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19,342 results for “Sequences”
DNA sequences of transgenes detected via environmental DNA (raw ABI files, processed FASTA files, and reference alignments)
We demonstrate that simple, non-invasive environmental DNA (eDNA) methods can detect transgenes of genetically modified (GM) animals from terrestrial and aquatic sources in invertebrate and vertebrate systems. We detected transgenic fragments between 82-234 bp through targeted PCR amplification of environmental DNA extracted from food media of GM fruit flies (<i>Drosophila melanogaster</i>), feces, urine, and saliva of GM laboratory mice (<i>Mus musculus</i>), and aquarium water of GM tetra fish (<i>Gymnocorymbus ternetzi</i>). With rapidly growing accessibility of genome-editing technologies such as CRISPR, the prevalence and diversity of GM animals will increase dramatically. GM animals have already been released into the wild with more releases planned in the future. eDNA methods have the potential to address the critical need for sensitive, accurate, and cost-effective detection and monitoring of GM animals and their transgenes in nature.
Low coverage Cnidaria sequencing GENEWIZ_2021_08_20
<p>These samples were submitted for sequencing but there wasn't adequate DNA available for full coverage:</p> <p>Samples are named by taxid as follows: (two files each, forward and reverse).</p> <p>168719 - Carybdea xaymacana</p> <p>2835545 - Manania auricula</p> <p>645345 - Atolla wyvillei</p> <p>880221 - Periphylla periphylla</p>
Stress Drop Catalog for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence" - Kemna et al. 2021 JGR - Solid Earth
<p>Catalog with stress drop estimates for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence"</p> <p>Kemna et al., 2021, JGR: Solid-Earth, https://doi.org/10.1029/2021JB022566.</p> <p>Description of columns:</p> <p><strong>Earthquake information</strong></p> <ul> <li>ID - INGV Earthquake ID</li> <li>Latitude - Latitude in Degrees</li> <li>Longitude - Longitude in Degrees</li> <li>Depth - Depth in km</li> <li>Magnitude_INGV - Magnitude reported by INGV</li> <li>Origin_UTC - UTC Origin Time in ISO Format</li> <li>Catalog - Catalog source of specific event. See section 2.1 for details</li> <li>Profile_distance_norcia - Distance of earthquake from Norcia Mainshock location projected onto a NW-SE trending line</li> <li>Dayafter_20160101 - Day after start of catalog in float</li> </ul> <p><strong>Single spectra fitting estimates</strong></p> <ul> <li>mw_s_mean - Moment Magnitude averaged over station estimates</li> <li>mw_s_err - 95% error (from delete-one jackknife-mean)</li> <li>m0_s_mean - Seismic Moment in Nm averaged over station estimates</li> <li>m0_s_err - 95 % error(from delete-one jackknife-mean)</li> <li>fc_s_sssa_mean - Corner frequency estimate averaged over station estimates</li> <li>fc_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_sssa_mean - Stress drop estimate averaged over station estimates</li> <li>strdrop_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>sample_size_s_sssa - Number of stations with an estimate</li> <li>azimuthal_gap_s_sssa - Maximum azimuthal gap</li> <li>alpha_vel - P-wave velocity in m/s at Hypocenter</li> <li>beta_vel - S-wave velocity in m/s at Hypocenter</li> </ul> <p><strong>Cluster-event method estimates</strong></p> <ul> <li>fc_s_cema_mean - Corner frequency estimated averaged over clusters</li> <li>fc_s_cema_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_cema_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_cema_err - 95 % error</li> </ul> <p><strong>Spectral Ratio fitting estimates</strong></p> <ul> <li>fc1_s_rsta_mean - Target event corner frequency estimate using automatic source spectra fitting averaged over eGfs</li> <li>fc1_s_rsta_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_rsta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_rsta_err - 95 % error</li> <li>egf_number_rsta_s - Number of eGfs for each target event</li> <li>fc1_s_rrta_mean - Target event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc1_strdrop_s_rrta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>fc2_s_rrea_mean - eGf event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc2_strdrop_s_rrea_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> </ul> <p><strong>Magnitude-normalized stress drop</strong></p> <ul> <li>prio_strdrop_s - Which type of estimate is used</li> <li>magbin_s - Magnitude bin to which event is associated</li> <li>prio_strdrop_s_magbinmean - Stress drop mean for specific magnitude bin</li> <li>prio_strdrop_s_magbinstderr - 95 % error(from delete-one jackknife-mean)</li> <li>prio_strdrop_s_magnitude-normalized - Magnitude-normalized stress drop estimate</li> </ul>
Assessing reproduciblity of Inherited Variants Detected with Short-read Whole Genome Sequencing
<p>This dataset is part of the following study:</p> <p><a href="https://www.fda.gov/science-research/bioinformatics-tools/microarraysequencing-quality-control-maqcseqc">https://www.fda.gov/science-research/bioinformatics-tools/microarraysequencing-quality-control-maqcseqc</a></p> <p>The raw sequencing data can be downloaded from SRA:</p> <p><a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA723125">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA723125</a></p> <p>and NODE:</p> <p><a href="https://www.biosino.org/node/project/detail/OEP0018966">https://www.biosino.org/node/project/detail/OEP0018966</a></p>
Improving the efficiency of single cell genome sequencing based on overlapping pooling strategy
Single cell genome sequencing has become a useful tool in medicine and biology studies. However, an independent library is required for each cell in single cell genome sequencing, so that the cost grows in step with the number of cells. In this study, we report a study on efficient single-cell copy number variation (CNV) analysis based on overlapping pooling strategy together with branch and bound (B&B) algorithm. Single cells are overlapped pooled before sequencing, and later are assorted into specific types by estimating their CNV patterns by B&B algorithm. Instead of constructing libraries for each cell, a library is required only for each pool. As long as the number of pools is smaller than the cells, fewer libraries are needed, and a lower cost is spent. Through computer simulations, we overlapping pooled 80 cells into 40 and 27 pools and classified them into cell types based on CNV pattern. The results showed that 84% cells in 40 pools and 76.5% cells in 27 pools were correctly classified on average, while only half or one-third of the sequencing libraries are required. Combining with traditional approaches, our method is expected to significantly improve the efficiency of single cell genome sequencing.
Data for "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer"
<p>These are the data associated with the paper, "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer" (DOI 10.1039/D1SM01012C). Each of the directories contains subdirectories with `GSD` files dumped from HOOMD. Each subdirectory roughly corresponds to one or two of the figures in the paper.</p>
Simulated wastewater sequencing data for benchmarking SARS-CoV-2 variant abundance estimation
<p>To evaluate the accuracy of variant abundance predictions from wastewater sequencing, we built a collection of benchmarking datasets that resemble real wastewater samples. For each variant (B.1.1.7, B.1.351, B.1.427, B.1.429, P.1) we created a series of 33 benchmarks by simulating sequencing reads from a variant genome, as well as a collection of background (non-variant of concern/interest) sequences, such that the variant abundance ranges from 0.05% to 100%. Analogously, we created a second series of benchmarks, simulating reads only from the Spike gene of each SARS-CoV-2 genome. We refer to the first set of benchmarks as "whole genome" (WG) and to the second set of benchmarks as "S-only". We repeated these simulations at different sequencing depths: 100x and 1000x coverage for the whole genome benchmarks, and 100x, 1000x, and 10,000x coverage for the S-only benchmarks.</p>
Fig. 5 in Copelatus sibelaemontis sp. nov. (Coleoptera: Dytiscidae) from the Moluccas with generic assignment based on morphology and DNA sequence data
Fig. 5. Distribution of Copelatus sibelaemontis sp. nov.
Fig. 23 in Phylogenetic Studies On Didelphid Marsupials Ii. Nonmolecular Data And New Irbp Sequences: Separate And Combined Analyses Of Didelphine Relationships With Denser Taxon Sampling
Fig. 23. Skull of Tlacuatzin canescens, a composite drawing based on USNM 125659 and 511261.
List of known SNP positions (based on SNP chip data) for base quality score recalibration of alignments for whole-genome resequencing and whole-genome bisulfite sequencing data from great tits (Parus major)
<p>The profiling of epigenetic marks like DNA methylation has become a central aspect of studies in evolution and ecology. Bisulfite sequencing is commonly used for assessing genome-wide DNA methylation at single nucleotide resolution but these data can also provide information on genetic variants like single nucleotide polymorphisms (SNPs). However, bisulfite conversion causes unmethylated cytosines to appear as thymines, complicating the alignment and subsequent SNP calling. Several tools have been developed to overcome this challenge, but there is no independent evaluation of such tools for non-model species, which often lack genomic references. Here, we used whole-genome bisulfite sequencing (WGBS) data from four female great tits (<i>Parus major</i>) to evaluate the performance of seven tools for SNP calling from bisulfite sequencing data. We used SNPs from whole-genome resequencing data of the same samples as baseline SNPs to assess common performance metrics like sensitivity, precision, and the number of true positive, false positive, and false negative SNPs for the full range of variant and genotype quality values. We found clear differences between the tools in either optimizing precision (Bis-SNP), sensitivity (biscuit), or a compromise between both (all other tools). Overall, the choice of SNP caller strongly depends on which performance parameter should be maximized and whether ascertainment bias should be minimized to optimize downstream analysis, highlighting the need for studies that assess such differences.</p>
Fig. 4 in Some Unusual Small-Subunit Ribosomal RNA Sequences of Metazoans
Fig. 4. Variable region (V7) of the 18S rRNA locus of 17 species of centipedes.
FIGURE 2 in Voucher Specimens For Dna Sequences Of Phytoseiid Mites (Acari: Mesostigmata)
FIGURE 2: DNA bands of the expected sizes were amplified for the mitochondrial Cytb fragment from A – Neoseiulus idaeus, from B – from Typhlodromus (Typhlodromus) exhilaratus. A – Lane M shows the molecular weight marker, C indicates the water control, C+ indicates the positive control, lanes 1-6 show the PCR products of a single N. idaeus specimen preliminarily treated with lactic acid. Image B: Lane M shows the molecular weight marker, Lanes 1-5 show the PCR products of a single T. (T.) exhilaratus specimen conserved in 100 % alcohol.
FIGURE 1 in Voucher Specimens For Dna Sequences Of Phytoseiid Mites (Acari: Mesostigmata)
FIGURE 1: Slide-mounted females of Typhlodromus (Typhlodromus) exhilaratus and Neoseiulus idaeus after genomic DNA extraction using a Qiagen DNeasy kit. A – dorsal shield of T. (T.) exhilaratus; B – ventral shields of T. (T.) exhilaratus; C – dorsal shield of Neoseiulus idaeus; D – ventral shields of Neoseiulus idaeus.
Measuring phylogenetic information of incomplete sequence data
<p>Widely used approaches for extracting phylogenetic information from aligned sets of molecular sequences rely upon probabilistic models of nucleotide substitution or amino-acid replacement. The phylogenetic information that can be extracted depends on the number of columns in the sequence alignment and will be decreased when the alignment contains gaps due to insertion or deletion events. Motivated by the measurement of information loss, we suggest assessment of the Effective Sequence Length (ESL) of an aligned data set. The ESL can differ from the actual number of columns in a sequence alignment because of the presence of alignment gaps. Furthermore, the estimation of phylogenetic information is affected by model misspecification. Inevitably, the actual process of molecular evolution differs from the probabilistic models employed to describe this process. This disparity means the amount of phylogenetic information in an actual sequence alignment will differ from the amount in a simulated data set, which motivated us to develop a new test for model adequacy. Via theory and empirical data analysis, we show how to disentangle the effects of gaps and model misspecification. By comparing the Fisher information of actual and simulated sequences, we identify which alignment sites and tree branches are most affected by gaps and model misspecification.</p>
FIGURE 6 in Holothyrids And Ticks: New Insights From Larval Morphology And Dna Sequencing, With The Description Of A New Species Of Diplothyrus (Parasitiformes: Neothyridae)
FIGURE 6: Diplothyrus lecorrei n. sp. Adult, idiosoma. (a) – male, ventral view; (b) – female, ventral view; (c) – male, Thon's organ; (d) – female, Thon's organ, detail view. Scale bars for a, b = 500 µm; scale bars for c, d = 200 µm.
FIGURE 5 in Holothyrids And Ticks: New Insights From Larval Morphology And Dna Sequencing, With The Description Of A New Species Of Diplothyrus (Parasitiformes: Neothyridae)
FIGURE 5: Diplothyrus lecorrei n. sp. Female. (a) – chelicera (details of whole chelicera and membranous outgrowth); (b) – palp, anterolateral (left) and posterolateral (right) views (setae drawn in dashed lines not consistently present); (c) – subcapitulum; (d) – genital area (arrow indicates presumed latigynal shield), open circles: setal bases, filled grey circles: pores; (e) – pretarsus II. Scale bars = 200 µm.
FIGURE 3 in Holothyrids And Ticks: New Insights From Larval Morphology And Dna Sequencing, With The Description Of A New Species Of Diplothyrus (Parasitiformes: Neothyridae)
FIGURE 3: Diplothyrus lecorrei n. sp. Larva, idiosoma, reconstructed. (a) – dorsal view; (b) – ventral view. Scale bar = 100 µm.
FIGURE 1 in Holothyrids And Ticks: New Insights From Larval Morphology And Dna Sequencing, With The Description Of A New Species Of Diplothyrus (Parasitiformes: Neothyridae)
FIGURE 1: Previous hypotheses of subordinal relationships in the Parasitiformes. (a) – Baker and Wharton (1952); (b) – Norton et al. (1993); (c) – Lehtinen (1991); (d) – Murrell et al. (2005)
FIGURE 2 in Holothyrids And Ticks: New Insights From Larval Morphology And Dna Sequencing, With The Description Of A New Species Of Diplothyrus (Parasitiformes: Neothyridae)
FIGURE 2: Diplothyrus lecorrei n. sp. Larva, gnathosoma. (a) – chelicera; (b) – palp, anterolateral view; (c) – palp tarsus, detail, anterolateral view; (d) – subcapitulum. Scale for a, b, d identical, scale bar = 100 µm; scale bar for c = 50 µm.
FIGURE 4 in Holothyrids And Ticks: New Insights From Larval Morphology And Dna Sequencing, With The Description Of A New Species Of Diplothyrus (Parasitiformes: Neothyridae)
FIGURE 4: Diplothyrus lecorrei n. sp. Larva, legs. (a) – leg I; (b) – detail tarsus I; (c) – leg II; (d) – leg III. Scale bar for a, c, d = 100 µm.
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.