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19,342 results for “Sequences”
CARACTERIZAÇÃO GENÉTICA DE SOROVARES DE Salmonella spp. ISOLADOS NA AVICULTURA POR WHOLE GENOME SEQUENCING
<p><strong><span>Table S1.</span></strong><span> </span><span>Metadata for the 21 <em>Salmonella</em> <em>enterica</em> genomes used to perform phylogenetic analysis.</span></p> <p><strong><span>Table S2.</span></strong><span> Comprehensive overview of sequences of targeted serovars present in the genomic database by prescribed selection criteria. </span></p>
CARACTERIZAÇÃO GENÉTICA DE SOROVARES DE Salmonella spp. ISOLADOS NA AVICULTURA POR WHOLE GENOME SEQUENCING
<p><strong><span>Table S1:</span></strong><span> Metadata for the 317 <em>Salmonella</em> Heidelberg genomes retrieved from the Enterobase database to perform genomic analysis in this study.</span></p>
1000 Genomes Data: Sequencing Data in BAM Format - LPA Region
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Narrative development sequences from comic strips used in the Storytelling in Sequence Task (STST) - Tâche de Narration d'Histoires en Séquence (TNHS)
<p>The stimuli presented in la Tâche de Narration d'Histoires en Séquence (TNHS) correspond to 9 sequences of 6 comic-style images. These sequences were developed by Sophie Courchesne and Sonia Routhier, under the supervision of Marion Fossard, as part of a master's thesis in speech-language pathology at Laval University, Quebec, Canada.<br> <br>The TNHS - or Storytelling in Sequence Task (TSST) in English - was subsequently introduced in an article by Fossard, Achim, et al. published in 2018 in the journal Frontiers in Psychology.<br> <br> To cite this material:<br> <br>Courchesne, S., & Routhier, S. (2009). Tâche de narration d'histoires en séquence et son analyse référentielle (Master's essay, Université Laval). Université Laval, Québec, Canada.<br> <br>Fossard M, Achim AM (co-first authors), Roussier-Vercruyssen L, Gonzalez S, Bureau A, Champagne-Lavau M. (2018). Referential choices in a collaborative storytelling task: Discourse stages and referential complexity matter. Frontiers in Psychology: Language Sciences, vol. 9, article 176. doi: 10.3389/fpsyg.2018.00176</p> <p><br>TERMS OF USE: This material belongs to Marion Fossard and her research team. It may be used for research or clinical purposes if the following conditions are met: <br>Commercial use is prohibited; <br>The source must be cited in any publication resulting from the use of this material; <br>No changes may be made to the material without the agreement of Marion Fossard (marion.fossard@unine.ch);<br>Any clinical use must recognise the limitations of the test.</p>
Genome Sequence of Colletotrichum karsti isolated from rose leaves exhibiting anthracnose symptoms in Potchefstroom, South Africa.
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Tutorial-Next-Generation-Sequence-Analysis
<p>This upload contains .fastq and reference genome files for learning and practicing NGS analysis.</p>
Sequencing of fractionated marine nanoparticles from Station ALOHA (HOT319)
<p>Seawater from 25m at Station ALOHA in the North Pacific Subtropical Gyre was filtered through a 0.2 µm filter and separated into extracellular vesicle-enriched and viral-like particle enriched fractions. Particle fractions were sequenced with an Oxford Nanopore R9.4.1 flowcell. Frameshifts introduced from Nanopore sequencing error were corrected using the <em>proovframe </em>algorithm (https://github.com/thackl/proovframe) and Illumina short-read sequences from the identical DNA samples.</p> <p>This dataset includes all proovframe-corrected Nanopore sequences (>1kb) from the two fractions in FASTA format, each fraction divided into multiple gzip-compressed files:</p> <p>EV_enriched_corrected_nanopore_[01-10]: reads from the extracellular vesicle-enriched fraction</p> <p>VLP_enriched_corrected_nanopore_[01-10]: reads from the viral-like particle-enriched fraction</p> <p>Additionally, we provide assembled metagenomic contigs from the cellular (>0.2µm) fraction of the same water sample in HOT319_25m_cellularfraction_assembly.fna.gz</p> <p>The original raw seqence data used to generate these datasets can be obtained from the NCBI sequence read archive (Bioproject PRJNA855972).</p>
Sample data for analysis of FFPE sequencing data
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Sequenced Raw Data: Untreated PTB (UTB) Cases, Completed ATT Treatment PTB (CTB) Cases, and Healthy Controls (HC)
<p>The FastQ datasets were generated using the Illumina MiSeq sequencing platform, specifically targeting the v3-v4 hypervariable region of the 16S rRNA gene, to facilitate high-resolution profiling of microbial communities.</p>
Mutant Harmonia axyridis raw sequencing data of CRISPR/Cas9 induced mutations in the genes laccase2 and scarlet
<p><em>Harmonia axyridis</em> (Pallas), commonly known as the Asian lady beetle, is a native insect species of Asia that has been intentionally introduced to various regions for biocontrol purposes. However, its widespread presence beyond its original release sites suggests a high degree of invasiveness. In this study, we utilized the CRISPR-Cas9 approach to achieve precise genome editing in <em>H. axyridis</em>. Specifically, we targeted two genes in <em>H. axyridis</em>, <em>laccase2</em> and <em>scarlet</em>, knockdown of which orthologs in other insects showed visible phenotypic changes. The knockout <em>laccase2</em> resulted in an early-detectable phenotype but also in lethality. However, we successfully established a viable and genetically stable mutant colony by disrupting the <em>scarlet </em>gene, resulting in beetles with white eyes. Our findings contribute to the expanding knowledge of genetic manipulation in <em>H. axyridis</em> and provide insights into its potential for future research and practical applications for biocontrol and invasive species management.</p>
Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota
<div> <p><span><span>Background</span></span><span><span>: Gut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. </span><span>This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases</span><span>, with each sample sequenced using both 16S and shotgun methods</span><span>. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models.</span></span><span> </span></p> </div> <div> <p><span><span>Results</span></span><span><span>: </span><span>As expected, the results showed that </span></span><span><span>16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The </span></span><span><span>16S </span><span>abundance data was sparser and </span><span>exhibited</span><span> lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, </span><span>partially</span><span> due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their </span><span>PCoAs</span><span>. </span><span>Regarding</span><span> the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not </span><span>demonstrate</span><span> a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques reveal</span><span>ed</span><span> taxa previously associated with CRC development, e.g., </span></span><span><span>Parvimonas</span><span> micra</span></span><span><span>.</span></span><span> </span></p> </div> <div> <p><span><span>Conclusions</span></span><span><span>: </span></span><span><span>Shotgun and 16S sequencing provide two different lenses to examine microbial communities.</span><span> While we have </span><span>demonstrated</span><span> that they can unravel common patterns (including microbial signatures), </span><span>shotgun often gives</span><span> a more detailed snapshot than 16S, both in depth and breadth. </span><span>Instead</span><span>,</span> <span>16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample.</span> <span>Therefore, w</span><span>e recommend choosing one or another sequencing technique before launching a study.</span><span> Specifically, </span><span>s</span><span>hotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is </span><span>more </span><span>suitable for tissue samples</span><span> and</span><span> studies with </span><span>targeted</span> <span>aims</span><span>.</span></span><span> </span></p> </div>
MADDD-seq, a novel massively parallel sequencing tool for simultaneous detection of DNA damage and mutations
<p>The file "data.tar" contains the output of the MADDD-seq pipepline. There is one sub-folder per sample. For each sample, the most important files are:</p> <ul> <li>max_variants_2.adduct.gtf : A GTF file with the location (and details) about each adduct called by the pipepline</li> <li>max_variants_2.DSC.vcf.gz : A VCF (Variant Call File) with information about mutations called.</li> <li>coverage.rds : pre-computed coverage information in binary format to be loaded in R.</li> </ul> <p>To analyze this data, use the following R files: adducts.R, mutations.R and jason-function-2022-04.R</p> <p> </p> <p>The file "kallisto-h5.tar" contains the output of running Kallisto on the regular RNAseq data (for expression level analysis). To analyze this data, use the following R files: Yeast-MNNG-MGT.Rmd and myDESeq2.R</p> <p> </p> <p>The source code of the R files will need to be modified to point at the location of files on the computer being used. These modifications are pointed by comments in the code and are located towards the start of each file.</p>
Orchestrated movement sequences and shape-memory-like effects in pine cones
<p>Supplementary material accompanying the publication "Orchestrated movement sequences and shape-memory-like effects in pine cones" by Horstmann, Speck and Poppinga (2024)</p>
Sequence and assembly artifacts: GRCr8 and Illumina reads from BN
<p>GRCr8 rat reference genome assembly polishing data.</p>
Sequencing reads simulated with ART from Homo sapiens CHM13 chr 21
<p>Sequencing reads simulated with ART from Homo sapiens CHM13 chr 21</p>
Data and code for, "Predicting self-assembly of sequence-controlled copoly- mers with stochastic sequence variation"
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Zircon constraints on the eruptive sequence and magma evolution of rhyolites at South Sister volcano, Oregon
<p>This is the repository for data used for analysis in “Zircon constraints on the eruptive sequence and magma evolution of rhyolites at South Sister volcano, Oregon” submitted to Geochemistry, Geophysics, Geosystems. There are four datafiles. Dechert_TableS1.xlsx includes rock sample unit information and locations. Dechert_TableS2.xlsx includes the <sup>230</sup>Th-<sup>238</sup>U isotope ratios as collected with the Stanford-USGS SHRIMP-RG, the isochron model slope and ages, and the Boehnke model slope and ages (Boehnke et al., 2016). Dechert_TableS3.xlsx includes the trace element and rare earth element concentrations collected with the Stanford-USGS SHRIMP-RG. Dechert_TableS4.xlsx records whole rock data for a split of the zircon rock samples collected at Hamilton Analytical Lab and Washington State University.</p>
Data used in the study "Application of nanopore sequencing for accurate identification of bioaerosol-derived bacterial colonies"
<p>Data used in the study "Application of nanopore sequencing for accurate identification of bioaerosol-derived bacterial colonies." The datasets contain nanopore and Sanger sequencing data (including the electropherograms) as well as EPI2ME and NGSpeciesID analysis.</p>
Laboratory validation of a clinical metagenomic next-generation sequencing assay for respiratory virus detection and discovery
<p>This repository contains data and code used to analyze data for this manuscript: </p> <p><em>Laboratory validation of a clinical metagenomic next-generation sequencing assay for respiratory virus detection and discovery</em></p>
Fig 6 in Whole-Genome Optical Mapping and Finished Genome Sequence of Sphingobacterium deserti sp. nov., a New Species Isolated from the Western Desert of China
Fig 6. Venn diagram depicting orthologous groups of predicted proteins encoded in four sphingobacterial genomes. C1: Sphingobacterium spiritivorum ATCC 33300; C2: Sphingobacterium paucimobilis HER1398; C3: Sphingobacterium thalpophilum DSM11723; and C4: Sphingobacterium deserti ZWT. doi:10.1371/journal.pone.0122254.g006
ScienceDex guides
Understand access before you commit
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.