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4,683 results for “embryonic”
Identification of a 41-bp Embryonic Cell-Specific Region within the Pineapple SERK1 Promoter
<p>Plant regeneration via somatic embryogenesis (SE) is a key step during genetic engineering. <em>AcSERK1</em> is a gene specifically expressed in the early stage of SE in pineapple (<em>Ananas comosus</em>), suggesting that the promoter of <em>SERK1</em> might contain specific cis-acting element to regulate SE.</p>
Fluid Mechanics of the Zebrafish Embryonic Heart Trabeculation
<p>Images, simulation files, and user-defined functions used for the data shown in the manuscript "Fluid Mechanics of the Zebrafish Embryonic Heart Trabeculation".</p>
ALRA-imputed E10 and E11 mouse embryonic eye region dbit-seq data from Liu et al., Cell 183.6 (2020): 1665-1681
<p>E10 and E11 mouse embryonic eye region dbit-seq data demonstrated in Liu, Yang, et al. “High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue.” Cell 183.6 (2020): 1665-1681. The data are processed using the original pipeline and then imputed by ALRA.</p>
Response of different embryonic developmental stages of Radix balthica to a 24 h 36°C exposure
<p>Sample datasets and video acquired using the EmbryoPhenomics platform. Experiment assessing influence of culture at 36C for 24 h on three developmental stages (E3, E7 and E9) of the aquatic gastropod <em>Radix balthica</em>. The EmbryoCV package was used to produce phenome-level data. Datasets and associated video for an individual embryo from each developmental stage.</p>
Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development
<p>This upload contains the expression prediction dataset discussed in the manuscript "Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development" and used by the webserver https://find.princeton.edu.</p> <p>Abstract:</p> <p>Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput <em>in situ</em> hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like <em>Drosophila</em>, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current <em>in situ</em> hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver <a href="http://find.princeton.edu/">http://find.princeton.edu</a>, which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles.</p>
Supplementary material 2 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S2. Effects of water extracts of leaves of various plants on the numbers of egg pods laid when presented to adult female desert locusts mixed with sand.
Supplementary material 1 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S1. Effects of water extracts of desert locust frass after feeding on various plants on the numbers of egg pods laid when adult female desert locusts were presented with extracts mixed with sand.
Supplementary material 3 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S3. Effects of water extracts of rescue grass-fed desert locust frass on antennal lengths of embryos (A, mean ± SD; n = 6–10).
Annotation for human genomic variation during the BMP4-induced conversion from embryonic stem cells to trophoblast by bone
<p>Whole genomic data of three cell lines were sequenced. These three cell lines are two hESC lines (H1 & H9) by invasion assays and iPSC cell line MRucR. These three cell lines are two hESC lines (H1 & H9) by invasion assays and iPSC cell line MRucR. Paired-end DNA libraries were prepared according to manufacturer’s instructions (Illumina Truseq Library Construction). And then, valid sequencing data is mapped to the reference genome (UCSC hg19) by Burrows-Wheeler Aligner (BWA) software. Reads that aligned to genomic regions were collected for mutation identification and subsequent analysis. Samtools mpileup and bcftools are used to do variant calling and identify SNP, indels. Control-free (Boeva V et al.2012) is utilized to do CNV detection. And BreakDancer (Chen K et al.2009) is applied to detect SV information. And, Single nucleotide variant (SNV) and small somatic insertions and deletions (indels) were identified using Strelka2.</p>
Fig. 7 in An Embryonic Oviraptorid (Dinosauria: Theropoda) from the Upper Cretaceous of Mongolia
Fig. 7. The skull of IGM 100/971 in dorsal view. The anterior right lateral surface of the skull is visible due to oblique crushing on the palate. Abbreviations are given in appendix 1.
Fig. 6 in An Embryonic Oviraptorid (Dinosauria: Theropoda) from the Upper Cretaceous of Mongolia
Fig. 6. Although refuted by evidence, museum displays and popular accounts often show Protoceratops andrewsi associated with oviraptorid eggs and nests. This example from the American Museum of Natural History shows two Protoceratops in the vicinity of a nest. This mount was created in 1935 to highlight the findings of the first reported dinosaur nests by the museum's Central Asiatic Expeditions.
Fig. 2 in An Embryonic Oviraptorid (Dinosauria: Theropoda) from the Upper Cretaceous of Mongolia
Fig. 2. The Ukhaa Tolgod Xanadu site looking east. The site of the IGM 100/971 find is indicated with an arrow.
Fig. 8 in An Embryonic Oviraptorid (Dinosauria: Theropoda) from the Upper Cretaceous of Mongolia
Fig. 8. The skull and left knee of IGM 100/971. The skull is in anterior view showing the mandibular symphysis and the suture between the premaxillae. Abbreviations are given in appendix 1.
Fig. 9 in An Embryonic Oviraptorid (Dinosauria: Theropoda) from the Upper Cretaceous of Mongolia
Fig. 9. Detail of the basicranium, cervical vertebra, and shoulder region of IGM 100/971. Abbreviations are given in appendix 1.
ONT direct RNA sequencing Fast5 files from mouse embryonic stem cell (C57BL/6J x CAST/EiJ) - Rep1 (run3)
<p>We present a novel approach that leverages Oxford Nanopore direct RNA sequencing technology to detect allele-biased patterns of N6-methyladenosine (m6A) modifications in native mRNAs. Our approach utilized human and mouse cells with known genetic variants to ascertain allelic origin of each mRNA molecule. We employed a supervised machine learning model to detect read-level modification ratios, providing a comprehensive understanding of allele-specific m6A modification (ASM) patterns. To analyze the effects of allele specific m6A modification in mouse, we used hybrid mosue embryonic stem cells (C57BL/6J x CAST/EiJ). This page contains replicate 1 (run 3)</p>
ONT direct RNA sequencing Fast5 files from mouse embryonic stem cell (C57BL/6J x CAST/EiJ) - Rep2 (run1)
<p>We present a novel approach that leverages Oxford Nanopore direct RNA sequencing technology to detect allele-biased patterns of N6-methyladenosine (m6A) modifications in native mRNAs. Our approach utilized human and mouse cells with known genetic variants to ascertain allelic origin of each mRNA molecule. We employed a supervised machine learning model to detect read-level modification ratios, providing a comprehensive understanding of allele-specific m6A modification (ASM) patterns. To analyze the effects of allele specific m6A modification in mouse, we used hybrid mosue embryonic stem cells (C57BL/6J x CAST/EiJ). This page contains replicate 2 (run 1).</p>
ONT direct RNA sequencing Fast5 files from mouse embryonic stem cell (C57BL/6J x CAST/EiJ) Mettl3 Knockout
<p>We present a novel approach that leverages Oxford Nanopore direct RNA sequencing technology to detect allele-biased patterns of N6-methyladenosine (m6A) modifications in native mRNAs. Our approach utilized human and mouse cells with known genetic variants to ascertain allelic origin of each mRNA molecule. We employed a supervised machine learning model to detect read-level modification ratios, providing a comprehensive understanding of allele-specific m6A modification (ASM) patterns. To analyze the effects of allele specific m6A modification in mouse, we used hybrid mosue embryonic stem cells (C57BL/6J x CAST/EiJ). </p>
ONT direct RNA sequencing Fast5 files from mouse embryonic stem cell (C57BL/6J x CAST/EiJ) - Rep2 (run3-1)
<p>We present a novel approach that leverages Oxford Nanopore direct RNA sequencing technology to detect allele-biased patterns of N6-methyladenosine (m6A) modifications in native mRNAs. Our approach utilized human and mouse cells with known genetic variants to ascertain allelic origin of each mRNA molecule. We employed a supervised machine learning model to detect read-level modification ratios, providing a comprehensive understanding of allele-specific m6A modification (ASM) patterns. To analyze the effects of allele specific m6A modification in mouse, we used hybrid mosue embryonic stem cells (C57BL/6J x CAST/EiJ). This page contains replicate 2 (run 3-1; the files are splited two two parts because of limitations of file size).</p>
ONT direct RNA sequencing Fast5 files from mouse embryonic stem cell (C57BL/6J x CAST/EiJ) - Rep2 (run2)
<p>We present a novel approach that leverages Oxford Nanopore direct RNA sequencing technology to detect allele-biased patterns of N6-methyladenosine (m6A) modifications in native mRNAs. Our approach utilized human and mouse cells with known genetic variants to ascertain allelic origin of each mRNA molecule. We employed a supervised machine learning model to detect read-level modification ratios, providing a comprehensive understanding of allele-specific m6A modification (ASM) patterns. To analyze the effects of allele specific m6A modification in mouse, we used hybrid mosue embryonic stem cells (C57BL/6J x CAST/EiJ). This page contains replicate 2 (run 2)</p>
ONT direct RNA sequencing Fast5 files from mouse embryonic stem cell (C57BL/6J x CAST/EiJ) - Rep1 (run2)
<p>We present a novel approach that leverages Oxford Nanopore direct RNA sequencing technology to detect allele-biased patterns of N6-methyladenosine (m6A) modifications in native mRNAs. Our approach utilized human and mouse cells with known genetic variants to ascertain allelic origin of each mRNA molecule. We employed a supervised machine learning model to detect read-level modification ratios, providing a comprehensive understanding of allele-specific m6A modification (ASM) patterns. To analyze the effects of allele specific m6A modification in mouse, we used hybrid mosue embryonic stem cells (C57BL/6J x CAST/EiJ). This page contains replicate 1 (run 2). </p>
ScienceDex guides
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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.