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19,342
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19,342 results for “Sequences”
Melange (COGs and Pfams) and AntiSMASH (BGCs) annotations of the genome sequence of Lentilitoribacter sp. EG35
<p>Secondary Metabolite Encoding Biosynthetic Gene Cluster (BGC) annotation files from antiSMASH bacterial version 7.1.0 as well as Clusters of Orthologous Groups of proteins (COG) and Protein families (Pfam) annotations from the Melange pipeline (<a href="https://github.com/sandragodinhosilva/melange">https://github.com/sandragodinhosilva/melange</a>) of the genome assembly of <em>Lentilitoribacter </em>sp. strain EG35, isolated from the temperate octocoral <em>Eunicella gazella</em> sampled in the Northeast Atlantic Ocean, Portugal. </p> <p>The data correspond to the genome assembly of EG35 available under the BioProject accession number<a href="https://www.ncbi.nlm.nih.gov/bioproject/1075806"> PRJNA1135483</a>.</p> <p>To interactively view the AntiSMASH results, please download and extract the entire content of the AntiSMASH folder, and open the HTML file named "index".</p> <p><strong>This dataset is part of the following study:</strong></p> <p><strong>Tina Keller-Costa, Selene Madureira, Ana S. Fernandes, Lydia Kozma, Jorge M.S. Gonçalves, Cristina Barroso, Conçeicão Egas, & Rodrigo Costa<sup> </sup>2024. Genome sequence of the marine alphaproteobacterium <em>Lentilitoribacter</em> sp. EG35 isolated from the temperate octocoral <em>Eunicella gazella</em>. Microbiology Resource Announcements. MRA00872-24.</strong></p>
p-IgGen Dataset: Cleaned paired and unpaired antibody sequence data for machine learning applications.
<p>This data is released alongside "p-IgGen: A Paired Antibody Generative Language Model", which contains full details on the data processing and cleaning.</p> <p>p-IgGen Paper: https://www.biorxiv.org/content/10.1101/2024.08.06.606780v1 .</p> <p>OAS: https://opig.stats.ox.ac.uk/webapps/oas/</p> <p> </p>
Time series transcriptomes resolve metabolic pathways underlying crocin's anti-cancer activity | Dataset: sequencing reads (2,6,12,24 hr crocin treatments)
<p><span>Natural products like saffron show promise in treating hepatocellular carcinoma (HCC), but their mechanisms remain unclear. Here, we used time-series transcriptomics to elucidate crocin's anti-cancer mechanisms in HCC cells. We treated HepG2 cells with 1 and 2 mM crocin for 2, 6, 12, and 24 hours and analyzed transcriptomic profiles at each timepoint. The strongest transcriptional response occurred at 2 hours with 1 mM crocin, with diminishing effects at later timepoints. We observed upregulation of metabolic-, adhesion-, and endocytosis-related genes across all timepoints. Pathway analysis revealed activation of DNA damage checkpoints and senescence while proliferation pathways were suppressed. Notably, 52 genes involved in non-alcoholic fatty liver disease were downregulated at 24 hours (FDR p = 8 × 10⁻⁸), suggesting reversal of carcinogenic pathways. Strikingly, crocin consistently downregulated spliceosomal machinery genes across all timepoints while upregulating senescence and autophagy pathways. This spliceosome targeting represents a clinically relevant mechanism, as aberrant splicing drives oncogenesis in more than 90% of cancers. The transcription factor PAX5 was significantly upregulated while oncogenic ELK1 targets were downregulated. Our findings show that crocin treatment is accompanied by HCC cell senescence induction through coordinated spliceosome disruption and metabolic reprogramming, providing novel therapeutic targets for hepatocellular carcinoma.</span></p> <p><strong><span>Keywords: </span></strong><span>Hepatocellular carcinoma (HCC), crocin, transcriptomics, spliceosome, senescence, natural anti-cancer compounds</span></p>
Respiratory-triggered MRCP acquisition at 3T using a T2-weighted TSE (3D SPACE) sequence for Deep Learning-based reconstruction of MRCP
<h1>Description</h1> <p>This dataset is the sample data for MRCP_DLRecon (<a href="https://github.com/JinhoKim46/MRCP_DLRecon">GitHub</a>). <br>Place this dataset in the "Sample_data/" directory along with the "dataset.csv" file. </p> <h1>Data</h1> <p>The provided 3D MRCP data were acquired at 3T (Skyra, Siemens Healthineers AG, Erlangen) using the 3D-SPACE (3D T2w TSE) sequence for a single healthy volunteer. We provide two 2x and one 6x 3D MRCP data to ensure various training and testing scenarios. Each data in the HDF5 format contains the following structures:</p> <ul> <li>Datasets <ul> <li><strong>grappa</strong>: target data (y * x *<em> </em>slice)</li> <li><strong>kdata_raw</strong>: Raw <em>k</em>-space data (x2 or x6) (nCoil * PE * RO * slice)</li> <li><strong>kdata_fs</strong>: Fully-sampled k-space data from <strong>kdata_raw</strong> using GRAPPA (nCoil ×× PE ×× RO ×× Slice)</li> <li><strong>sm_espirit</strong>: ESPIRiT-based sensitivity maps (nCoil * y * x * slice)</li> </ul> </li> </ul> <h1>Citation</h1> <p>Please cite the following <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.70002" target="_blank" rel="noopener">paper</a> if this dataset is helpful for your research :)</p> <blockquote> <p>Kim, J., Nickel, M. and Knoll, F. (2025), Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data. NMR in Biomedicine, 38: e70002. https://doi.org/10.1002/nbm.70002 </p> </blockquote>
Source code of tables for the publication "K- and L-shell theoretical fluorescence yields for the Fe isonuclear sequence"
<p>In this work, we present K- and L- shell fluorescence yield values of the full isonuclear sequence of Fe ions, using a state-of-the-art multiconfiguration Dirac-Fock approach. These results may be of importance for spectral fitting and plasma modeling, both in laboratory and astrophysical studies, where Fe is an important benchmark element. The K-shell fluorescence yields were found to be very similar up to the removal of 14 electrons.</p>
Subsampled 20x of sequence reads from study PRJNA315192
<p>subsampled reads from study PRJNA315192 for study purposes.</p> <p>Nanopore version</p> <p> </p> <p>Original works:</p> <p>Greig, D.R., Do Nascimento, V., Gally, D.L. <em>et al.</em> Re-analysis of an outbreak of Shiga toxin-producing <em>Escherichia coli</em> O157:H7 associated with raw drinking milk using Nanopore sequencing. <em>Sci Rep</em> <strong>14</strong>, 5821 (2024). https://doi.org/10.1038/s41598-024-54662-0</p>
Sanger sequencing traces experimental evolution rVSV-SADS
<p>A recombinant vesicular stomatitis virus (VSV) expressing the spike glycoprotein of swine accute diarrhea syndrome virus (SADS) (rVSV-SADS) was serially passaged in three human cell lines (Huh-7, H23, OVCAR-8). The wild-type (WT), initial virus (P0) and final viruses at passage 10 (P10) were sequenced using Sanger sequencing. The seuquence traces are attached.</p>
NA12878, 22RV1 and NB4 sequence dataset
<p>Adaptive Nanopore PromethION multiplexed sample. </p> <p>NA12878 is Barcode 05.</p> <p>NB4 is Barcode 06.</p> <p>22Rv1 is Barcode 07.</p> <p>The FASTQ is split by whether the read was sequenced or actively rejected by readfish.</p> <p>Corresponds to the dataset used in the readfish dataset.</p>
NanoBaseLib: A Multi-Task Benchmark Dataset for Nanopore Sequencing
<p>NanoBaseLib is a multi-task benchmark dataset for Nanopore Sequencing. We compile and preprocess publicly available datasets using a unified pipeline to ensure consistency and quality across all tasks. The dataset is benchmarked for four key Nanopore sequencing tasks: base calling, polyA detection, segmentation and event alignment, and RNA modification detection. NanoBaseLib is available at <a href="https://nanobaselib.github.io/">https://nanobaselib.github.io</a>. </p>
Supplementary materials for "High-Throughput Discovery of Substrate Peptide Sequences for E3 Ubiquitin Ligases Using a cDNA Display Method."
<p>The next-generation sequencing (NGS) data of the 5th rounds' samples for LX9 library and p53deg library. The csv files contain DNA sequences read out, amino acid sequences and their read counts in descending order. </p>
Additional files for Horvath et al., 2024. Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning.
<p>Additional data for Horvath et al., 2024 (source code freeze, models, data, supplementary figures, tables and files(.</p>
Random samples of SARS-CoV-2 sequences for Germany
<p>Mutation profiles and meta data for SARS-CoV-2 lineages in Germany submitted via DESH to the Robert Koch Institute (<a href="https://github.com/robert-koch-institut/SARS-CoV-2-Sequenzdaten_aus_Deutschland">https://github.com/robert-koch-institut/SARS-CoV-2-Sequenzdaten_aus_Deutschland</a>), containing a subset from https://doi.org/10.5281/zenodo.12806236.<br>For this CSV file, the raw data (fasta) was processed with covSonar (https://github.com/rki-mf1/covsonar) and the resulting database queried for random samples (RKI_STICHPROBE, DESH_STICHPROBE) and with dates ranging from 2021-01-01 until 2024-07-24.</p>
IsoQuant graphs from PacBio and ONT Mouse sequencing data
<p>Graph files and simulated ONT BAM files.</p>
ABI sequencing files for mouse Prex2 mutant sequence verification
<p>File HWZD44_3A_1-P0058.ab1 is an ABI sanger sequencing trace showing the presece of the mutation E22A in exon 1 of mouse Prex2,</p> <p>File 59_2h-P0060.ab1 is an ABI sanger sequencing trace showing the presece of the mutation N204A in exon 6 of mouse Prex2.</p>
Raw data for whole plasmid and whole genome sequencing
<p>Original data for plasmid and genomic DNA sequencing in the paper: Tailoring Microbial Fitness Through Computational Steering and CRISPRi-Driven Robustness Regulation</p>
Data from: Parallel mechanisms signal a hierarchy of sequence structure violations in the auditory cortex
<p>The brain predicts regularities in sensory inputs at multiple complexity levels, with neuronal mechanisms that remain elusive. Here, we monitored auditory cortex activity during the local-global paradigm, a protocol nesting different regularity levels in sound sequences. We observed that mice encode local predictions based on stimulus occurrence and stimulus transition probabilities, because auditory responses are boosted upon prediction violation. This boosting was due to both short-term adaptation and an adaptation-independent surprise mechanism resisting anesthesia. In parallel, and only in wakefulness, VIP interneurons responded to the omission of the locally expected sound repeat at sequence ending, thus providing a chunking signal potentially useful for establishing global sequence structure. When this global structure was violated, by either shortening the sequence or ending it with a locally expected but globally unexpected sound transition, activity slightly increased in VIP and PV neurons respectively. Hence, distinct cellular mechanisms predict different regularity levels in sound sequences.</p>
Supplementary dataset to publication: Whole-genome sequencing of Streptococcus uberis isolated from cows with mastitis in Thuringia
<p><span><strong>Introduction</strong>.</span> <em><span>Streptococcus uberis</span></em> is a common cause of mastitis in cattle, leading to significant economic losses. The widespread use of antimicrobials has contributed to the emergence of resistance, which poses a severe challenge in controlling <em><span>S. uberis</span></em> infection.</p> <p><span><strong>Aim</strong>.</span> The objective of this study was to gain insights into the antimicrobial resistance (AMR) and epidemiological typing of <em><span>S. uberis</span></em> isolated from milk collected from bovine mastitis on dairy farms in Thuringia.</p> <p><span><strong>Methodology</strong>.</span> In this study, 84 <em><span>S. uberis</span></em> isolates were obtained from cattle with clinical mastitis in Thuringia, their phenotypic and genotypic AMR were analyzed and their phylogenetic relationship was explored using whole-genome sequencing.</p> <p><span><strong>Results</strong>.</span> Genetically heterogeneous strains were found on the farms, but clusters of highly similar strains also circulated within the same farms. All isolates were sensitive to ampicillin, penicillin, ceftiofur, and vancomycin. However, 42.9%, 42.9%, 22.6%, 19.0%, and 13.0% were resistant to tetracycline, doxycycline, clindamycin, pirlimycin, and erythromycin, respectively. Thirty-nine strains were phenotypically resistant to two or more tested antibiotics. We identified a plasmid associated with macrolide and lincosamide resistance in 12% of the strains.</p> <p><span><strong>Conclusion</strong>.</span> The emergence of <em><span>S. uberis</span></em> strains resistant to multiple antibiotics highlights the importance of <em><span>S. uberis</span></em> surveillance and the prudent use of antimicrobials.</p>
Data for 'NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning'
<p>The uploaded files include two archives for <a href="https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00300" target="_blank" rel="noopener">NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning</a>. The '<em>mgf_data</em>' archive contains all MGF files used in the study, while the '<em>sample_data</em>' archive includes sequencing data, clustering data generated using <code>MSCluster</code>, and XCorr calculation data computed with <code>CometX</code>, all of which were used in the research.</p>
List of SSCVs Identification with juncmut from Sequence Read Archive
<p>List of Splice-Site Creating Variants (SSCVs) via <a href="https://github.com/ncc-gap/juncmut">juncmut</a> applied to 322,072 transcriptome sequence data from Sequence Read Archive.</p>
Table 3 in First insights into past biodiversity of giraffes based on mitochondrial sequences from museum specimens
<p><b>Table 3.</b> Minimum and maximum pairwise distances (in %), as well as mean distance (between brackets), calculated using the mtDNA-91T dataset both within and between haplogroups (Fig. 3). <b>Boldface</b> = maximal intrapopulational variation.</p><table><tbody><tr><th>Taxa</th><th><b>I</b>.</th><th><b>II</b>.</th><th><b>III</b>.</th><th><b>IV</b>.</th><th><b>V</b>.</th><th><b>VI</b>.</th><th><b>VII</b>.</th><th><b>VIII</b>.</th><th><b>IX</b>.</th><th><b>X</b>.</th><th><b>XI</b>.</th></tr></tbody><tbody><tr><th><b>I</b>. Nubia</th><td><b>0</b>. <b>17</b></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th><b>II</b>. Senegal</th><td>1.38 – 1.5 (1.44)</td><td><b>0</b></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th><b>III</b>. Niger</th><td>1.09 – 1.47 (1.24)</td><td>1.74 – 1.93 (1.82)</td><td><b>0</b>. <b>26</b></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th><b>IV</b>. Kordofan I</th><td>0.75 – 1.14 (0.96)</td><td>1.67 – 1.78 (1.7)</td><td>1.28 – 1.67 (1.41)</td><td><b>0</b>. <b>52</b></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th><b>V</b>. Kordofan II</th><td>1.22 – 1.41 (1.32)</td><td>1.99</td><td>1.67 – 1.86 (1.75)</td><td>0.9 – 1.15 (0.98)</td><td><b>0</b></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th><b>VI</b>. Rothschild</th><td>1.15 – 1.73 (1.35)</td><td>1.49 – 2 (1.68)</td><td>1.21 – 1.67 (1.35)</td><td>1.21 – 1.67 (1.48)</td><td>1.48 – 1.67 (1.58)</td><td><b>0</b>. <b>58</b></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th><b>VII</b>. Reticulated I</th><td>1.54 – 1.99 (1.69)</td><td>1.55 – 1.87 (1.75)</td><td>1.48 – 2.19 (1.74)</td><td>1.48 – 2.12 (1.68)</td><td>1.8 – 2.06 (1.89)</td><td>1.35 – 1.99 (1.60)</td><td><b>1</b>. <b>16</b></td><td></td><td></td><td></td><td></td></tr><tr><th><b>VIII</b>. Masai I</th><td>3.75 – 3.87 (3.82)</td><td>4.13 – 4.13 (4.13)</td><td>3.99 – 4.13 (4.05)</td><td>3.68 – 3.93 (3.80)</td><td>4.12 – 4.12 (4.12)</td><td>3.81 – 4.26 (4.01)</td><td>3.81 – 4.07 (4.00)</td><td><b>0</b></td><td></td><td></td><td></td></tr><tr><th><b>IX</b>. Masai II</th><td>3.73 – 3.99 (3.85)</td><td>4.08 – 4.26 (4.17)</td><td>3.92 – 4.25 (4.04)</td><td>3.50 – 3.91 (3.74)</td><td>4.05 – 4.24 (4.11)</td><td>3.85 – 4.37 (4.03)</td><td>3.86 – 4.31 (4.03)</td><td>0.31 – 0.94 (076)</td><td><b>0</b>. <b>98</b></td><td></td><td></td></tr><tr><th><b>X</b>. Southeast Africa</th><td>4.12 – 4.31 (4.21)</td><td>4.26 – 4.45 (4.34)</td><td>4.31 – 4.69 (4.46)</td><td>4.18 – 4.63 (4.32)</td><td>4.44 – 4.63 (4.51)</td><td>4.18 – 4.63 (4.35)</td><td>4.12 – 4.57 (4.37)</td><td>1.35 – 1.54 (1.43)</td><td>1.29 – 1.56 (1.40)</td><td><b>0</b>. <b>32</b></td><td></td></tr><tr><th><b>XI</b>. Southwestern</th><td>3.13 – 3.73 (3.52)</td><td>3.49 – 4.12 (3.95)</td><td>3.53 – 4.11 (3.80)</td><td>3.07 – 3.86 (3.58)</td><td>3.66 – 4.11 (3.91)</td><td>3.54 – 4.31 (3.94)</td><td>3.73 – 4.24 (3.95)</td><td>2.64 – 2.9 (2.78)</td><td>2.48 – 3.09 (2.80)</td><td>3.02 – 3.41 (3.16)</td><td><b>0</b>. <b>65</b></td></tr></tbody></table>
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.