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19,342 results for “sequence”
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 10. - Maximum-likelihood phylogeny of Epicephala species based on sequences of the COI, ArgK and EF1α genes. Numbers above nodes are maximum-likelihood bootstrap support values based on 1,000 replications. The Japanese Epicephala species are marked in blue. Symbols right to species names donate ovipositor morphology: inverted U-shape, rounded apically; inverted V-shape, acute apically.
Figure 10. - Maximum-likelihood phylogeny of Epicephala species based on sequences of the COI, ArgK and EF1α genes. Numbers above nodes are maximum-likelihood bootstrap support values based on 1,000 replications. The Japanese Epicephala species are marked in blue. Symbols right to species names donate ovipositor morphology: inverted U-shape, rounded apically; inverted V-shape, acute apically.
Timelapse sequences of radish and tomato growth from seed
<p>These files accompany a schools plant imaging workshop, created by Hannah Dee and colleagues as part of EPSRC Grant EP/LO17253/1.</p> <p>The aim of the workshops is to introduce school-aged pupils to the concepts of plant imaging, and to image processing by building up a set of python programs that can open and investigate properties of images. The images in question are images of plants as they grow.</p> <p>Workshop one concerns the creation of such datasets and can be found here:<br> https://docs.google.com/document/d/1tXNXWhs49XUKWZl5s51mYF4Rpl44P00eih7Ulr8CHvw/edit#</p> <p>Workshop two concerns investigating light changes in such datasets, introducing the concept of a "colour space" and can be found here:<br> https://docs.google.com/document/d/1cAqEZQ-llwgvifhOiNROsFp91AaMjjU0qJOn5F7wb5o/edit#</p> <p>These files allow students to skip workshop one and use our pre-recorded timelapse sequences. Further workshops are planned and will be linked from the google docs above when completed.</p>
CPBI_seqdb_demo sample QFO sequence library
<p>A medium-sized (approx 1 million entry) protein sequence database constructed from the NCBI 'nr' (Jan, 2017) database selecting Uniprot (SwissProt), RefSeq, and PDB entries for 66 species (taxon_id's) from the Quest for Orthologs organism set. These files are designed to be used in conjunction with scripts and SQL files to construct the seqdb_demo database, as described in a Current Protocols in Bioinformatics Unit 3.9 revised Spring, 2017. The files are:</p> <p>qfo_demo.gz - a fasta-format sequence library with the curren NR Defline format (gzip compressed)</p> <p>qfo_prot.accession2taxonid.gz, qfo_pdb.accession2taxid.gz- tables that map accessions to taxon_id's and gi-numbers, similar to that available in the NCBI pub/taxonomy/accession2taxid/prot.accession2taxid and pdb.accession2taxid files (gzip compressed).</p>
Entire confocal z-stack series as .tif image sequences
<p>The manuscript entitled "Parvalbumin-expressing ependymal cells in rostral lateral ventricle wall adhesions contribute to aging-related ventricle stenosis in mice" shows confocal z-stack maximum intensity projections and thin z-plane reconstructions in the figure plates. The entire confocal z-stack image series are provided here as .tif image sequences, respectively the confocal z-stacks of the negative controls as well. The file names refer to the figure numbers and position in the figure plates. For more information about the immunostaining and image acquisition, see the Materials & Methods and Figure legends in the manuscript.</p>
MRI raw data: RF-spoiled radial FLASH sequence without cardiac gating and during free breathing
<p>RF-spoiled radial FLASH sequence without cardiac gating and during free breathing<br> Sequence parameters:<br> TR 2.0ms<br> TE 1.3 ms<br> Flip angle 8°<br> Matrix Size 128x128 (two-fold oversampling, resulting in 256 sample points for each radial spoke)<br> Radial Spokes: 125<br> In Plane Resolution 2mmx2mm<br> Slice Thickness 8mm<br> 3T System<br> 32 channel body array coil, compressed to 12 virtual channels using svd based coil compression<br> ote: This data set was acquired with a radial acquisition. The corresponding k-Space trajectory is also included (256x125 matrix k). The Non-Uniform-FFT Toolbox is needed for reconstruction of this data set.<br> Florian Knoll (florian.knoll@tugraz.at) Date: 2.2.2011</p>
Massively parallel sequencing data of the HIV-1 pol region generated from the plasma of therapy-naïve chronically infected Brazilian blood donors
<p>The submitted massively parallel sequencing (MPS) data were partial data from the pol region of HIV-1 plasma viruses. Samples were obtained from 18 therapy-naive HIV-1 Brazilian blood donors with longstanding infection. Illumina ultra-deep sequencing technology (MiSeq platform) was used to generate the sequences. </p>
Pre-processed B cell receptor sequences from BioProject PRJNA349143
<p>Processed sequencing data from BioProject PRJNA349143.</p> <p><strong>Study Design</strong></p> <p>Samples were collected from human volunteers as described in Laserson and Vigneault et al, 2014 (1). Briefly, blood samples were collected from three individuals both pre- and post-vaccination for seasonal influenza. Samples were collected for sequencing at time points -8 days, -2 days, -1 hour, +1 hour, +1 day, +3 days, +7 days, +14 days, +21 days and +28 days relative to injection with seasonal influenza vaccine.</p> <p><strong>Library Preparation and Sequencing</strong></p> <p>The original samples from Laserson and Vigneault et al, 2014 (1) were re-sequenced as described in Gupta et al, 2017 (2). Briefly, sequencing libraries were prepared from mRNA using 5'RACE with addition of 17-nucleotide unique molecular identifiers (UMIs). Amplification was performed using constant region primers specific to IGHA, IGHD, IGHE, IGHG, IGHM, IGKC and IGLC. Sequencing was conducted on the Illumina MiSeq platform using the 600 cycle kit with 325 cycles for read 1 and 275 cycles for read 2. A 10% PhiX spike-in was added for sequencing.</p> <p><strong>Data Processing</strong></p> <p>Sequences were processed using the pRESTO (3) and Change-O (4) toolkits as described in Gupta et al, 2017 (2).</p> <p>Note, the provided data has been filtered significantly, including the removal of sequences that fail V(D)J alignment and the exclusion of non-functional sequences.</p> <p><strong>Format</strong></p> <p>Processed sequences are provided in FASTA format annotated using the pRESTO scheme.</p> <p>Annotations included are as follows:</p> <ul> <li><strong>CONSCOUNT:</strong> Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</li> <li><strong>DUPCOUNT:</strong> UMI count for the given unique sequence.</li> <li><strong>PRCONS:</strong> Constant region primer (isotype).</li> <li><strong>SUBJECT:</strong> Subject identifier.</li> <li><strong>TIME_POINT:</strong> Time point label.</li> </ul> <p><strong>Citations</strong></p> <ol> <li>Laserson U and Vigneault F, et al. High-resolution antibody dynamics of vaccine-induced immune responses. Proc Natl Acad Sci USA 111, 4928-33 (2014).</li> <li>Gupta NT, et al. Hierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data. J Immunol 1601850 (2017).</li> <li>Vander Heiden JA and Yaari G, et al. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 30, 1930–2 (2014).</li> <li>Gupta NT and Vander Heiden JA, et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–8 (2015).</li> </ol>
Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included
<p>Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included.</p>
Human bone marrow assessment by single-cell RNA sequencing
<p>Seurat objects and CyTOF data for <a href="https://doi.org/10.1172/jci.insight.124928">10.1172/jci.insight.124928</a></p> <p>Please run UpdateSeuratObject() after loading. Small object contains annotated metadata with cca and tsne analyses. Large object contains additional reductions (eg umap). </p>
Low-frequency Cortical Activity Reflects Context-dependent Parsing of Word Sequences
<p>The dataset and code used for the publication of "<a href="https://www.biorxiv.org/content/10.1101/2024.11.12.623335v1" target="_blank" rel="noopener"><strong>Low-frequency Cortical Activity Reflects Context-dependent Parsing of Word Sequences</strong></a>".</p> <div> <div><strong>Data path</strong></div> <div> <ol> <li>derivatives (./data/derivatives/): preprocessed data. <ul> <li>meg_quat-tsss-withEOG_100hz_0d3-40: meg data preprocessed at 100 Hz filtered between 0.3 and 40 Hz.</li> <li>FreeSurfer: anatomy data preprocessed</li> </ul> </li> <li>behavior_data (./behavior_data/)</li> <li>stimuli (./exp_procedure/material/): exp stimuli and procedure</li> <li>features (./data/features/): calculated phonological and linguistic features.</li> </ol> </div> <strong>Code path</strong></div> <div><br> <div>Preprocessing<strong>:</strong></div> <div> <ol> <li>[preprocessing.py]: preprocessing of behavior and MEG data.</li> <li>[recon.py]: reconstruction of T1 .niift data into surface data in FreeSurfer.</li> <li>[analysis_stimuli.ipynb]: preprocessing and feature extraction of stimuli.</li> </ol> </div> Analysis</div> <div> <ul> <li>[analysis_meg.ipynb]: spectral, time course, and RSA analysis.</li> </ul> </div>
Cross-disease integration of single-cell RNA sequencing data from lung myeloid cells reveals TAM signature in in vitro model
<p>Single cells from a 3D human cell-based model comprising tumor cell line-derived spheroids, cancer-associated fibroblasts and primary monocytes were dissociated and analyzed using scRNAseq. 4 monocyte donors were used in the 3D model, and 3 monocyte donors were used for 2D differentiation of macrophages.</p>
Grain size and clay mineralogy data of the Esplugafreda sequence (Spain) with stable carbon, oxygen and clumped isotope data of soil carbonates
<p>This datasets contains 1) grain size distribution data of mudstone paleosols of the Esplugafreda sequence (Esplugafreda and Claret Formations) measured by laser diffraction, and 2) clay mineralogy measured by powder X-ray diffraction, as well as 3) stable carbon, oxygen and clumped (D47) isotope compositions of soil carbonates. The Esplugafreda sequence is found in the Tremp-Graus Basin in the southern forefront of the Pyrenees and consists of continental sediments formed in a coastal alluvial setting during the late Paleocene and early Eocene.</p> <p>In addition, a proxy dataset of late Paleocene and PETM continental temperatures of the northern hemisphere is also included.</p>
SMAdd-seq: Probing chromatin accessibility with small molecule DNA intercalation and nanopore sequencing
<p>Studies of in vivo chromatin organization have relied on the accessibility of the underlying DNA to nucleases or methyltransferases, which is limited by their requirement for purified nuclei and enzymatic treatment. Here, we introduce a nanopore-based sequencing technique called Small-Molecule Adduct sequencing (SMAdd-seq), where we profile chromatin accessibility by treating nuclei or intact cells with a small molecule, angelicin. Angelicin reacts with thymine bases in linker DNA not bound to core nucleosomes after UV light exposure, thereby labeling accessible DNA regions. By applying SMAdd-seq in Saccharomyces cerevisiae, we demonstrate that angelicin-modified DNA can be detected by its distinct nanopore current signals. To systematically identify angelicin modifications and analyze chromatin structure, we developed a neural network model, NEural network for mapping MOdifications in nanopore long-reads (NEMO). NEMO accurately called expected nucleosome occupancy patterns near transcription start sites at both bulk and single-molecule levels. We observe heterogeneity in chromatin structure and identify clusters of single-molecule reads with varying configurations at specific yeast loci. Furthermore, SMAdd-seq performs equivalently on purified yeast nuclei and intact cells, indicating the promise of this method for in vivo chromatin labeling on long single molecules to measure native chromatin dynamics and heterogeneity.</p>
ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data. Additional Datasets.
<p>Data supporting the main figures of the "ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data" manuscript and a copy of the ParaMask software and scripts for analysis, and SV calls from longreads. README files are included.</p>
Uncovering hundreds of RNA viral RdRps amongst uncharacterised sequences in public protein databases.
<p>These data are associated with the following manuscript:</p> <p>Brown, K., Firth, A. E. (2025)<br>Uncovering hundreds of RNA viral RdRps amongst uncharacterised sequences in public protein databases.<br><br></p>
Supplementary data 'Mitochondrial genome sequence of the protist Ancyromonas sigmoides Kent, 1881 (Ancyromonadida) from the Sugluk Inlet, Hudson Strait, Nunavik, Québec'
<p>Fasta file with the transcripts obtained from RNAseq sequencing of Ancryomonas sigmoides (kmer 35) and file with datamining results.</p><p>Fasta file of the transcript matching the cox1 gene.</p><p>Scaffolds of the kmer 85 assembly of the genomic data of Ancryomonas sigmoides.</p><p>Databse used for datamining (fasta file)</p>
Amino acid sequences of annotated genes in Pelargonium zonale
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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.