For more details, please refer to our research article published at TAO (Sun et al., 2024; https://doi.org/10.1007/s44195-024-00063-9). The refined SeisBlue earthquake, FMS, and 20-year M3+ relocated CWA earthquake catalogs obtained in this study are listed here.</p> <ol> <li>The refined, deep-learning-based earthquake catalog of the 2022 Mw 6.9 Chihshang, Taiwan, earthquake sequence contains 5,151 seismic events with event time, location and error information, local and moment magnitudes, and hypoDD location. </li> <li>The FMS (focal mechanism solution) catalog is obtained by the P-wave polarities of 14 broadband stations and the FPFIT program (Reasenberg & Oppenheimer, 1985). 865 out of 1629 FMSs with at least six readings of P-wave polarity, F-fator <span>≤ </span>0.1 (F <span>< </span>0.5 for a good fit), and errors of strike, dip, and rake are all <span>< </span>20<span>°</span>, respectively, are listed in the attached FMS catalog.</li> <li>The 2001-2020 3D-hypoDD-relocated M3+ CWA earthquake catalog: We applied the HypoDD program (Waldhauser & Ellsworth, 2000) to the CWA (Central Weather Administration (CWA, Taiwan), 2012) catalog with P- and S-wave arrivals and obtained 5862 M3+ events between 2001 and 2020 for eastern Taiwan. The 3D velocity models used for this catalog are the local models from Kuo-Chen et al. (2012).</li> </ol> <p> </p>
Protein haplotype sequences obtained by ProHap from the Human Pangenome Reference Consotruim dataset
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the Human Pangenome Reference Consotruim (HPRC), first release (<a href="https://github.com/human-pangenomics/hpp_pangenome_resources">https://github.com/human-pangenomics/hpp_pangenome_resources</a>), 44 samples. We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts.</p> <p>This repository contains one database created using all 43 samples of the HPRC release (the haplotypes of the sample NA21309 did not encode any non-canonical sequences), and then a database for each of the 43 samples separately. No filtering on allele frequency or haplotype frequency was applied in any of the databases. The complete configuration file for the ProHap run is attached to this repository.</p> <p>There is one compressed directory for each of the databases, containing the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>For this dataset, only the simplified format is provided. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
Genotyping-by-sequencing (GBS) dataset for genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))
<p>These vcf-files constitute underlying raw data material for the manuscript "Genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))". For more detailed information please consult the README file in the repository.</p>
Genomes plasmids MDR B. fragilis ONT sequence read files in fastq format
<p>Supporting data for the manuscript <em>Complete genome assembly of clinical multidrug resistant Bacteroides fragilis isolates enables comprehensive identification of antimicrobial resistance genes and plasmids.</em></p> <p>Oxford Nanopore reads demultiplexed with <a href="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwjNjqL5tY7iAhUawMQBHZHfDasQFjAAegQIAhAB&url=https%3A%2F%2Fgithub.com%2Frrwick%2FDeepbinner&usg=AOvVaw0wikvIUagLuFV38CwKZtia">Deepbinner</a> v0.2.0 and base-called (with demultiplexing) using Albacore v2.3.3. Barcodes and adapters were removed with <a href="https://github.com/rrwick/Porechop">Porechop</a> v0.2.4 with the --discard_middle option.</p> <p>Data from each isolate was produced from two runs per isolate. Data for the individual runs are included here. They can easily be concatenated eg with cat. Runs are named TVS_01,. TVS_02, TVS_03 and TVS_04.</p> <p>Fast5 (only demultiplexed with deepbinner and basecalled with albacore) as well as illumina reads and genome assemblies can be found via the NCBI bioproject accessions:</p> <p>Isolates, NCBI bioproject accession no:</p> <p>CCUG4856T, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA525024">PRJNA525024</a></p> <p>BFO17, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244943">PRJNA244943</a></p> <p>BFO18, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244944">PRJNA244944</a></p> <p>S01, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244942">PRJNA244942</a></p> <p>BFO42, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA253771">PRJNA253771</a></p> <p>BFO67, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA254401">PRJNA254401</a></p> <p>BFO85, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA254455">PRJNA254455</a></p> <p> </p> <p><strong>md5sum's (also found in the file md5.md5):</strong></p> <p>135d0570a1e49e25c8fde59f321cca68 BFO17_TVS_03_99377.barcode02_trimmed.fastq.gz<br> 3c6ca800a1f735937c0cffccd263fb82 BFO18_TVS_01_97673.barcode03_trimmed.fastq.gz<br> dc37823950f529d3a859b7a7c514af8c BFO18_TVS_03_99377.barcode03_trimmed.fastq.gz<br> abd707404f9ebbc38652e45ed378ca21 BFO42_TVS_02.barcode10_trimmed.fastq.gz<br> 9761e9ab082e276624c5778dcbeaffd2 BFO42_TVS_04.barcode10_trimmed.fastq.gz<br> ffc27009c0f7fead1af84ce045dbe5f3 BFO67_TVS_02.barcode09_trimmed.fastq.gz<br> 06c363a7feeeb88f9d195b3769b37f2b BFO67_TVS_04.barcode09_trimmed.fastq.gz<br> 5553c95cc98f4b9d4cfb38c4f8f8f037 BFO85_TVS_02.barcode08_trimmed.fastq.gz<br> b1a8013cba7a079cee6d3bdd6cd97ff2 BFO85_TVS_04.barcode08_trimmed.fastq.gz<br> 8169225219a5fb20935d5f0304aa80c5 CCUG4856T_TVS_01_97673.barcode01_trimmed.fastq.gz<br> a509b0ae912a798a91e677795198c1c6 CCUG5846T_TVS_03_99377.barcode01_trimmed.fastq.gz<br> 8a9d2eb8b626a6e87ed267d31aa220e3 S01_TVS_01_97673.barcode04_trimmed.fastq.gz<br> 69e239a17becc25a4f103dfc3bf5886a S01_TVS_03_99377.barcode04_trimmed.fastq.gz</p>
Variant Data from Pooled Sequencing of Hybrid Kiwifruit
<p>Variant data from pooled sequencing of hybrid <em>Actinidia</em> families segregating for fruit size and Vitamin C Content.</p>
DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea
<p><strong><em>This version is to stay up to date with the improvements and increase in 16S rRNA gene sequences (SSU) added to the GTDB release 220. Please read this post for the stats on the updates. </em></strong><strong><em>https://gtdb.ecogenomic.org/stats/r220 </em></strong><strong><em>.</em></strong><strong><em> </em></strong></p> <p><strong><em>There has been no change to the RDP-RefSeq reference database please use previous versions.</em></strong></p> <p><strong><em>If anyone has concerns with MAG extracted 16S rRNA gene contamination concerns, then I suggest that they contact the curators of GTDB themselves because it is outside of my role with these resources designed for DADA2 usage only. </em></strong></p> <p><strong><em>Another concern that was raised was the orientation of the DB sequences, to get past this problem please use the tryRC = TRUE argument in the assignTaxonomy command within DADA2, this will search your ASVs in the reverse complement as well. </em></strong></p> <p>The bacterial and archaeal 16S rRNA gene sequence databases were collated from various sources and formatted to use the "assignTaxonomy" command within the DADA2 pipeline. The data was converted to suite DADA2 format by Alishum Ali.</p> <ol> <li>Genome Taxonomy Database (GTDB): The new version of our dada2 formatted GTDB reference sequences now contains 58102 bacteria and 3672 archaea full 16S rRNA gene sequences. If you wonder why there are fewer species with 16S rRNA, that is because some metagenomics-assembled genomes (MAGs) lack the 16S gene and thus cannot be extracted. The database was downloaded from <a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release95/">https://data.ace.uq.edu.au/public/gtdb/data/releases/</a> on 24/10/2024. Please read the release notes and file descriptions. </li> </ol> <p>The formatting to DADA2 was done using simple awk bash scripts. The script takes as input a fasta file and a tab-delimited taxonomy file (slightly edited to remove special characters) and then it outputs a fasta file with all 7 taxonomy ranks separated by ";" as required for DADA2 compatibility. Additionally, we have concatenated the unique sequence GTDB ID to the species entry (but replaced the "." with an " _". We see this as an important QC step to highlight the issues/confidence associated with short-read taxonomy assignment at the finer rank levels.</p> <p>Also, this update includes two other files that you can use with the assignTaxonomy and addSpecies commands in DADA2.</p>
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
<p>Bacteria of the genus <em>Salmonella</em> pose a major risk to livestock, the food economy, and public health. <em>Salmonella</em> infections are one of the leading causes of food poisoning. The identification of serovars of <em>Salmonella</em> 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 <em>in silico</em> serotyping has been established as an alternative method for serotyping and the detection of genetic markers for <em>Salmonella</em>. Until now, WGS data generated with Illumina sequencing are used to validate <em>in silico</em> 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 <em>Salmonella</em> strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the <em>in silico</em> 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, <em>in silico</em> 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 <em>Salmonella in silico</em> serotyping and genetic marker detection.</p>
Graphic Illustration of Neal Platt's Talk: Targeted sequencing of pathogen DNA from museum specimens
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Neal Platt at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
18S V4 rDNA sequences organized at the OTU level for the SOMLIT-Astan time-series (2009-2016)
<p>The present file includes metadata for each 18S V4<strong> rDNA OTU</strong> from the SOMLIT-Astan time series (2009-2016) including the following fields: <strong>amplicon</strong> = identifier of the representative (most abundant) sequence; <strong>total</strong> = total number of reads; <strong>spread </strong>= number of samples in which the OTU has been found; <strong>cloud </strong>= number of unique sequences constituting the OTU; <strong>sequence</strong> = nucleic acid sequence of the representative sequence; <strong>length</strong> = length of the representative sequence; <strong>quality </strong>= minimum expected error observed for the representative sequence, divided by sequence length; <strong> taxonomy</strong> = taxonomic path assigned to the representative sequence; <strong>identity</strong> = percentage of identity of the representative sequence to the closest reference sequence from PR2; <strong>references</strong> = best hit reference sequence(s) ; <strong>RA090107_02:RA161222_3 </strong>= 375 samples from January 2009 to December 2016, the first two number are the year followed by the month and the day (sampling twice a month during 8 years). Values after “_” indicate the size of the filter used for the filtration: 02 for 0.2 µm and 3 for 3 µm.</p> <p>Generation of 18S V4 rDNA Operational Taxonomic Units (OTUs) from the raw sequencing reads and their assembly into a OTUtable was obtained according to the following pipeline (https://doi.org/10.5281/zenodo.5791089). The V4 region was extracted from the 18S rDNA reference sequences from PR2 v4.12 (Guillou et al., 2013) with Cutadapt. The representative sequences of each OTU were compared to these V4 reference sequences by pairwise global alignment (usearch_global VSEARCH’s command). Each OTU inherits the taxonomy of the best hit or the last common ancestor in case of ties. OTUs with a score below 80% similarity were considered as unassigned (Mahé et al., 2017; Stoeck et al., 2010).</p> <p>The final dataset (filtered OTU table) contains 375 samples (sampled twice per month from 2009 to 2016) with a total of ~30 million sequence reads and 21,418 OTUs.</p>
High-precision Aftershock Locations and Fault Planes of the 2016-2017 Central Italy Sequence
<p>The earthquake catalog includes high-precision hypocenter relocations for 390,334<br> earthquakes recorded during the 2016-2017 Amatrice (Central Italy) <br> earthquake sequence. The relative locations were computed by double-difference inversion of a <br> combination of INGV phase picks and cross-correlation differential <br> times measured from correlated seismograms with correlation coefficients > 0.7.</p> <p>Planes of normal faults (idx=1-5) are derived from PCA analysis of 2 months of aftershock <br> locations in the CAT4 catalog following large events. Surfaces of detachment faults (idx=7-10) are derived from mapping out the location of correlated earthquakes. </p> <p>Citation: Waldhauser, F., Michele, M., Chiaraluce, L., Di Stefano, R., & Schaff, D. P. (2021). Fault planes, fault zone structure and detachment fragmentation resolved with highprecision aftershock locations of the 2016-2017 central Italy sequence. Geophysical Research Letters, 48, e2021GL092918. https://doi.org/10.1029/2021GL092918</p>
Updated NLL-SSST-coherence earthquake relocation catalog for the 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake sequence.
<p>CSV catalog and plots of NLL-SSST-coherence earthquake relocations of 17583 events M≥1 2020.01.01 to 2021.08.20 for the 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake sequence.</p> <p>This dataset is an update of the relocations presented in the pre-print article:<br> The 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake: relocated seismicity shows rupture of a complete shear-crack system<br> https://eartharxiv.org/repository/view/1904/</p> <p> </p> <p>Plots show map, view from south and view from N73E of NLL-SSST-coherence relocations events with error-ellipsoid, semi-major axis ≤ 5 km. Caption (adapted from https://eartharxiv.org/repository/dashboard/1904/ ; Fig 2):</p> <p>Event color showing hypocenter depth and symbol size proportional to magnitude. Re-picked Mw6.5 hypocenter and its proxy (mean hypocenter of 3 well constrained foreshocks) indicated by small and large, dark red, cross symbols, respectively. SHmin and SHmax show directions of regional minimum and maximum compressive stress, respectively; the intermediate principal stress axis is vertical. Seismic stations shown as dark gray tetrahedrons. Brown lines show faults from the Quaternary fault and fold database for the United States. Background topography image from OpenTopgraphy.org. Red lines show mapped surface ruptures from http://www.nbmg.unr.edu/Geohazards/Earthquakes/MonteCristoRangeEQData.html and https://doi.org/10.1785/0220200371</p>
NLL-SSST-coherence earthquake relocation catalogs for the Parkfield and Lone Pine, California earthquake sequence.
<p>CSV tables of the final, NLL-SSST-coherence earthquake relocation catalogs for the Parkfield and Lone Pine, California earthquake sequence.</p> <p>These datasets are from relocations presented in the article<br> High-precision, earthquake location using source-specific station terms and inter-event waveform similarity<br> submitted to Journal of Geophysical Research Solid Earth</p> <p> </p> <p> </p>
Data from: Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones
<p>This vcf file contains 7.023.689 SNPs and 870 honey bee samples, as described in the paper "Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones" by Wragg et al., available at https://doi.org/10.1101/2021.09.20.460798 as preprint.</p> <p>Eight hundred and seventy haploid drone samples from several honey bee subspecies hybrids were sequenced and aligned to the HAv3.1 reference genome. Sequence read alignment and genotyping quality filters were used to obtain a selection of 7.023.689 high-quality SNPs. The file Diversity_Study_629_Samples.txt corresponds to the 629 unique samples that were used for the diversity study described in the paper and can be used to recreate the restricted diversity dataset using bcftools or an equivalent software.</p> <p>Having sequenced haploid drones, heterozygous SNPs resulting from duplicated regions could be filtered out and the data is phased.</p>
Discoba protein sequences for protein structure predictions
<p>Comprehensive database of Discoba protein sequences, gathered for the purpose of improving protein structure predictions of Discoba species (including <em>Trypanosoma </em>and <em>Leishmania</em>) by AlphaFold and RoseTTAFold. Originally gathered for use with: https://github.com/zephyris/discoba_alphafold</p>
Benchmarking bioinformatic tools for amplicon-based sequencing of norovirus
<p>This repository contains associated datasets and accession numbers for a study entitled '<strong>Benchmarking bioinformatic tools for amplicon-based sequencing of norovirus'</strong>. The scripts for this project can be found on the GitHub project<a href="https://github.com/ahfitzpa/Benchmarking-bioinformatics-norovirus-amplicons"> page</a>. </p> <p>Expected composition tsv files are the OTU tables for each simulation performed (001-010). OTU IDs in this case are the expected taxonomy with the associated accession numbers. Samples are numbered 1-40, including the simulation number. Expected sequences fasta files contain the sequences used as input for each simulation, without primers or Illumina adapter sequences.</p> <p>Amplicons were generated using the following primers:</p> <p><strong>GI Primers </strong><br> GISKF: CTG CCC GAA TTY GTA AAT GA 4<br> GISKR: CCA ACC CAR CCA TTR TAC A 5<br> <br> <strong>GII Primers </strong><br> G2SKF: CNT GGG AGG GCG ATC GCAA 8<br> G2SKR: CCR CCN GCA TRH CCR TTR TAC AT</p> <p>In this study, three databases and multiple classifiers were compared. Here we include the taxonomy and fasta files for each database; noronet =NoroNet RIVM, calicinet= HuCat CDC and custom, randomly generated database. Fasta files for the classifiers include the GI/GII primers listed above in a 5-3 orientation. </p> <p>The tags.txt file contains the Illumina adapters used for the simulation component of the study.</p>
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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.