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19,342 results for “sequence”
Dorsal striatum coding for the timely execution of action sequences
<p>The automatic initiation of actions can be highly functional. But occasionally these actions cannot be withheld and are released at inappropriate times, impulsively. Striatal activity has been shown to participate in the timing of action sequence initiation and it has been linked to impulsivity. Using a self-initiated task, we trained adult male rats to withhold a rewarded action sequence until a waiting time interval has elapsed. By analyzing neuronal activity we show that the striatal response preceding the initiation of the learned sequence is strongly modulated by the time subjects wait before eliciting the sequence. Interestingly, the modulation is steeper in adolescent rats, which show a strong prevalence of impulsive responses compared to adults. We hypothesize this anticipatory striatal activity reflects the animals' subjective reward expectation, based on the elapsed waiting time, while the steeper waiting modulation in adolescence reflects age-related differences in temporal discounting, internal urgency states, or explore-exploit balance. </p>
Genome-wide mapping of individual replication fork velocities using nanopore sequencing
<p>These data are related to the <strong>"Genome-wide mapping of individual replication fork velocities using nanopore sequencing" </strong>manuscript by Theulot, Lacroix et al. (https://doi.org/10.1038/s41467-022-31012-0) and to the GitHub repository NanoForkSpeed (https://github.com/LacroixLaurent/NanoForkSpeed).</p> <p>BT1_run4.tar.gz contains the nanopore reads from a typical experiment as fast5 files.</p> <p>BT1_run4_mega.zip contains the result of the BrdU basecalling as described on the GitHub page in a modified bam file format as described on the GitHub page NanoForkSpeed/BrdU_Basecalling..</p> <p>BT1_run4_Megalodon_00_smdata.rds contains the result of the parsing function for the modified bam file as described on the GitHub page NanoForkSpeed/BrdU_Basecalling.</p> <p>BT1_run4_Megalodon_00_NFS_data.rds contains the result of the fork detection procedure on the BT1_run4_Megalodon_00_smdata.rds file as described on the GitHub page NanoForkSpeed/Forks_Detection.</p> <p>BT1_run4_merged_NFS_data.rds contains the result of the NFS_merge function as described on the GitHub page NanoForkSpeed/Forks_Detection.</p> <p> </p>
Genome-wide sequence data show no evidence of hybridization and introgression among pollinator wasps associated with a community of Panamanian strangler figs
<p>The specificity of pollinator host choice influences opportunities for reproductive isolation in their host plants. Similarly, host plants can influence opportunities for reproductive isolation in their pollinators. For example, in the fig and fig wasp mutualism, offspring of fig pollinator wasps mate inside the inflorescence that the mothers pollinate. Although often host specific, multiple fig pollinator species are sometimes associated with the same fig species, potentially enabling hybridization between wasp species. Here we study the 19 pollinator species (<em>Pegoscapus</em> spp.) associated with an entire community of 16 Panamanian strangler fig species (<em>Ficus</em> subgenus <em>Urostigma</em>, section <em>Americanae</em>) to determine whether the previously documented history of pollinator host switching and current host sharing predicts genetic admixture among the pollinator species, as has been observed in their host figs. Specifically, we use genome-wide ultraconserved element (UCE) loci to estimate phylogenetic relationships and test for hybridization and introgression among the pollinator species. In all cases, we recover well-delimited pollinator species that contain high interspecific divergence. Even among pairs of pollinator species that currently reproduce within syconia of shared host fig species, we found no evidence of hybridization or introgression. This is in contrast to their host figs, where hybridization and introgression have been detected within this community, and more generally, within figs worldwide. Consistent with general patterns recovered among other obligate pollination mutualisms (<em>e.g.</em>, yucca moths and yuccas), our results suggest that while hybridization and introgression are processes operating within the host plants, these processes are relatively unimportant within their associated insect pollinators.<br> </p>
Data from: Restriction site-associated DNA sequencing reveals local adaptation despite high levels of gene flow in Sardinella lemuru (Bleeker, 1853) along the northern coast of Mindanao, Philippines
<p>Stock identification and delineation are important in the management and conservation of marine resources. These were highlighted as priority research areas for Bali sardinella (<em>Sardinella lemuru</em>) which is among the most commercially important fishery resources in the Philippines. Previous studies have already assessed the stocks of <em>S. lemuru</em> between Northern Mindanao Region (NMR) and Northern Zamboanga Peninsula (NZP), yielding conflicting results. Phenotypic variation suggests distinct stocks between the two regions, while mitochondrial DNA did not detect evidence of genetic differentiation for this high gene flow species. This paper tested the hypothesis of regional structuring using genome-wide single nucleotide polymorphisms (SNPs) acquired through restriction-site associated DNA sequencing (RADseq). We examined patterns of population genomic structure using a full panel of 3,573 loci, which was then partitioned into a neutral panel of 3,348 loci and an outlier panel of 31 loci. Similar inferences were obtained from the full and neutral panels, which were contrary to the inferences from the outlier panel. While the full and neutral panels suggested a panmictic population (global F<sub>ST</sub> ~ 0, p > 0.05), the outlier panel revealed genetic differentiation between the two regions (global F<sub>ST</sub> = 0.161, p = 0.001; F<sub>CT</sub> = 0.263, p < 0.05). This indicated that while gene flow is apparent, selective forces due to environmental heterogeneity between the two regions play a role in maintaining adaptive variation. Annotation of the outlier loci returned five genes that were mostly involved in organismal development. Meanwhile, three unannotated loci had allele frequencies that correlated with sea surface temperature. Overall, our results provided support for local adaptation despite high levels of gene flow in <em>S. lemuru</em>. Management therefore should not only focus on demographic parameters (e.g., stock size, catch volume), but also consider the preservation of adaptive variation.</p>
Raw gene sequences
<p>Raw sequences used for the phylogenetic analysis </p>
Aligned gene sequences
<p>Aligned gene sequences used for the phylogenetic analysis</p>
Trimmed gene sequences
<p>Trimmed gene sequences used for phylogenetic analysis</p>
Integrated Data of Single cell RNA sequencing for Human Pancreatic Adenocarcinoma
<p>These data are collected and integrated from five available deposit data and one original data of single cell RNA sequencing from human pancreatic adenocarcinoma. Further analyses data for bulk transcriptomics (such as TCGA )using scRNAseq data and re-clustering for ductal epithelial cells and fibroblasts are also stored in step by step. Moreover, all R code is uploaded.</p>
PredictIO Sequencing Data
<p># Data for our paper titled "Leveraging Big Data of Immune Checkpoint Blockade Response Identifies Novel Potential Targets".</p> <p>------------------------------------------------<br> ------------------------------------------------</p> <p><br> *Yacine Bareche, Deirdre Kelly, Farnoosh Abbas-Aghababazadeh, Minoru Nakano, Parinaz Nasr Esfahani, Denis Tkachuk, Hassan Mohammad, Robert Samstein, Chung-Han Lee, Luc G. T. Morris, Philippe L. Bedard, Benjamin Haibe-Kains & John Stagg*</p> <p>## Summary<br> Several genomics and gene expression signatures have been proposed as predictive biomarkers of clinical response to immune checkpoint blockade (ICB), with questionable reproducibility. We here report a large-scale comparative analysis of candidate biomarkers of ICB responses in a pan-cancer meta-analysis of over 3,500 ICB-treated patients representing 12 different tumor types. A web-application (predictIO.ca) was developed to allow researchers to further interrogate this data compendium. We tested the hypothesis that a de novo pan-cancer gene expression analysis would bring forth critical ICB resistance pathways and novel therapeutic targets. At the genomic level, we confirmed that non-synonymous tumour mutational burden (nsTMB) was significantly associated with ICB responses across tumor types, with the exception of kidney cancer. At the transcriptional level, 21 out of 39 published gene expression signatures were significantly associated with pan-cancer ICB responses. Strikingly, the predictive value of a de novo gene expression signature (referred to as PredictIO_100) composed of the top 100 genes most significantly associated with ICB responses at pan-cancer level was superior to nsTMB and other gene expression biomarkers. Within PredictIO_100, two genes, F2RL1 (encoding protease-activated receptor-2) and RBFOX2 (encoding RNA binding motif protein 9), were concomitantly associated with worse ICB clinical outcomes, T cell dysfunction in ICB-naive patients and resistance to dual PD-1/CTLA-4 blockade in preclinical mouse cancer models. Taken together, our study underlined the relative impact of candidate biomarkers of ICB responses in a large pan-cancer cohort, demonstrated the potential of de novo pan-cancer gene expression signatures and identified F2RL1, previously involved in tumor immune regulation, and RBFOX2, a critical regulator of epithelial-to-mesenchymal transition, as potential therapeutic targets to overcome ICB resistance.</p> <p>------------------------------------------------<br> ------------------------------------------------</p> <p>## mouseModel_Zemek<br> Expression data of the mouse model study from Zemek et al. (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117358)</p> <p>## process_data<br> Expression and SNV data of the discovery cohort</p> <p>## validation_cohort<br> Expression and SNV data of the validation cohort</p>
MetaCOXI: An integrated collection of metazoan cytochrome oxidase subunit-I DNA sequences
<p><strong>MetaCOXI Sequences in fasta format</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: http://www.boldsystems.org/) data.</p> <p><strong>CONTENT: </strong>Currently it contains 5,608,848 entries of metazoan COXI sequences and their corresponding taxonomic classification and metadata. MetaCOXI_Seqs_1.tar.gz contains the full sequence collection in 'fasta' format. MetaCOXI_Taxonomy_Metadata.tar.gz 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>
A volumetric model of rabbit heart and torso including ECG data and ventricular activation sequence
<p>Data generated and analyzed of our work titled "A computational model of rabbit geometry and ECG: Optimizing ventricular activation sequence and APD distribution". Please see the respective publication for more context.</p> <p> </p> <ul> <li>BSPM_filtered.dat <ul> <li>Contains the filtered ECG Data</li> </ul> </li> <li>BSPM_original.bdf <ul> <li>Contains the originally recorded signal.<br> Information on the file format itself can be found here: <a href="https://www.biosemi.com/faq/file_format.htm">https://www.biosemi.com/faq/file_format.htm</a><br> Links to various toolboxes to open the file can be found here: <a href="https://www.biosemi.com/download.htm">https://www.biosemi.com/download.htm</a></li> </ul> </li> <li>CT_DataDCM.zip <ul> <li>Contains the recorded CT images of heart and torso in DCM file format</li> </ul> </li> <li>ECG_NodeIndices.txt <ul> <li>Contains the the node IDs of the torso mesh corresponding to the electrode positions of the ECG Vest</li> </ul> </li> <li>Endocardial_Surface_Papillary.stl <ul> <li>Segmented endocardial surface including papillary muscles</li> </ul> </li> <li>Mat_LeadField.dat <ul> <li>Contains the calculated lead field matrix to be used in combination with the provided Mesh_Ven.vtu. Make sure to keep the node order</li> <li> <pre><code class="language-python"># Python example of usage # Define a read_vm_vec function which reads your calculated data beforehand import numpy as np t_begin = 0 t_end = 400 LF_mat = np.loadtxt('Mat_LeadField.dat') times = np.linspace(t_begin, t_end, t_end-t_begin) result = np.zeros((len(times), 31)) for i,t in enumerate(times): vm_vec = read_vm_vec(t) result[i, :] = LF_mat.dot(vm_vec)[0:31] result = np.insert(result, 0, times, axis=1) np.savetxt('BSPM.dat', result)</code></pre> <p> </p> </li> </ul> </li> <li>Mesh_PurkinjeTree.vtp <ul> <li>The resulting optimized Purkinje Node tree. We recommend using <a href="https://www.paraview.org/">ParaView</a> for visualization</li> </ul> </li> <li>Mesh_StimPoints.vtp <ul> <li>The resulting points of stimulation.</li> </ul> </li> <li>Stim_IndexTime.dat <ul> <li>Contains the stimulation pattern in terms the node index of Mesh_Ven.vtu and the respective stimulation time</li> </ul> </li> <li>Mesh_Ven.vtu <ul> <li>Contains the ventricular mesh as well as the repective lead field matrix values for each surface node.</li> <li>Material:<br> Right Ventricle 30<br> Left Ventricle 31</li> </ul> </li> <li>Mesh_Torso.vtu <ul> <li>Contains the whole torso mesh.</li> <li>Material:<br> Fat 2<br> Bones 3<br> Blood 9<br> Cartilage 14<br> Liver 20<br> Lungs 17<br> Right Ventricle 30<br> Left Ventricle 31<br> Right Atrium 32<br> Left Atrium 33<br> Aorta 60<br> Pulmonary artery 61<br> Left Vena Jugularis 62<br> Right Vena Jugularis 62<br> Post Vena Cava 62</li> </ul> </li> </ul> <p> </p>
Dataset of UAV thermal video sequences with annotations for MOTS benchmarking
<p>Instance segmentation dataset created for the research 'Monitoring Mammalian Herbivores via Convolutional Neural Networks implemented on Thermal UAV imagery'. It comprises 959 frames, 20.647 masks, and 239 tracks, and consists of 7 video sequences depicting aerial thermal imagery of cattle collected with a UAV (Parrot ANAFI Thermal) in two outdoor farms in the Netherlands. Data were acquired at three temperatures (10ºC, 19ºC, and 26.5ºC), under sunny and overcast weather conditions, at various angles of inclination (including nadir), and at heights ranging between 8-28 meters. Ground truth was labeled manually with the Computer Vision Annotation Tool <em>CVAT</em>.</p>
ManyTypes4TypeScript: A Comprehensive TypeScript Dataset for Sequence-Based Type Inference
<p>In this paper, we present ManyTypes4TypeScript, a very large corpus for training and evaluating machine-learning models for sequence-based type inference in TypeScript. The dataset includes over 9 million type annotations, across 13,953 projects and 539,571 files. The dataset is approximately 10x larger than analogous type inference datasets for Python, and is the largest available for TypeScript. We also provide API access to the dataset, which can be integrated into any tokenizer and used with any state-of-the-art sequence-based model. Finally, we provide analysis and performance results for state-of-the-art code-specific models, for baselining. ManyTypes4TypeScript is available on Huggingface and Zenodo.</p> <p>This dataset was collected on January 22, 2022 and deduplicated with Allamanis code deduplication tool.</p>
CONGA: Copy number variation genotyping in ancient genomes and low-coverage sequencing data
<p>To date, ancient genome analyses have been largely confined to the study of single nucleotide polymorphisms (SNPs). Copy number variants (CNVs) are a major contributor of disease and of evolutionary adaptation, but identifying CNVs in ancient shotgun-sequenced genomes is hampered by (i) most published genomes being <1x coverage, (ii) ancient DNA fragments being typically <80 bps. These characteristics preclude state-of-the-art CNV detection software to be effectively applied to ancient genomes. Here we present CONGA, an algorithm tailored for genotyping deletion and duplication events in genomes with low depths of coverage. Simulations and down-sampling experiments show that CONGA can genotype deletions >1 kbps with F-scores >0.75 at >=1x, and distinguish between heterozygous and homozygous states. Using CONGA, we analyse deletion events at 10,018 loci in 56 ancient human genomes spanning the last 50,000 years, with coverages 0.4x-26x. We show that inter-individual genetic diversity measured using deletions and SNPs are highly correlated, as in modern-day genomes, confirming that deletion frequencies broadly reflect demographic history. We also identify signatures of strong purifying selection on deletions in ancient-genomes, such as an excess of singletons compared to those in SNPs. CONGA paves the way for systematic studies of drift, mutation load, and adaptation in ancient and modern-day gene pools through the lens of CNVs.</p>
Illumina sequencing data of Agro-mediated gene-edited apple lines
<p>FastaQ pair-end Illumina sequencing data of the Dipm1/4, Hipm1, and Mlo19 genes from the different apple Agro-edited lines obtained in the project. GA = Gala; GD = Golden Delicious</p> <p>Lines included are:</p> <p>GA1, GA3, GA4, GA5, and GA WT</p> <p>GD1, 2, 6, 10, 11, 12, 15, 17, 18, 19, 20, 21, 23, 24, 26, 27, 29, 31, 33, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, and WT</p>
MGBC-26640: nucleotide sequences for gene annotations
<p>Nucleotide sequences of annotated genes from the 26,640 high-quality, non-redundant genomes of the MGBC.</p>
Fig. 3 in Application Of Dna Barcoding In Taxonomy And Phylogeny: An Individual Case Of Coi Partial Gene Sequencing From Seven Animal Species
Fig. 3. Phylogenetic position of Macrobiotus sp., Bayesian inference phylogenetic tree. Sequences obtained by us are written in bold.
Fig. 1 in Application Of Dna Barcoding In Taxonomy And Phylogeny: An Individual Case Of Coi Partial Gene Sequencing From Seven Animal Species
Fig. 1. Phylogenetic position of D. lindholmi and L. a. exigua, Bayesian inference phylogenetic tree. Sequences obtained by us are written in bold.
Sequencing data of RNA editing, RNA modifications, and transcriptional units in Listeria monocytogenes
<p>Sequencing data for "RNA editing, RNA modifications, and transcriptional units in <em>Listeria monocytogenes</em>" manuscript, which is submitted to BMC genomics.</p>
Course 27255 - Subsampled Sequencing Results
<p>This dataset contains a subset of the raw fastq data of the microbial isolates collected during the course <strong>27255 - Advanced Experimental Prokaryotic Molecular Biology and Ecology (Spring 2022) </strong>at DTU.</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.