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19,342 results for “Sequences”
FIG. 9 in Foraminiferal biostratigraphy, facies and sequence stratigraphy analysis across the K-Pg Boundary in Hazara, Lesser Himalayas (Dhudial Section)
FIG. 9. — Sketch showing the platform evolution through different stages. The platform geometry in the early Campanian time (Stage I) during deposition of Kawagarh Formation was characterized by a steeper ramp. An incised valley and sequence boundary developed in Stage II during Selandian and the platform configuration became much gentler during the Thanetian (Stage III) as the accommodation space filled up.
FIG. 7 in Foraminiferal biostratigraphy, facies and sequence stratigraphy analysis across the K-Pg Boundary in Hazara, Lesser Himalayas (Dhudial Section)
FIG. 7. — Chart showing the sequence stratigraphic framework, sea level changes and paleoecology of the Dhudial Section.
Alignment files for coverage benchmarks: Illumina and Nanopore sequencing datasets
<ul> <li><strong>cpara-illumina-noseq.bam</strong> and <strong>cpara-ont-noseq.bam</strong>: BAM files produced aligning the raw reads produced respectively by Illumina NextSeq and ONT Nanopore sequencing of an isolate of <em>C. parapsilosis</em> to evaluate the coverage calculations using real datasets.*</li> <li><strong>HG00258.bam</strong>: 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><strong>panel_01.bam</strong>: targeted sequencing of a Human gene panel of 16 genes.*</li> </ul> <p>* Sequences and qualities have been removed</p>
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank in Superficially described and ignored for 92 years, rediscovered and emended: Apodera angatakere (Amoebozoa: Arcellinida: Hyalospheniformes) is a new flagship testate amoeba taxon from Aotearoa (New Zealand)
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank
FIG. 8 in Foraminiferal biostratigraphy, facies and sequence stratigraphy analysis across the K-Pg Boundary in Hazara, Lesser Himalayas (Dhudial Section)
FIG. 8. — Chart showing the planktonic foraminiferal distribution and biozones of the Dhudial Section.
The impulse response of optic flow sensitive descending neurons to roll m-sequences
<p>When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensori-motor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. We therefore here used m-sequences to quantify the impulse responses of optic flow sensitive descending neurons in male <i>Eristalis tenax </i>hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5-18.0 ms. This is similar to the impulse response time-to-peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.</p>
Accession passport and sequence data
<p>Accession passport data corresponding to <em>An2-like</em> and <em>Ant1 </em>sequence data.</p>
MOREST Experiment Data with test sequence
<p>MOREST experiment data that contains the test results of MOREST vs. 3 other competing methods. The json files are the raw data generated by the tool, which contains the 500 responses corresponding to bugs information. </p> <p> </p> <p>The detailed test sequence outputs are in the "Test sequence details.zip"</p>
Data from: Pitfalls and pointers: an accessible guide to marker gene amplicon sequencing in ecological applications
<p>Next Generation Sequencing (NGS) is a powerful tool that has been rapidly adopted by many ecologists studying microbial communities. Despite the exciting demonstration of NGS technology as a tool for ecological research, cryptic pitfalls inherent to its use can obscure correct interpretation of NGS data. Here, we provide an accessible overview of a NGS process that uses marker gene amplicon sequences (MGAS) that will allow scientists, particularly community ecologists, to make appropriate methodological choices and understand limits on inference about community composition and diversity that can be drawn from MGAS data.</p> <p>We describe the MGAS pipeline, focusing specifically on cryptic sources of variation that have received less emphasis in the ecological literature, but which may substantially impact inference about microbial community diversity and composition. By simulating communities from published microbiome data, we demonstrate how these sources of variation can generate inaccurate or misleading patterns.</p> <p>We specifically highlight sample dilution without researcher awareness and lane-to-lane variability, two cryptic sources of variation arising during the MGAS pipeline. These sources of variation affect estimates of species presence and relative abundance, particularly for species with moderate to low abundances. Each of these sources of bias can lead to errors in the estimation of both absolute and relative abundance within, and turnover among, microbial communities.</p> <p>Awareness and understanding of what happens and, specifically, why it happens during MGAS generation is key to generating a strong data set and building a robust community matrix. Requesting sample dilution information from the sequencing center, including technical replicates across sequencing lanes, and understanding how sampling intensity and community taxa distribution patterns shape the measurement of community richness, evenness, and diversity are critical for drawing correct ecological inferences using MGAS data.</p>
SARS-Cov-2 illumina sequencing training course
<p>Dataset with two samples of SARS-Cov-2 sequenced with Illumina using Artic v3 amplicon enrichment protocol.</p>
Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.
<p>Datasets: <em>CommonClinvar</em> and <em>NewClinvar</em></p> <p>Authors: Emidio Capriotti and Piero Fariselli</p> <p>Article: Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.<br> </p>
Fig. 1. Collectionsitesfor 91 specimensfrom 29 locationsof Apodemusagrarius, usedin thisstudy. ThespecimennumberforeachlocationarelistedinTable 1 in Comparative Genetics Of Apodemus Agrarius (Rodentia: Mammalia) From Insular And Continental Eurasian Populations: Cytochrome B Sequence Analyses
Fig. 1. Collectionsitesfor 91 specimensfrom 29 locationsof Apodemusagrarius, usedin thisstudy. ThespecimennumberforeachlocationarelistedinTable 1, andminimum Jukes-Cantordistancesbetween 17 haplotypesfrommainlandKoreaandother 49 haplo- typesfromsevenregions (Taiwan, Jeju, northeasternChina, easternChina, centralChina, Far-easternRussia, andEurope) basedonthecytochrome b completesequencesaregiven
Synthetic dataset for the flow-shop problem with sequence-dependent setup times (FSsdsu_DS_01)
<p>Synthetic dataset containing setup and processing times for a flow-shop problem of 1.000 parts and 200 machines. The setup times are specified for each machine taking into account the previous part produced on the machine.</p>
apomixis_parallel_evolution, and Fortunella hindsii (Citrus hindsii )Genome sequencing and assembly
<p>##The assemble(genome) files</p> <p>Citrus hindsii (Mini citrus)Genome assembly</p> <p>Citrus hindsii (Mini citrus)Genome assembly gene model gff3 file</p> <p>Citrus hindsii (Mini citrus)Genome assembly function annotation</p> <p>Citrus hindsii (Mini citrus)Genome assembly TE gff3 file</p> <p>##The population dataset</p> <p>LD_pur.vcf.gz //The LD purning SNP vcfs (1.4 M sites) used in analysis</p> <p><br> log10_auxin.txt //The expression (log10) related to auxin pathway</p> <p><br> sjg.temergedref.fasta.gz //The TE insertion modify genome in popTE2 analysis</p> <p><br> SVs.vcf.gz //The SV vcfs used in the paper</p> <p><br> te-hierarchy.txt //The TE classfication in popTE2 analysis</p> <p><br> TPM_count.txt //All samples expression in TMP count</p> <p><br> unfiltered.vcf.gz //The unfiltered vcfs file (7.3 M sites, within 0.4 M indels) </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. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis</p> <p>2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis</p>
Supplementary Data for "Sequencing the Pandemic: Rapid and High-Throughput Processing and Analysis of COVID-19 Clinical Samples for 21st Century Public Health"
<p>Supplementary material for F1000 methods manuscript. Includes raw sequencing metrics for two COVID sequencing methodologies, as well as a complete cost breakdown for each methodology.</p>
Fig. 17. Phylogenetic hypothesis using nuclear gene sequences TMO-4C4 and 18S in A New Genus and Species of Pygmy Pipehorse from Taitokerau Northland, Aotearoa New Zealand, with a Redescription of Acentronura Kaup, 1853 and Idiotropiscis Whitley, 1947 (Teleostei, Syngnathidae)
Fig. 17. Phylogenetic hypothesis using nuclear gene sequences TMO-4C4 and 18S retrieved with Maximum Likelihood (ML), Maximum Parsimony (MP), and Bayesian Inference (MrBayes), representing 17 species from clade 6 from the analysis of Hamilton et al. (2017) and the new taxon. Tree rooted with the southern Australian trunk-brooder pipefish Heraldia nocturna. Nodal support at the generic level is shown in ML/MP/MrBayes order. See Data Accessibility for tree file.
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (1/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>Cneg</td> <td>RNASEQ-AVF1</td> <td>RNASEQ-AVF1_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF1_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF2</td> <td>RNASEQ-AVF2_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF2_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF3</td> <td>RNASEQ-AVF3_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF3_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF4</td> <td>RNASEQ-AVF4_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF4_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF5</td> <td>RNASEQ-AVF5_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF5_S5_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA A</td> <td>RNASEQ-AVF6</td> <td>RNASEQ-AVF6_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF6_S6_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA B</td> <td>RNASEQ-AVF7</td> <td>RNASEQ-AVF7_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF7_S7_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA C</td> <td>RNASEQ-AVF8</td> <td>RNASEQ-AVF8_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF8_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (2/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>TDP-43 siRNA D</td> <td>RNASEQ-AVF9</td> <td>RNASEQ-AVF9_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF9_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF10</td> <td>RNASEQ-AVF10_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF10_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF11</td> <td>RNASEQ-AVF11_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF11_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF12</td> <td>RNASEQ-AVF12_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF12_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF13</td> <td>RNASEQ-AVF13_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF13_S5_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF14</td> <td>RNASEQ-AVF14_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF14_S6_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos B+C</td> <td>RNASEQ-AVF15</td> <td>RNASEQ-AVF15_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF15_S7_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos A+B+C</td> <td>RNASEQ-AVF16</td> <td>RNASEQ-AVF16_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF16_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
MetaCOXI: An integrated collection of metazoan cytochrome oxidase subunit-I DNA sequences
<p><strong>MetaCOXI Sequences Taxonomy and Metadata</strong></p> <p>This collection is based on the integration of the European Nucleotide Archive (ENA, release 142) and the Barcode of Life Data Systems (BOLD: <a href="http://www.boldsystems.org/">http://www.boldsystems.org/</a>) data.</p> <p><strong>CONTENT: </strong>Currently it contains 5,608,848 entries of metazoan COXI sequences and their corresponding taxonomic classification and metadata. <em>MetaCOXI_Seqs.tar.gz</em> contains the full sequence collection in 'fasta' format. <em>MetaCOXI_Taxonomy_Metadata.tar.gz</em> contains the entries-associated taxonomy path and additional metadata</p> <p>Taxonomic path are provided for the following seven levels with their NCBI-TaxIDs: Kingdom, Phylum, Class, Order, Family, Genus, Species.</p> <p>For additional information visit: https://github.com/bachob5/MetaCOXI</p>
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.