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19,342 results for “Sequences”
Comprehensive identification of pathogenic variants in retinoblastoma by long- and short-read sequencing
<p>Retinoblastoma (RB) is the most common intraocular malignancy in childhood. The causal variants in RB are mostly characterized by previously used short-read sequencing (SRS) analysis, which has technical limitations in identifying structural variants (SVs) and phasing information. Long-read sequencing (LRS) technology has significant advantages over SRS in detecting SVs, phased genetic variants, and methylation. In this study, we comprehensively characterized the genetic landscape of RB using combinatorial LRS and SRS of 16 RB tumors and 16 matched blood samples. We detected a total of 232 somatic SVs, with an average of 14.5 SVs per sample across the cohort. We identified 20 distinct pathogenic variants, including <span>three</span> novel small variants and <span>five</span> <span>somatic</span> SVs. Furthermore, our analysis shows that the vast majority (93.8%) of <em>RB1</em>-disrupted patients fit the two-hit hypothesis through diverse types, including the biallelic hypermethylated promoter as well as small and large compound heterozygous mutations which were missing in SRS analysis. By constructing the evolutionary history of all genetic variants, we reveal the evolution trends that <em>RB1</em> disruption early and followed by copy number changes, including amplifications of Chr2p, and deletions of Chr16q, during RB tumorigenesis. Altogether, we characterize the comprehensive genetic landscape of RB, providing novel insights into the genetic alterations and mechanisms contributing to RB initiation and development. Our work also establishes a framework to analyze genomic landscape of cancers based on LRS data. <br><br>We took IGV screenshots of SV and SNV/indels. The IGV naming conventions for SV is: sample name, chromosome 1, start, chromosome 2, end, SV type, SV length, SV breakpoint. The IGV naming convention for SNV/indels is: sample name, chromosome, position, reference allele, alternate allele.</p>
Phylogenetic and recombination analysis of adenovirus isolates reveals discordance between serotype and phylogeny: Multiple sequence alignments
<p><strong>Background</strong></p> <p>Human adenovirus (HAdV) infections are caused by seven mastadenovirus species (A-G) and are the source for a variety of pathologies including gastrointestinal, respiratory, neurological, and ocular disease. While HAdV-D is the most common cause of adenovirus ocular infections, human adenoviruses B and E have also been isolated from the eye. </p> <p><strong>Results</strong></p> <p>In the course of classifying three new atypical ocular adenovirus samples, taken from the vitreous humor, we found that all three isolates were HAdV-B species, with isolate BP-AdV1 sorting with the B1 clade, and isolates BP-AdV2 and BP-AdV3 grouping into the B2 clade. The three Bascom Palmer HAdV-B genomes were then combined with over 300 HAdV-B genome sequences, including 9 ocular HAdV-B genome sequences. The whole genome phylogenetic analysis showed that 9 of the 11 ocular sequences grouped into the B1 clade, forming two clusters within B1. Attempts to categorize the penton, hexon and fiber serotypes using phylogeny of the three Bascom Palmer samples were inconclusive due to incongruence between serotype and phylogeny in the dataset. Recombination analysis using a subset of HAdV-B strains to generate a hybridization network detected recombination between non-human primate and human derived strains, recombination between one HAdV-B strain and the HAdV-E outgroup and limited recombination between the B1 and B2 clades. </p> <p><strong>Conclusions</strong></p> <p>The discordance between serotype and phylogeny detected in this study suggests that the current penton/hexon/fiber-based classification mechanism does not accurately describe the natural history and phylogenetic relationships amongst adenoviruses. A new adenovirus strain classification strategy may be beneficial to the field.</p>
CO1 reference sequences dataset of the sedDNA Chironomidae study by Blattner et al. 2024
<p>This dataset contains the CO1 reference sequences used to identify the sedDNA metabarcoding reads in the study entitled "Sediment core DNA-Metabarcoding and chitinous remain identification: Integrating complementary methods to characterise Chironomidae biodiversity in lake sediment archives", which was submitted to Molecular Ecology Resources.</p>
Human intestinal Bacteria Collection (HiBC): Plasmids sequences
<p>The <a href="https://hibc.rwth-aachen.de/" target="_blank" rel="noopener">Human intestinal Bacteria Collection (HiBC)</a> is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly.</p> <p>This dataset includes the plasmids sequences of some of the isolates in the FASTA nucleotide format.</p>
Single-molecule structural and kinetic studies across sequence space
<p>Data and analysis code related to the research article:</p> <p>Single-molecule structural and kinetic studies across sequence space</p>
Alignment of ITS sequences of Erysiphe quercicola and related Erysiphe species
<p>Internal transcribed spacer sequences (ITS) of the nuc rDNA are given by their scientific name, the specimen and GenBank accession numbers, the host name and country in an alignment composed by MUSCLE implemented in MEGA X.</p>
Datasets for evaluating SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data
<p>This resource contains pre-processed A. thaliana root , the H. sapiens aortic valve datasets, PBMC Covid atlas and public 10x datasetse used in the paper, SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data. The raw datasets provided in the links below are pre-processed for quality control with respect to both cells and genes. </p> <p>A. thaliana datasets are sourced from the following locations at <a href="https://www.ebi.ac.uk/gxa/sc/home">Single-cell Gene expression Atlas </a>and <a href="https://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus (GEO)</a>:</p> <ol> <li>E-GEOD-121619 : <a title="E-GEOD-121619" href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results</a></li> <li>E-GEOD-152766 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results</a></li> <li>E-GEOD-158761 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results</a></li> <li>E-GEOD-123013 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results</a></li> </ol> <p>H. sapiens datasets are obtained from the NCBI database : <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/ </a></p> <ol> <li>GSE152766: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766</a></li> <li>GSE158761: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761</a></li> </ol> <p>All COVID atlas datasets are from: <a href="http://covid19.cancer-pku.cn">http://covid19.cancer-pku.cn</a> . covid_atlas_data1.zip contains the h5ad files and covid_atlas_data2.zip contains the Seurat rds files.</p> <p>PBMC datasets are from the following public sources:</p> <table> <tbody> <tr> <td>Dataset Name</td> <td>Chemistry Version</td> <td>Web Link</td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high</a></td> </tr> <tr> <td>20k Human PBMCs, 3' HT v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0">https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0</a></td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium Controller</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 1)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 5)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0</a></td> </tr> <tr> <td>10k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v2 chemistry)</td> <td>v2.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>Fresh 68k PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor B)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor C)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0</a></td> </tr> <tr> <td>PBMCs from a Healthy Donor: Whole Transcriptome Analysis</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0">https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0</a></td> </tr> <tr> <td>PBMC 600K</td> <td>v1</td> <td><a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads">https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads</a></td> </tr> <tr> <td>GSM4560071</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071</a></td> </tr> <tr> <td>GSM4560074</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074</a></td> </tr> <tr> <td>GSM4560070</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070</a></td> </tr> </tbody> </table> <p>References for the Datasets :</p> <ol> <li>H. sapiens dataset: Kang Xu, Shangbo Xie,Yuming Huang,Tingwen Zhou, Ming Liu, Peng Zhu, Chunli Wang, Jiawei Shi, Fei Li,Frank W. Sellke and Nianguo Dong (2020) Cell-Type Transcriptome Atlas of Human Aortic Valves Reveal Cell Heterogeneity and Endothelial to Mesenchymal Transition Involved in Calcific Aortic Valve Disease.</li> <li>E-GEOD-152766: Shahan R, Hsu C, Nolan TM, Cole BJ, Taylor IW et al. (2020) A single cell Arabidopsisroot atlas reveals developmental trajectories in wild type and cell identity mutants.</li> <li>E-GEOD-121619: Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, Dorrity MW, Saunders L et al. (2019) Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana.</li> <li>E-GEOD-123013: Ryu KH, Huang L, Kang HM, Schiefelbein J. (2019) Single-Cell RNA Sequencing Resolves Molecular Relationships Among Individual Plant Cells.</li> <li>E-GEOD-158761: Gala HP, Lanctot A, Jean-Baptiste K, Guiziou S, Chu JC et al. (2020) A single cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana.</li> <li>COVID Atlas Reference: Xianwen Ren, Wen Wen, Xiaoying Fan et.al. (2021) COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas</li> <li>PBMC data are downloaded from respective links</li> </ol>
Figure 1 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 1. Map of geographical locations of research sites.
Mobility-assisted psuedo-MS3 sequencing of protein ions
<p>The sequencing of intact proteins within a mass spectrometer has many benefits but is frequently limited by the fact that tandem mass spectrometry (MS/MS) techniques often generate poor sequence coverages when applied to protein ions. To overcome this limitation exotic MS/MS techniques that rely on lasers and radical chemistry have been developed. These techniques generate high sequence coverages, but they require specialized instrumentation, create products through multiple dissociation mechanisms, and often require long acquisition times. Recently, we demonstrated that protein ions can be dissociated in a trapped ion mobility spectrometry (TIMS) device prior to mobility separation in a commercial timsTOF. All generated product ions were distributed throughout the mobility dimension and this separation enabled deconvolution of complex tandem mass spectra and could enable facile pseudo-MS<sup>3</sup> interrogation of generated product ions with the downstream quadrupole and collision cell. A second activation step improves sequence coverage because the most labile bonds have been depleted during the first dissociation and subsequent dissociation events are more evenly distributed throughout the product ion backbone. In this work, we explore the potential of this mobility-assisted pseudo-MS<sup>3</sup> (MAP) method on a commercial timsTOF and timsTOF Pro 2. We demonstrate that while MAP only generates 92% of the sequence coverage of the most effective MS/MS technique, it accomplished this feat in 1.5 mins and could be facilely integrated with liquid chromatographic separations.</p>
Data from: Affordable de novo generation of fish mitogenomes using amplification-free enrichment of mitochondrial DNA and deep sequencing of long fragments
<p>Biomonitoring surveys from environmental DNA make use of metabarcoding tools to describe the community composition. These studies match their sequencing results against public genomic databases to identify the species. However, mitochondrial genomic reference data are yet incomplete, only a few genes may be available, or the suitability of existing sequence data is suboptimal for species-level resolution. Here we present a dedicated and cost-effective workflow with no DNA amplification for generating complete fish mitogenomes for the purpose of strengthening fish mitochondrial databases. Two different long-fragment sequencing approaches using Oxford Nanopore sequencing coupled with mitochondrial DNA enrichment were used. One where the enrichment is achieved by preferential isolation of mitochondria followed by DNA extraction and nuclear DNA depletion ('mitoenrichment'). A second enrichment approach takes advantage of the CRISPR-Cas9 targeted scission on previously dephosphorylated DNA ('targeted mitosequencing'). The sequencing results varied between tissue, species, and integrity of the DNA. The mitoenrichment method yielded 0.17-12.33 % of sequences on target and a mean coverage ranging from 74.9 to 805-fold. The targeted mitosequencing experiment from native genomic DNA yielded 1.83-55 % of sequences on target and a 38 to 2123-fold mean coverage. This produced complete the mitogenome of species with homopolymeric regions, tandem repeats, and gene rearrangements. We demonstrate that deep sequencing of long fragments of native fish DNA is possible and can be achieved with low computational resources in a cost-effective manner, opening the discovery of mitogenomes of non-model or understudied fish taxa to a broad range of laboratories worldwide.</p>
Analysis of public single-cell sequencing database of COVID lung samples
<p>Lung endothelial cells from three published scRNA-seq datasets (GSE122960, GSE149878, GSE171668) of healthy subjects and COVID-19 patients were collected for further integrative analyses. The endothelial cells were classified into three sub-groups according to their distinguished expression of IL7R, DKK2, and EDNRB. For differential analysis of gene expression, counts per million of aggregated UMIs in each group were adopted in Wilcoxon rank-sum test.</p>
Ranger models for predicting isoform abundance from UTR sequence features
<p>Each RDS file contains a ranger object, trained on transcripts after removing those associated with the held-out genes in one of the five cross-validation folds. The day and replicate number in the file name corresponds to the neuronal differentiation sample on which the model was trained. The file gene_folds.txt indicates the fold from which each gene was excluded during model training. The file transcript_gene_associations.txt contains transcript-gene associations. The file predictors.RDS contains the matrix of predictor variables.</p>
Plenoptic 2.0 - RayTrix R8 and RayTrix R32 - Stereo View Video Sequence: TempleBoatGiant
<p>The test sequence "Plenoptic 2.0 - RayTrix R8 and RayTrix R32 - Stereo View Video Sequence: TempleBoatGiant" is provided by Sarah Fachada, Daniele Bonatto, Jaime Sancho, Gauthier Lafruit,Mehrdad Teratani, and Eduardo Juárez, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium and CITSEM (Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad), UPM (Universidad Politecnica de Madrid), Spain.</p> <h2>License:</h2> <p>CC BY-NC-SA</p> <h2>Terms of Use:</h2> <p>Any kind of publication or report using this sequence should refer to the following references:</p> <p>[1] **Sarah Fachada, Daniele Bonatto, Jaime Sancho, Gauthier Lafruit, Mehrdad Teratani and Eduardo Juárez, "Plenoptic 2.0 - RayTrix R8 and RayTrix R32 - Stereo View Video Sequence: TempleBoatGiant," 2024.07, 10.5281/zenodo.12663777.**</p> <blockquote> <p>@misc{fachada_templeboatgiantvideo_2024,</p> <p> title = {{Plenoptic} 2.0 {Raytrix} {R8} and {Raytrix} {R32} - {Stereo} {View} {Video} {Sequence}: {TempleBoatGiant}},</p> <p> author = {Fachada, Sarah and Bonatto, Daniele and Sancho, Jaime and Lafruit, Gauthier and Teratani, Mehrdad and Juárez, Eduardo},</p> <p> month = jul,</p> <p> year = {2024},</p> <p> doi = {10.5281/zenodo.12663777}</p> <p>}</p> </blockquote> <p>[2] **Sarah Fachada, Daniele Bonatto, Jaime Sancho, Gauthier Lafruit, Mehrdad Teratani and Eduardo Juárez, "[LVC] A New Plenoptic 2.0 - RayTrix R8 and RayTrix R32 - Stereo View Video Sequence: TempleBoatGiant [M68363]," 2024.07, Sapporo, Japan, ISO/IEC JTC1/SC29/WG04**</p> <blockquote> <p>@article{fachada_lvc_templeboatgiantvideo_2024,</p> <p> title = {{[LVC]} a {New} {Plenoptic} 2.0 {Raytrix} {R8} and {Raytrix} {R32} - {Stereo} {View} {Video} {Sequence}: {TempleBoatGiant}},</p> <p> author = {Fachada, Sarah and Bonatto, Daniele and Sancho, Jaime and Lafruit, Gauthier and Teratani, Mehrdad and Juárez, Eduardo},</p> <p> month = jul,</p> <p> year = {2024},</p> <p> address = {Sapporo, Japan},</p> <p> edition = {MPEG2021/M68363},</p> <p> journal = {ISO/IEC JTC1/SC29/WG04}</p> <p>}</p> </blockquote> <h2>Production:</h2> <p>Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Université Libre de Bruxelles, Belgium,</p> <p>Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad, CITSEM, Universidad Politécnica de Madrid, Spain.</p> <h2>Content:</h2> <p>This dataset contains a dynamic scene representing an artistic view of a boat floating on an ocean made of colored blocks, near a temple, while a giant is chasing them. The boat is animated by a giant wooden hand. The dataset is in the format of plenoptic images extracted from Raytrix [1] .rays files. The dataset was acquired with two Raytrix cameras, the R8 and the R32, mounted on the UPM’s robotic bench, keeping the same positon during the video. The distance transformation between the two Total Focus cameras is T= (193.968, 6.13558, 126.865) (mm), R= (-2.7386289, -11.0520427, 1.479145) (°). The main lens focal length focals are F=25 for the R8 and F=50 for the R32.</p> <p>The dataset additionally contains calibration images of a checkerboard with square size of 12mm.</p> <p>1. **Scene**</p> <ul> <li>300 frames of processed images extracted from .ray file.</li> <li>Total focus videos</li> </ul> <p>2. **Calibration images:**</p> <ul> <li>Processed and raw views of a checkerboard with 12mm square size.</li> <li>Raw gray images to extract the processed image.</li> </ul> <p>MLA Calibration files are provided in XML format.</p> <h2>The dataset contains:</h2> <p>- `video_{R32/R8}` folders containing:</p> <ul> <li>Processed plenoptic PNG files</li> <li>XML MLA calibration files</li> <li>Total focus MP4 videos extracted with RxLive software for visualization of the scene.</li> </ul> <p>- A `checkerboard` folder containing:</p> <ul> <li>XML MLA calibration files</li> <li>8 Plenoptic raw images in PNG format of a checkerboard with square size of 12mm</li> <li>8 Plenoptic processed images in PNG format of the checkerboard</li> <li>Gray images in PNG for raw file processing.</li> </ul> <h2>References and links:</h2> <p>[1] https://raytrix.de/</p> <h2>Acknowledgments:</h2> <p>Sarah Fachada is a Postdoctoral Researcher of the Fonds de la Recherche Scientifique - FNRS, Belgium.</p> <p>This work was supported in part by the HoviTron project (no. 951989), the FER 2021 project (no. 1060H000066-FAISAN), the Emile DEFAY 2021 project (no. 4R00H000236), and the FER 2023 project (no. 1060H000075).</p> <p>Additionally, this work has been funded by the project AIMS5.0, supported by the Chips Joint Undertaking and its members, including top-up funding by National Funding Authorities from involved countries (no. 101112089), and the European project STRATUM (no. 101137416). The robotic bench was funded by “Programa Propio UPM” in the call “convocatoria de ayudas a centros e institutos de I+D+i”.</p>
Fig. 3 in Prevalence of a new genetic group, MEAM-K, of the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) in Karnataka, India, as evident from mtCOI sequences
Fig. 3. Graph indicating net evolutionary divergence between groups of sequences.
FIGURE 2 in Eocene planktonic foraminifera from the north Eastern Desert, Egypt: Biostratigraphic, paleoenvironmental and sequence stratigraphy implications
FIGURE 2. Lithologic and biostratigraphic units of the studied sections (A, B, C).
FIGURE 5 in Eocene planktonic foraminifera from the north Eastern Desert, Egypt: Biostratigraphic, paleoenvironmental and sequence stratigraphy implications
FIGURE 5. Range chart of the identified planktonic species in section C.
FIGURE 8 in Eocene planktonic foraminifera from the north Eastern Desert, Egypt: Biostratigraphic, paleoenvironmental and sequence stratigraphy implications
FIGURE 8 (caption on next page).
FIGURE 3 in Eocene planktonic foraminifera from the north Eastern Desert, Egypt: Biostratigraphic, paleoenvironmental and sequence stratigraphy implications
FIGURE 3. Range chart of the identified planktonic species in section A.
Aligned_sequences
<p>A csv file of aligned sequence for the pre-training of DNABERT-S. Used during the master thesis 'Transformer-based AI models as classification tool for DNA-metabarcoding' by JSchreijer. <span><br></span></p>
YfaL sequence analysis
<p><strong><span>Table S6A</span></strong><span>- Table showing parameters related to each yfaL sequence identified. The contig identifier and position of the sequence in it (column A), the strain name (column B),<span> </span>the phylogroup (column C), the size of each structural part of the sequence (column D to H), the number of mutations in these parts (column I to M), the related mutation frequency (column N to R). </span><strong><span>Table S6B</span></strong><span>- Table showing the number of yfaL sequences in each phylogroup as well as the related mutation frequences in the whole sequence<span> </span>and for each structural part of it. </span></p>
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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.