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54,099 results for “Expression”
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>
mTagBFP2-expressing vectors for electroporation of marine protists
<p>Plasmid maps and genbank sequence files for the plasmids used for electroporation of model organism <em>Nannochloropsis oceanica</em> and environmental samples following the protocol <a href="https://www.protocols.io/view/fabrication-of-dna-constructs-by-gibson-assembly-a-7r8hm9w">Matute et al.</a></p> <p>EMS initiative from Gordon and Betty Moore foundation.</p>
supplementary expressions of "On the derivation of an admissibility condition for phase boundary propagation in an SMA bar based on a 3-D fo
<p>supplementary expressions of "On the derivation of an admissibility condition for phase boundary propagation in an SMA bar based on a 3-D fo-0</p>
Differential Gene Expression Dataset of Thalassiosira hyalina and Nitzschia frigida: Response to light stress under contemporary and elevated pCO2
<p>This is a time-course gene expression dataset on the transcriptomic responses of <em>Thalassiosira hyalina</em> and <em>Nitzschia frigida</em> to light stress under contemporary and elevated pCO2. The corresponding Sequencing data is deposited at the EBI ArrayExpress database under accession number E-MTAB-6999. Contigs were created with Trinity Assembler and are available under DOI:10.5281/zenodo.3361258</p> <p>The according publication is currently in review (8/6/2019): Higher sensitivity towards light stress and ocean acidification in an Arctic sympagic compared to a pelagic diatom;</p> <p>Author team: Ane C. Kvernvik, Sebastian D. Rokitta, Eva Leu, Lars Harms, Tove M. Gabrielsen, Björn Rost and Clara J. M. Hoppe</p> <p>Do not hesitate to contact the authors if you like more information!</p>
Fig. 5 in Transcriptome profiling of Symbion pandora (phylum Cycliophora): insights from a differential gene expression analysis
Fig. 5 Differential transcript expression analysis. Bar charts represent the enriched molecular functions associated with the upregulated genes in a feeding stages with Prometheus larva(e) and b feeding stages alone
Fig. 4 in Transcriptome profiling of Symbion pandora (phylum Cycliophora): insights from a differential gene expression analysis
Fig. 4 Differential transcript expression analysis. Bar charts represent the enriched biological processes associated with the upregulated genes in a feeding stages with Prometheus larva(e) and b feeding stages alone
Fig. 2 in Transcriptome profiling of Symbion pandora (phylum Cycliophora): insights from a differential gene expression analysis
Fig. 2 Scheme of the methodology employed in this study. In a first approach, the reference transcriptome (workflow in grey) was assembled de novo from three distinct life cycle stages: the feeding stage alone (asexual generation; note that in young feeding stages, the buccal funnel is located inside in the trunk), the feeding stage with Prometheus larva(e) attached to its trunk (sexual generation) and the free-swimming chordoid larva. Secondly, in the differential gene expression analysis (workflow in black), only two different conditions were investigated: feeding stages with Prometheus larva(e) attached to its trunk and feeding stages alone. Finally, sequenced reads were mapped to the reference transcriptome
Fig. 6 in Transcriptome profiling of Symbion pandora (phylum Cycliophora): insights from a differential gene expression analysis
Fig. 6 Differential transcript expression analysis. Bar charts represent the enriched cellular components associated with the upregulated genes in a feeding stages with Prometheus larva(e) and b feeding stages alone
Fig. 3 in A study of common scorpionfly (Mecoptera: Panorpidae) visual systems reveals the expression of a single opsin
Fig. 3 Panorpid wing banding in eight taxa. a P. nebulosa. b P. japonica. c P. pryeri. d P. isolata. e P. lewisi. f P. arakavae. g P. gracilis. h P. acuminata (chart generated in Adobe Illustrator)
Fig. 2 in A study of common scorpionfly (Mecoptera: Panorpidae) visual systems reveals the expression of a single opsin
Fig. 2 Maximum likelihood topology (score of −32468.33) generated from UV, blue, and LW opsin genes (image generated in Adobe Illustrator)
Fig. 1 a P in A study of common scorpionfly (Mecoptera: Panorpidae) visual systems reveals the expression of a single opsin
Fig. 1 a P. nebulosa, contributed by Gayle Strickland. b P. hungerfordii, contributed by Ken Sproule. c P. hungerfordii, contributed by Ken Sproule. d P. nebulosa, contributed by Gayle Strickland. e B. coloradensis. f B. coloradensis
Fig. 2 in The Effects of Continuous Acoustic Stress on ROS Levels and Antioxidant-related Gene Expression in the Black Porgy (Acanthopagrus schlegelii)
Fig. 2. The effects of exposure to the underwater noise on plasma (A) cortisol and (B) ROS levels during the 2-week treatment period. NC (solid line), QC (dashed line) and CC (dotted line). Means ± SEM are shown. N = 5 for initial control and N = 10 for all other groups. The measured samples were randomly selected from each treatment pool. *Mann-Whitney U-tests p <0.05. Solid bracket indicates comparisons between two groups at the same time point; dotted bracket indicate the comparison of the same group between different time points.
Fig. 1 in The Effects of Continuous Acoustic Stress on ROS Levels and Antioxidant-related Gene Expression in the Black Porgy (Acanthopagrus schlegelii)
Fig. 1. Acoustic characteristics of the noise perturbation experiment. The 1/3-octave band spectral characteristics of the noisy condition (NC; solid line with open circles), quiet condition (QC, dashed line with open squares), ambient noise (control, CC, dotted line with open triangles) and the hearing curve of the black porgy (dashed line with solid triangles) (Means ± SEM are shown). N = 5 at each frequency in the audiogram.
Fig. 3 in The Effects of Continuous Acoustic Stress on ROS Levels and Antioxidant-related Gene Expression in the Black Porgy (Acanthopagrus schlegelii)
Fig. 3. Relative expression levels of the indicated genes in fish exposed to wind turbine noise. (A) hepatic sod1 mRNA levels; (B) hepatic cat mRNA levels; (C) hepatic gpx mRNA levels. Means ± SEM are shown. # indicated the group N = 8, in all other groups N = 10. The measured samples were randomly selected from each treatment pool. One-way Kruskal-Wallis ** p <0.005 or * p <0.05. Solid bracket indicates comparisons between two groups at the same time point. Values with different letters differ from each other in the same group (CC, NC or QC) (one-way Kruskal-Wallis p <0.05).
Association between Galectin-1 gene expression and acute myeloid leukemia patient survival in The Cancer Genome Atlas
Open the record for dataset details and reuse information.
GTEx: DICOM converted whole slide hematoxylin and eosin stained images from the Genotype-Tissue Expression (GTEx) Project
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=gtex" target="_blank" rel="noopener">GTEx</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> <p>The<a href="https://commonfund.nih.gov/GTEx"> Genotype-Tissue Expression (GTEx) Project</a> established a data resource and tissue bank to study the relationship between genetic variants and gene expression in multiple human tissues and across individuals. The project included contributions from numerous groups with diverse expertise in biospecimen collection and processing, pathology review, molecular analysis, and data management. The contributors are collectively called the GTEx Consortium.</p> <p>GTEx collected a total of 26,468 unique tissue samples from 50+ different tissue types, from 956 healthy postmortem donors. The standardized biospecimen collection and analysis practices applied during the study served to minimize preanalytical variability associated with specimen-related factors and their potential impact on analytic endpoints. Each GTEx tissue was divided into two tissue blocks, one for histology and one for molecular analysis; both tissue blocks were preserved in PAXgene Tissue Fixative (Qiagen) solution for 6 to 24 hours, followed by PAXgene Tissue Stabilizer (Qiagen) as specified in the project-specific<a href="https://biospecimens.cancer.gov/resources/sops/library.asp"> standard operating procedures</a>. Tissue blocks were processed and embedded in paraffin at the GTEx central repository at the Van Andel Institute (MI) and hematoxylin and eosin–stained slides were generated from all GTEx donors. Digitally scanned whole slide images of PAXgene-fixed/stabilized, paraffin-embedded tissue sections were created using Aperio Scanscope software (Leica Biosystems). The digital images were then reviewed and annotated by one of four board-certified pathologists assigned to the GTEx study. There are a total of 25,503 digital histology images in the GTEx collection.</p> <p>GTEx was supported by the NIH Common Fund (2010 – 2019). Additional resources include the<a href="https://gtexportal.org/home/biobank"> GTEx Biobank</a>, the<a href="https://gtexportal.org/home/"> GTEx Portal</a>, and the full dataset at<a href="https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v9.p2"> dbGaP</a> (accession number phs000424).</p> <p>Please refer to the listed GTEx publications below for more details [2-7]. </p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>gtex-idc_v19-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>gtex-idc_v19-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>gtex-idc_v19-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <div>Please acknowledge the GTEx Consortium in any published work that includes the images. A sample statement for the acknowledgment of the Genotype-Tissue Expression (GTEx) Project dataset(s) follows.</div> <p>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (<a href="http://commonfund.nih.gov/GTEx" target="_blank" rel="noopener">commonfund.nih.gov/GTEx</a>). Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI/Leidos Biomedical Research, Inc. subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to the Broad Institute of MIT and Harvard. Biorepository operations were funded through a Leidos Biomedical Research, Inc. subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by Leidos Biomedical Research, Inc. (HHSN261200800001E). The Brain Bank was supported with supplements to University of Miami grant DA006227. Statistical Methods development grants were made to the University of Geneva (MH090941& MH101814), the University of Chicago (MH090951, MH090937, MH101825, & MH101820), the University of North Carolina - Chapel Hill (MH090936), North Carolina State University (MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University (MH101810), and to the University of Pennsylvania (MH101822).</p> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. <em>Radiographics</em> <strong>43,</strong> (2023).</p> <p>[2] Sobin, L., Barcus, M., Branton, P. A., Engel, K. B., Keen, J., Tabor, D., Ardlie, K. G., Greytak, S. R., Roche, N., Luke, B., Vaught, J., Guan, P. & Moore, H. M. Histologic and quality assessment of genotype-Tissue Expression (GTEx) research samples: A large postmortem tissue collection. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0467-OA">10.5858/arpa.2023-0467-OA</a></p> <p>[3] GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).</p> <p>[4] GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).</p> <p>[5] GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).</p> <p>[6] Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., Compton, C. C., DeLuca, D. S., Peter-Demchok, J., Gelfand, E. T., Guan, P., Korzeniewski, G. E., Lockhart, N. C., Rabiner, C. A., Rao, A. K., Robinson, K. L., Roche, N. V., Sawyer, S. J., Segrè, A. V., Shive, C. E., Smith, A. M., Sobin, L. H., Undale, A. H., Valentino, K. M., Vaught, J., Young, T. R., Moore, H. M. & GTEx Consortium. A novel approach to high-quality postmortem tissue procurement: The GTEx project. Biopreserv. Biobank. 13, 311–319 (2015).</p> <p>[7] Branton, P. A., Sobin, L., Barcus, M., Engel, K. B., Greytak, S. R., Guan, P., Vaught, J. & Moore, H. M. Notable histologic findings in a ‘normal’ cohort: The National Institutes of Health Genotype-Tissue Expression (GTEx) project. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0468-OA">10.5858/arpa.2023-0468-OA</a></p>
Gene expression profiling of normothermic machine perfusion of human livers
<p>Normothermic machine perfusion (NMP) has been successfully implemented in clinical routine of liver transplantation over the past years. However, little is known about the mechanisms how NMP impacts on the transcriptome of a human donor liver. We herein examined gene expression profiles in transplanted and non-transplanted livers over NMP time. 50 livers subjected to NMP were included in this study. 30 were transplanted after a maximum of 20 hours (h) perfusion, while 15 were discarded due to poor performance. Biopsies were collected before NP (PRE), 1h, 6h, 12h, 20h of NMP and after reperfusion. Next-generation sequencing was applied in liver biopsies to assess differential gene expression over perfusion time. Perfusate samples were collected regularly to monitor liver function. Significantly differentially expressed genes between each timepoint and PRE (LIVER_NMP_TIMEPOINT_PRE_DEG.xlsx) as well as between non-transplanted livers (NTP) and transplanted livers (TP) (LIVER_NMP_NTP_TP_DEG.xlsx) were determined using the R package DESeq2 and included in this dataset.</p>
Single cell RNA-seq data from: Differentiation signals induce APOBEC3A expression via GRHL3 in squamous epithelia and squamous cell carcinoma
<p>Seurat object for single cell RNA-seq of 10 head and neck squamous cell carcinoma patients, epithelial cells only. From "Differentiation signals induce APOBEC3A expression via GRHL3 in squamous epithelia and squamous cell carcinoma ". <span><span>Two APOBEC (apolipoprotein-B mRNA editing enzyme catalytic polypeptide-like) </span><span>DNA </span><span>cytosine deaminase enzymes (APOBEC3A and APOBEC3B) generate somatic mutations in cancer, driving tumour development and drug resistance. Here we used single cell RNA sequencing to study </span></span><span><span>APOBEC3A</span></span><span><span> and </span></span><span><span>AP</span><span>OB</span><span>EC3B</span></span><span><span> expression in healthy and malignant mucosal epithelia, </span><span>validating</span> <span>key</span><span> observations </span><span>with</span><span> immunohistochemistry, spatial </span><span>transcriptomics</span><span> and functional experiments. Wh</span><span>ereas</span> </span><span><span>APOBEC3B</span></span><span><span> is expressed in keratinocytes entering mitosis, we show that </span></span><span><span>APOBEC3A</span></span><span><span> expression is confined</span> <span>largely</span><span> to</span><span> terminally differentiating cells</span><span> and </span><span>requires </span><span>Grainyhead</span><span>-like transcription factor 3 (GRHL3). T</span><span>hus</span><span>, in normal tissue,</span><span> neither </span><span>deaminase</span> <span>appears to be</span><span> expressed at </span><span>high levels</span><span> during DNA replication, the c</span><span>ell cycle stage</span> <span>associated with</span><span> APOBEC-mediated mutagenesis. </span><span>In</span><span> contrast, we show that in squamous cell carcinoma, there is expansion of </span></span><span><span>GRHL3</span></span><span> <span>expression and </span><span>activity to a subset of cells undergoing DNA replication and concomitant extension of </span></span><span><span>APOBEC3A</span></span><span><span> expression to proliferating cells. </span></span><span><span>These findings </span><span>suggest</span><span> that</span> <span>APOBEC3A</span><span> may play a functional role during keratinocyte differentiation</span><span> and offer</span> </span><span><span>a mechanism for acquisition of APOBEC3A mutagenic activity in tumour</span><span>s</span><span>.</span></span><span> </span></p>
FGF23 Expression Is a Promising Immunohistochemical Diagnostic Marker for Undifferentiated Pleomorphic Sarcoma of Bone (UPSb)
<p>Raw data of the paper "FGF23 Expression Is a Promising Immunohistochemical Diagnostic Marker for Undifferentiated Pleomorphic Sarcoma of Bone (UPSb)" doi: 10.3390/genes15020242</p>
PCOS Combined with Obesity Aggravates the Metabolic and Immune Abnormality in Females Supplement_2_Differentially_expressed_statistics_Metabolomics
<p><span>This table represents the data of Figure 3/4. In this table, we show all the metabolites measured. Meanwhile, we show the anion mode and the cation mode separately, and on this basis, we count the differential metabolites between different combinations of PO/PN/NPN/NPO.<br>Differential metabolites were screened by fold change &gt. 1.5 or less than 0.67.</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.