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19,342 results for “sequence”
Raw data: multispecies amplicon sequencing (Loera, Studer, and Kölliker, 2021, Molecular Ecology Resources)
<p>Grasslands cover close to two fifths of Earth's land. They provide many ecosystem services related to the maintenance of soil integrity, and the regulation of water, carbon and nitrogen flows. Grasslands constitute the basis for sustainable roughage production for ruminant feeding. In Switzerland, grasslands cover more than 70% of the total agricultural land, which highlights their importance in the domestic food production chains.</p> <p>Plant genetic diversity (PGD), a component of biodiversity, influences ecosystem functioning in grasslands. High levels of grassland PGD are related to resistance against invasive plants and yield stabilization during environmental stress (e.g., drought or frost). The PGD of grasses and legumes —the two most economically relevant plant families found in grasslands, which naturally grow in a wide climate spectrum— harbors valuable genetic resources for forage breeding. Nevertheless, most PGD studies of natural or semi-natural grasslands (i.e., grasslands that are not sown) focus on a single or a few related species. Traditional PGD monitoring methods (e.g., simple sequence repeats, or SSRs) are ill-suited for large-scale, multispecies assessments. This limits our ability to study the ecological effects of grassland PGD, its spatiotemporal patterns, and its significance for grassland management.</p> <p>Looking to provide cost-effective tools for multispecies PGD monitoring in grasslands, we performed a sequence capture assay targeting 611 single-copy nuclear loci, followed by multispecies amplicon sequencing (i.e., amplicon sequencing using primer pairs that can be used in multiple species) on eleven selected loci.</p> <p>Our results indicate that multispecies amplicon sequencing is a cost-effective tool for genetic diversity assessment in grassland plant species. Furthermore, the sequence capture data provides the means to extend the number of multispecies amplicons for further research.</p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data : </strong></p> <p>1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis<br> 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation<br> 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s</p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. Immgen_cell_types.cls : Microarray Phase 1 expression</p> <p>2. Immgen_norm_exp_data.gct : Microarray Phase 1 class</p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis</p> <p><br> </p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary:</strong> Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>MDAlab_cDC1_maturation.tar : Docker image used for the analysis</p>
single-nucleus RNA sequencing data from female Aedes aegypti maxillary palp
<p>Single-nucleus RNA sequencing data accompanying Herre*, Goldman* et al. (2022), "Non-Canonical Odor Coding in the Mosquito" (https://doi.org/10.1016/j.cell.2022.07.024)</p> <p>For further analysis see: https://github.com/VosshallLab/Younger_Herre_Vosshall2020/tree/main/snRNAseq_SupplementaryData</p> <p>For raw sequencing files see NCBI BioProject: PRJNA794050</p>
Data for Coral Growth and Sequences for Cyclin-E and G3P Primers for Orbicella faveolata
<p>Cyclin-E and glyceraldehyde 3-phosphate dehydrogenase primer sets were constructed using Primer3 from an annotated<br> transcriptome (Polato et al., 2011). For each sample and gene, reactions were performed in triplicate on a Step One Plus qPCR<br> machine (Applied Biosystems, Waltham, MA), using cDNA of <em>Orbicella faveolata</em> (Ellis & Solander, 1786) as a template. Singlepeak melt curve analysis was performed to test for nonspecific amplification products, but limited sample prevented primer<br> efficiency analysis. To verify primer specificity, the two primers were tested on cDNA from <em>Casseopia xamachana</em>, with no<br> amplification being observed.</p>
Imputed Multiple Sequence Alignment used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'
<p>Multiple Sequence Alignment of imputed SARS-CoV-2 sequences used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'</p>
Metamorphic evolution of the Greater Himalayan Sequence, Bhagirathi Valley (India) through a combination of channel flow and in-sequence shearing
<p>Dataset of a manuscript entitled "Metamorphic evolution of the Greater Himalayan Sequence, Bhagirathi Valley (India) through a combination of channel flow and in-sequence shearing " submitted in Tectonics (AGU)</p>
Data from: Diversity of land snail tribe Helicini (Gastropoda: Stylommatophora: Helicidae): where do we stand after 20 years of sequencing mitochondrial markers?
<p>Sequences of mitochondrial genes revolutionized the understanding of animal diversity and continue to be an important tool in biodiversity research. In the tribe Helicini, a prominent group of the western Palaearctic land snail fauna, mitochondrial data accumulating since the 2000s helped to newly delimit genera, inform species-level taxonomy, and reconstruct past range dynamics. We combined the published data with own unpublished sequences and provide a detailed overview of what they revealed about the diversity of the group. The delimitation of <i>Helix</i> is revised by placing <i>Helix godetiana</i> back in the genus and new synonymies are suggested within the genera <i>Codringtonia</i> and<i> Helix</i>. The spatial distribution of intraspecific mitochondrial lineages of several species is shown for the first time. Comparisons between species reveal considerable variation in distribution patterns of intraspecific lineages, from broad postglacial distributions to regions with a fine-scale pattern of allopatric lineage replacement. To provide a baseline for further research and information for anyone re-using the data, we thoroughly discuss the gaps in the current dataset, focusing on both taxonomic and geographic coverage. Thanks to the wealth of data already amassed and the relative ease with which they can be obtained, mitochondrial sequences remain an important source of information on intraspecific diversity over large areas and taxa.</p>
Complete mitochondrial genome sequence of the Atlantic Mudskipper (Periophthalmus barbarus) (Linnaeus, 1766) (Perciformes: Gobiidae)
<p>The complete mitochondrial genome of the Atlantic mudskipper (<em>Periophthalmus barbarus</em>) was determined in this study. The specimen was collected from Abonnema, Nigeria (4.73075, 6.77565). The complete mitogenome sequence of <em>P. barbarus</em> would be useful for further studies on molecular phylogenetic relationship and population genetics of the subfamily Oxudercinae.</p>
Spatiotemporal variation of the 2010 Yushu Mw 6.9 earthquake sequence: Insight from the 3-D electrical resistivity structure
<p>This dada set will be available in GRL, it was support by the Chinese Earthquake Administration, and it was also supported by the National Natural Science Foundation of China (Grant No. 41674081).</p>
HiFi Metagenomic Sequencing Enables Assembly of Accurate and Complete Genomes from Human Gut Microbiota.
<p>We reported 102 complete metagenome assembled genomes (cMAGs) from five human fecal HiFi sequencing samples.</p> <p>102_cMAGs_fna.tar.gz: Fasta sequence files of 102 cMAGs.</p> <p>gc_skew_figures.tar.gz: GC-skew pattern figures of 102 cMAGs. (SVG format)</p> <p>coverage_plots.tar.gz: Genome coverage plot of 102 cMAGs.</p>
Blastn index for NCBI nr virus sequences 12/2020
<p>Blastn index for NCBI nr virus sequences</p> <p>Updated 12/2020</p>
Whole genome sequence data of Lactiplantibacillus plantarum IMI507027
<p>The present data files are the annotation output from the whole genome sequencing of the strain Lactiplantibacillus plantarum IMI507027. </p>
Arabidopsis thaliana Col-CEN complete Chromosome 2 numt sequences and alignments
<p>Data associated with the assembly of complete chromosome 2 nuclear insertion of mitochondrial DNA (numt) from the <em>Arabidopsis thaliana</em> accession Columbia (Col-CEN). A full report of this project can be obtained in a manuscript titled "<strong>Complete sequence of a 641-kb insertion of mitochondrial DNA in the <em>Arabidopsis thaliana </em>nuclear genome</strong>".</p>
Raw Data for the article: A retrospective molecular epidemiological scenario of carbapenemase-producing Klebsiella pneumoniae clinical isolates in a Sicilian transplantation hospital shows a swift polyclonal divergence among sequence types, resistome and virulome
<p>In this work, we assessed and characterized the epidemiological scenario of carbapenem-resistant Klebsiella pneumoniae strains (CR-Kp) at IRCCS-ISMETT, a transplantation hospital in Palermo, Italy, from 2008 to 2017. A total of 288 K. pneumoniae clinical isolates were selected based on their resistance to carbapenems. Molecular characterization was also done in terms of the presence of virulence and resistance genes. All patients were inpatients from our facility and clinical isolates were collected from several sources, either from infection or colonization cases. We observed that, in agreement with the Italian epidemiological scenario, initially only ST258 and ST512 clade II (but not from clade I) were identified from 2008 to 2011. From 2012 onwards, other STs have been observed, including the clinically relevant ST101 and ST307, but also others not previously observed in other Italian health settings, such as ST220 and ST753. The presence of genes involved in resistance and virulence was confirmed, and a heterogeneous genetic resistance profile throughout the years was observed. Our work highlights that resistance genes are rapidly disseminating between different and novel K. pneumoniae clones which, combined with resistance to multiple antibiotics, can derive into more aggressive and pathogenic multidrug-resistant strains of clinical importance. Our results stress the importance of continuous surveillance of CR Enterobacterales in health facilities so that novel STs carrying resistance and virulence genes that may become increasingly pathogenic can be identified and adequate therapies to adopted to avoid their dissemination and derived pathologies.</p>
Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/ Ya Tsie trial
<p><strong>Background:</strong> Mathematical models predict that community-wide access to HIV testing-and-treatment can rapidly and substantially reduce new HIV infections. Yet several large universal test-and-treat HIV prevention trials in high-prevalence epidemics demonstrated variable reduction in population-level incidence.</p> <p><strong>Methods:</strong> To elucidate patterns of HIV spread in universal test-and-treat trials we quantified the contribution of geographic-location, gender, age and randomized-HIV-intervention to HIV transmissions in the 30-community Ya Tsie trial in Botswana (estimated trial population: 175,664).</p> <p><strong>Results:</strong> Deep-sequence phylogenetic analysis revealed that most inferred HIV transmissions within the trial occurred within the same or between neighboring communities, and between similarly-aged partners. Transmissions into intervention communities from control communities were more common than the reverse post-baseline (30% [12.2 – 56.7] versus 3% [0.1 – 27.3]) than at baseline (7% [1.5 – 25.3] versus 5% [0.9 – 22.9]) compatible with a benefit from treatment-as-prevention.</p> <p><strong>Conclusion:</strong> Our findings suggest that population mobility patterns are fundamental to HIV transmission dynamics and to the impact of HIV control strategies.</p> <p><strong>Funding:</strong> This study was supported by the National Institute of General Medical Sciences (U54GM088558); the Fogarty International Center (FIC) of the U.S. National Institutes of Health (D43 TW009610); and the President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (CDC) (Cooperative agreements U01 GH000447 and U2G GH001911).</p>
Raw Data for the article: Liver Transplantation for Unresectable Intrahepatic Cholangiocarcinoma: The Role of Sequencing Genetic Profiling
<p>Intrahepatic cholangiocarcinoma (iCCA) is a rare and aggressive primary liver tumor, characterized by a range of different clinical manifestations and by increasing incidence and mortality rates even after curative treatment with radical resection. In recent years, growing attention has been devoted to this disease and some evidence supports liver transplantation (LT) as an appropriate treatment for intrahepatic cholangiocarcinoma; evolving work has also provided a framework for better understanding the genetic basis of this cancer. The aim of this study was to provide a clinical description of our series of patients complemented with Next-Generation Sequencing genomic profiling. From 1999 to 2021, 12 patients who underwent LT with either iCCA or a combined hepatocellular and cholangiocellular carcinoma (HCC-iCCA) were included in this study. Mutations were observed in gene activating signaling pathways known to be involved with iCCA tumorigenesis (KRAS/MAPK, P53, PI3K-Akt/mTOR, cAMP, WNT, epigenetic regulation and chromatin remodeling). Among several others, a strong association was observed between the Notch pathway and tumor size (point-biserial <em>rho<sub>pb</sub></em> = 0.93). Our results are suggestive of the benefit potentially derived from molecular analysis to improve our diagnostic capabilities and to devise new treatment protocols, and eventually ameliorate long-term survival of patients affected by iCCA or HCC-iCCA.</p>
Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"
<p>Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"</p>
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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.