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25,372 results for “Transcriptomics”
Transcriptomic analyses of normal-appearing CNS white matter from multiple sclerosis donors reveal subtype-specific molecular signatures of disease (REVISED)
<p>Datasets of bulk RNA-sequencing of NAWM from MS donors + supplementary images of RNA deconvolution of cell trajectories</p>
Transcriptomic profiles of resected pancreatic adenocarcinoma, whole-slide match
<p>RNA was extracted from the whole-slide tumor regions of 100 pancreatic adenocarcinomas, consecutively resected at the Beaujon hospital (Clichy, FRANCE). Tumors were sequenced in two batches, using 3' RNA-sequencing for FFPE compatibility.</p>
CrusTome: A transcriptome database resource for large-scale analyses across Crustacea
<p>CrusTome_v0.1.0 Prerelease<br> /ReadMe - this file<br> /crustome_aa_BLAST.tar.gz - CrusTome database of amino acid sequences in BLAST format<br> /crustome_aa_DIAMOND.tar.gz - CrusTome database of amino acid sequences in DIAMOND format<br> /crustome_mrna_BLAST.tar.gz - CrusTome database of mRNA sequences in BLAST format<br> /dict - Dictionary file to translate species IDs. For usage with sed/awk see link to Github site below<br> <br> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *<br> * <br> * Please note, most of the data files contained in this DOI are <br> * compressed into GZip files (.gz extension). <br> * Mac and Linux OS's can extract this file type natively. <br> * Windows OS requires software to extract the archive. 7-Zip <br> * (http://www.7-zip.org) is free and open source software that will <br> * allow windows PCs to open and decompress the archive. <br> * <br> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *<br> <br> <strong><em>Pérez-Moreno JL, Kozma MT, DeLeo DM, Bracken-Grissom HD, Durica DS, Mykles DL. 2023. CrusTome: A transcriptome database resource for large-scale analyses across Crustacea. G3: Genes, Genomes, Genetics.</em></strong></p> <p><strong>CrusTome: A transcriptome database resource for large-scale analyses across Crustacea</strong></p> <p>Transcriptomes from non-traditional model organisms often harbor a wealth of unexplored data. Examining these datasets can lead <br> to clarity and novel insights in traditional systems, as well as to discoveries across a multitude of fields. Despite significant <br> advances in DNA sequencing technologies and in their adoption, access to genomic and transcriptomic resources for non-traditional <br> model organisms remains limited. Crustaceans, for example, being amongst the most numerous, diverse, and widely distributed taxa on the planet, often serve as excellent systems to address ecological, evolutionary, and organismal questions. While they are <br> ubiquitously present across environments, and of economic and food security importance, they remain severely underrepresented in <br> publicly available sequence databases. Here, we present CrusTome, a multi-species, multi-tissue, transcriptome database of 201 <br> assembled mRNA transcriptomes (189 crustaceans, 30 of which were previously unpublished, and 12 ecdysozoan outgroups) as an evolving, and publicly available resource. This database is suitable for evolutionary, ecological, and functional studies that employ<br> genomic/transcriptomic techniques and datasets. CrusTome is presented in BLAST and DIAMOND formats, providing robust datasets for sequence similarity searches, orthology assignments, phylogenetic inference, etc., and thus allowing for straight-forward incorporation into existing custom pipelines for high-throughput analyses.</p> <p> <br> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *<br> <br> For questions regarding released datasets contact:<br> Corresponding Author: Jorge L. Perez-Moreno (Colorado State University)<br> jorgepm@colostate.edu / jpere645@fiu.edu<br> <br> <strong> https://github.com/invertome/crustome</strong></p> <p> </p> <p> <br> <strong>PLEASE CITE:</strong></p> <p>Pérez-Moreno JL, Kozma MT, DeLeo DM, Bracken-Grissom HD, Durica DS, Mykles DL. 2023. CrusTome: A transcriptome database resource for large-scale analyses across Crustacea. G3: Genes, Genomes, Genetics.</p> <p> </p> <p><strong>Funder Information</strong></p> <p>Supported by National Science Foundation grants to DLM (IOS-1922701) and DSD (IOS-1922755). In addition, this work was partially funded by two grants awarded from the National Science Foundation: Doctoral Dissertation Improvement Grant (#1701835) awarded to JPM and HBG and the Division of Environmental Biology Bioluminescence and Vision grant (DEB-1556059) awarded to HBG. Samples in the FICC were collected by grants from The Gulf of Mexico Research Initiative (GOMRI), Florida Institute of Oceanography Shiptime Funding awarded to HBG and DMD; the National Science Foundation Division of Environmental Biology Grant 1556059 awarded to HBG; and the National Oceanic and Atmospheric Administration Ocean Exploration Research (NOAA-OER 2015) grant awarded to HBG.</p>
(Annotation and metric) Thalassiosirales reference transcriptomes
<p>Here are deposited the public data recompiled for the construction of a Thalassiosirales reference database as part of a Ph.D Thesis "TEMPERATURE ACCLIMATION CAPACITY AND COLD-ADAPTATION MECHANIMS IN THALASSIOSIRALES ANTARCTIC MEMBERS". Data correspond to the annotation of 53 transcriptomes from the MMETSP of Thalassiosirales members.</p>
Transcriptome assemblies of three diatom and three prymnesiophyte isolates from Station ALOHA and Kaneohe Bay
<p><strong>Culture ID/name</strong></p> <p>AT125A – Pseudo-nitzschia sp.</p> <p>AT125C – Pseudo-nitzschia sp.</p> <p>ATCH2 – Chaetoceros sp.</p> <p>Pn B2 – Pseudo-nitzschia sp.</p> <p>KB-HA01 – Chrysochromulina sp. (also called </p> <p>AL-TEMP-12 – Chrysochromulina sp. (also called </p> <p>NF-H275 – Chrysochromulina sp.<br> <br> </p> <p><strong>Growth Conditions</strong></p> <p>All cultures were grown at 27°C, 12:12 light:dark cycle, and with a light intensity of 100 µmol photons m<sup>-2</sup> sec<sup>-1</sup>. AT125C, AT125A, and Pn B2 were grown with Aquil media. ATCH2 was grown with F/20 media with the phosphate concentration modified to a final concentration of 0.5µM. KB-HA01 was grown with F/2 media and AL-TEMP-12 and NF-H275 were grown with K media. None of the cultures were axenic. All cultures were filtered in “light” and “dark” conditions and were in exponential phase when filtered. (Filter types and volumes filtered listed below.) After filtration, all filters were placed into 2mL screwcap tubes, flash frozen with liquid nitrogen, and stored at -80°C.</p> <p><strong>Growth Conditions</strong></p> <p>All cultures were grown at 27°C, 12:12 light:dark cycle, and with a light intensity of 100 µmol photons m<sup>-2</sup> sec<sup>-1</sup>. AT125C, AT125A, and Pn B2 were grown with Aquil media. ATCH2 was grown with F/20 media with the phosphate concentration modified to a final concentration of 0.5µM. KB-HA01 was grown with F/2 media and AL-TEMP-12 and NF-H275 were grown with K media. None of the cultures were axenic. All cultures were filtered in “light” and “dark” conditions and were in exponential phase when filtered. (Filter types and volumes filtered listed below.) After filtration, all filters were placed into 2mL screwcap tubes, flash frozen with liquid nitrogen, and stored at -80°C.<br> <br> [TRANSCRIPTOME SEQUENCING]<br> <br> [QC AND ASSEMBLY]<br> <br> [POST-ASSEMBLY PROCESSING]<br> Diamond v2.0.5.143 was used to blast (e-value: 1e-5) to a cross-kingdom reference sequence database (as described in Coesel et al., 2021). Diamond v2.0.5.143 was used to find the least common ancestor of each contig based upon the blast results. Contigs that were identified as bacteria, archaea, or viruses were excluded from the assemblies.<br> <br> </p>
Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
<p>Data required to reproduce the results/figures of the "<strong>Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors</strong>" project. </p>
Gecarcinus lateralis - Comprehensive Multi-Tissue Transcriptome
<p><strong>Blackback land crab (<em>Gecarcinus lateralis</em>) comprehensive multi-tissue transcriptomic assemblies (nucleotide and amino acid) in FASTA format.</strong></p> <p><em>De novo</em> assembled using a multi-assembler approach according to Perez-Moreno (et al., 2023) from ~ 1 billion Illumina RNAseq reads and a PacBio HiFi sequencing run from polyA selected libraries of the following tissues:</p> <p>Y-Organ</p> <p>Eyestalk Ganglia</p> <p>Brain</p> <p>Hepatopancreas</p> <p>Heart</p> <p>Gill</p> <p>Hindgut</p> <p>Midgut</p> <p> </p> <p> </p> <p>References:</p> <p>Jorge L Pérez-Moreno, Mihika T Kozma, Danielle M DeLeo, Heather D Bracken-Grissom, David S Durica, Donald L Mykles, CrusTome: <em>A transcriptome database resource for large-scale analyses across Crustacea</em>, <em>G3 Genes|Genomes|Genetics</em>, 2023;, jkad098, <a href="https://doi.org/10.1093/g3journal/jkad098">https://doi.org/10.1093/g3journal/jkad098</a></p>
The First Transcriptomic Atlas of the Adult Lacrimal Gland Reveals Epithelial Complexity and Identifies Novel Progenitor Cells in Mice
<p>This project contains the R objects and code to reproduce the analyses and figures presented in the research article:</p> <p>'The First Transcriptomic Atlas of the Adult Lacrimal Gland Reveals Epithelial Complexity and Identifies Novel Progenitor Cells in Mice.' <em>Cells</em> <strong>2023</strong>, <em>12</em>, 1435. https://doi.org/10.3390/cells12101435</p> <p>Raw data (FASTQ files and CellRanger output files used for the preprocessing of individual datasets) can be found on Gene Expression Omnibus database (www.ncbi.nlm.nih.gov/geo/) under accession # GSE232146.</p>
Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics.
<p>This dataset contains a single zipped folder containing:</p> <ul> <li> <p>data</p> </li> <li> <p>scripts</p> </li> </ul> <p>needed to reproduce the manuscript entitled "Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics". Each folder is organized by tissue type, methodology, and analysis. A readme file accompanies each folder with details on the files/scripts within that folder. Alongside the paper and supplementary materials, it should be possible to reproduce all the figures in the manuscript.</p>
Supplementary datasets: sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data (Ng et al.)
<p>This repository contains data files from the manuscript Ng et al. "sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data".</p> <p><strong>Directories</strong></p> <p>Please untar the sciCSR-data-files.tar.gz archive.</p> <p><em><strong>Folder "Simulated_data"</strong></em></p> <ul> <li>'simulated_IGHC_reads' folder: containing list of simulated data (FASTQ sequence files and aligned BAM files) to test the accuracy of commonly used RNA-seq aligners (STAR, HISAT2) to distinguish sterile and productive heavy-chain transcripts. The code to generate these data is in the repository https://github.com/Fraternalilab/sciCSR-analysis.</li> <li>'simulated_transitions.RData': .RData file containing list of Seurat objects of simulated datasets of different number of cells, to test the robustness of sciCSR-inferred transitions across different dataset sizes.</li> </ul> <p><em><strong>Folder "Seurat_objects"</strong></em></p> <ul> <li>'human_Bcells_atlas_IGHC_NMF_rank.rds': Nonnegative matrix factorization (NMF) results to derive isotype signatures from the human B cell atlas (see below).</li> <li>'mouse_Bcells_atlas_IGHC_NMF_rank.rds': NMF results to derive isotype signatures from the mouse B cell atlas (see below)</li> <li>'Human_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'human B cell atlas' (i.e. merging data from Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) and King et al (https://doi.org/10.1101/2020.04.28.054775))</li> <li>'mouse_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'mouse B cell atlas' (i.e. merging data from Mathew et al (https://doi.org/10.1016/j.celrep.2021.109286) and Luo et al (https://doi.org/10.1186/s13578-022-00795-6))</li> <li>'Stewart_HumanPeripheral_Bcells_IGHC.rds': Seurat object containing cells from the Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) peripheral blood B cell atlas.</li> <li>* 'King_HumanTonsil_Bcells_IGHC.rds': Seurat object containing cells from the King et al. (https://doi.org/10.1101/2020.04.28.054775) human tonsilar B cell atlas.</li> <li>'Kim_Covid_Bcells_IGHC.rds': Seurat object containing cells from the Kim et al. (https://doi.org/10.1038/s41586-022-04527-1) time-course scRNA-seq data on human B cell response to SARS-CoV-2 vaccine.</li> <li>'Gomez_AID_VDJ_IGHC.rds': Seurat object containing cells from the Gómez-Escolar et al. (https://doi.org/10.15252/embr.202255000) Aicda mouse knockout scRNA-seq data.</li> <li>'Hong_IL23_Bcells_IGHC.rds': Seurat object containing cells from the Hong et al. (https://doi.org/10.4049/jimmunol.2000280) Il23 p19 mouse knockout scRNA-seq data.</li> <li>'scIFNg.rds': Seurat object containing scRNA-seq data of time-course in vitro culture of B cells stimulated with interferon gamma generated in this work.</li> </ul>
Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures
<p>Processed data associated with the manuscript DOI: <a href="https://doi.org/10.1016/j.xgen.2023.100421" target="_blank" rel="noopener">10.1016/j.xgen.2023.100421 </a></p> <p>Analysis code on GitHub: <a href="../doi/10.5281/zenodo.8253917" target="_blank" rel="noopener">10.5281/zenodo.8253917</a></p> <p> </p>
Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation"
<p>Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation". All nuclei passed QC and were corrected for background noise using cellBender. Files are in R objects saved in RDS (R Data Serialization) format. This repo contains one Seurat v4 object and one gene-by-cell raw RNA count matrix in sparse matrix format (dgCMatrix).</p>
PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes
<p>Supplementary Data for "PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes"</p> <p> </p>
Granulosa cell transcriptome data from four different time points before and up to 48 hours after PMSG induction to GTH-depend phase in mice
<p>GTH-dependent follicle development begins with small antrum follicles and ends with preovulatory follicles. GTH-dependent follicle development is mainly controlled by gonadotropins, and the development time is 48h. The purpose of this study is to monitor the changes in gene expression of granulosa cells at four different time points during the GTH-dependent phase to increase our understanding of human GTH-dependent follicle development.Granulosa cell mRNA profiles of GTH-depend phase in mice.There are 12 samples represented four time points 0h (n=3), 12h (n=3), 24h (n=3) and 48h (n=3)</p>
Ocean acidification induces distinct transcriptomic responses across life history stages of the sea urchin Heliocidaris erythrogramma
Ocean acidification (OA) from seawater uptake of rising carbon dioxide emissions impairs development in marine invertebrates, particularly in calcifying species. Plasticity in gene expression is thought to mediate many of these physiological effects, but how these responses change across life history stages remains unclear. The abbreviated lecithotrophic development of the sea urchin <i>Heliocidaris erythrogramma</i> provides a valuable opportunity to analyze gene expression responses across a wide range of life history stages, including the benthic, post-metamorphic juvenile. We measured the transcriptional response to OA in <i>H. erythrogramma</i> at three stages of the life cycle (embryo, larva, and juvenile) in a controlled breeding design. The results reveal a broad range of strikingly stage-specific impacts of OA on transcription, including changes in the number and identity of affected genes; the magnitude, sign, and variance of their expression response; and the developmental trajectory of expression. The impact of OA on transcription was notably modest in relation to gene expression changes during unperturbed development and dwarfed by genetic contributions from parentage. The latter result suggests that natural populations may provide an extensive genetic reservoir of resilience to OA. Taken together, these results highlight the complexity of the molecular response to OA, its substantial life history stage specificity, and the importance of contextualizing the transcriptional response to pH stress in light of normal development and standing genetic variation to better understand the capacity for marine invertebrates to adapt to OA.
Investigating the Effect of Positional Variation on Mid-Lactation Mammary Gland Transcriptomics in Mice Fed Either a Low-Fat or High-Fat Diet
<p>Investigating the Effect of Positional Variation on Mid-Lactation Mammary Gland Transcriptomics in Mice Fed Either a Low-Fat or High-Fat Diet.</p>
Genomic and transcriptomic data for the frog Platyplectrum ornatum
<p>The diversity of genome sizes across the tree of life is of key interest in evolutionary biology. Various correlates of variation in genome size, such as accumulation of transposable elements or rate of DNA gain and loss, are well known, but the underlying molecular mechanisms that drive or constrain genome size are poorly understood. Here we study one of the smallest genomes among frogs characterized thus far, that of the ornate burrowing frog<b> (</b><i>Platyplectrum ornatum</i>) from Australia, and compare it to other published frog and vertebrate genomes to examine the forces driving reduction in genome size. At ~1.06 Gb, the <i>P. ornatum </i>genome is like that of birds, revealing four major mechanisms underlying TE dynamics: reduced abundance of all major classes of transposable elements (TEs); increased net deletion bias in TEs; drastic reduction in the lengths of introns; and expansion via gene duplication of the repertoire of TE-suppressing Piwi genes, accompanied by increased expression of piRNA-based TE-silencing pathway genes in germline cells. Transcriptome data from multiple tissues in both sexes corroborate these results and provide insight into sex-differentiation pathways in <i>Platyplectrum</i>. Genome skimming of two closely related frog species (<i>Lechriodus fletcheri </i>and <i>Limnodynastes fletcheri</i>) confirms a reduction in TEs as a major driver of genome reduction in <i>Platyplectrum</i> and supports a macroevolutionary scenario of small genome size in frogs driven by convergence in life history, especially rapid tadpole development and tadpole diet. The <i>P. ornatum</i> genome offers a model for future comparative studies on mechanisms of genome size reduction in amphibians and in vertebrates generally.</p>
Data from: A phylogenomic approach to clarifying the relationship of Mesodinium within the Ciliophora: a case study in the complexity of mixed-species transcriptome analyses
<p>Recent high-throughput sequencing endeavors have yielded multi-gene/protein phylogenies that confidently resolve several inter- and intra-class relationships within the phylum Ciliophora. We leverage the massive sequencing efforts from the Marine Microbial Eukaryote Transcriptome Sequencing Project, other SRA submissions, and available genome data with our own sequencing efforts to determine the phylogenetic position of <i>Mesodinium</i> and to generate the most taxonomically-rich phylogenomic ciliate tree to date. Regardless of the data mining strategy, the multi-protein dataset, or the molecular models of evolution employed, we consistently recovered the same well-supported relationships among ciliate classes, confirming many of the higher-level relationships previously identified. <i>Mesodinium</i> always formed a monophyletic group with members of the Litostomatea, with mixotrophic species of <i>Mesodinium</i> – <i>M. rubrum</i>, <i>M. major</i>, and <i>M. chamaeleon</i> - being more closely related to each other than to the heterotrophic member, <i>M. pulex</i>. The well-supported position of <i>Mesodinium</i> as sister to other litostomes contrasts with previous molecular analyses including those from phylogenomic studies that exploited the same transcriptomic databases. These topological discrepancies illustrate the need for caution when mining mixed-species transcriptomes and indicate that identifying ciliate sequences among prey contamination - particularly for <i>Mesodinium</i> species where expression from stolen prey nuclei appears to dominate – requires thorough and iterative vetting with phylogenies that incorporate sequences from a large outgroup of prey.</p>
Data from: A phylogenomic approach to clarifying the relationship of Mesodinium within the Ciliophora: a case study in the complexity of mixed-species transcriptome analyses
<p>Recent high-throughput sequencing endeavors have yielded multi-gene/protein phylogenies that confidently resolve several inter- and intra-class relationships within the phylum Ciliophora. We leverage the massive sequencing efforts from the Marine Microbial Eukaryote Transcriptome Sequencing Project, other SRA submissions, and available genome data with our own sequencing efforts to determine the phylogenetic position of <i>Mesodinium</i> and to generate the most taxonomically-rich phylogenomic ciliate tree to date. Regardless of the data mining strategy, the multi-protein dataset, or the molecular models of evolution employed, we consistently recovered the same well-supported relationships among ciliate classes, confirming many of the higher-level relationships previously identified. <i>Mesodinium</i> always formed a monophyletic group with members of the Litostomatea, with mixotrophic species of <i>Mesodinium</i> – <i>M. rubrum</i>, <i>M. major</i>, and <i>M. chamaeleon</i> - being more closely related to each other than to the heterotrophic member, <i>M. pulex</i>. The well-supported position of <i>Mesodinium</i> as sister to other litostomes contrasts with previous molecular analyses including those from phylogenomic studies that exploited the same transcriptomic databases. These topological discrepancies illustrate the need for caution when mining mixed-species transcriptomes and indicate that identifying ciliate sequences among prey contamination - particularly for <i>Mesodinium</i> species where expression from stolen prey nuclei appears to dominate – requires thorough and iterative vetting with phylogenies that incorporate sequences from a large outgroup of prey.</p>
Data from "Corset: enabling differential gene expression analysis for de novo assembled transcriptomes"
<p>This dataset contains de novo transcriptome assemblies for three publicly available RNA-seq dataset (SRA055442, SRR453566-SRR453571 and GSE37704 ). For each assembly we also provide a table with the read counts per contig, the output from corset (clusters and counts), and the results from a genome-based analysis. This dataset was used to assess the performance of the corset software. More detail is provided in the paper: Nadia M Davidson and Alicia Oshlack,<strong> </strong>Corset: enabling differential gene expression analysis for de novo assembled transcriptomes, <em>Genome Biology</em> 2014, <strong>15</strong>:410. http://genomebiology.com/2014/15/7/410/abstract</p>
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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.