Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,609
datasets available to search
ShareScore release 0.7.1
Dataset results
6,609 results for “RNA sequencing”
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis</p> <p><br> </p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary:</strong> Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>MDAlab_cDC1_maturation.tar : Docker image used for the analysis</p>
single-nucleus RNA sequencing data from female Aedes aegypti maxillary palp
<p>Single-nucleus RNA sequencing data accompanying Herre*, Goldman* et al. (2022), "Non-Canonical Odor Coding in the Mosquito" (https://doi.org/10.1016/j.cell.2022.07.024)</p> <p>For further analysis see: https://github.com/VosshallLab/Younger_Herre_Vosshall2020/tree/main/snRNAseq_SupplementaryData</p> <p>For raw sequencing files see NCBI BioProject: PRJNA794050</p>
Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"
<p>Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"</p>
Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency
<p class="MsoNormal">Alzheimer's disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify an earlier targetable phenotype. Utilizing a mouse model of AD we found that cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it one of the earlier defects in the evolution of the disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. To delineate potential self-renewal pathways that might contribute to the defect and rescue of Tg-SwDI NPCs and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> NPCs, respectively, we performed single-cell RNA-seq and gene set enrichment analysis (GSEA) on lineage depleted primary FACS-sorted CD31<sup><span>-</span></sup>CD45<sup><span>-</span></sup>Ter119<sup><span>-</span></sup>CD24<sup><span>-</span></sup> NPCs from Tg-SwDI, WT, and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> mice at 3-4 months and 1 year of age. Using the GSEA Hallmark gene sets, we found only three gene sets that were enriched in Tg-SwDI mice over WT mice and rescued in the Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>mice at both ages: TGF-ß pathway, oxidative phosphorylation, and Myc Targets. The TGF-ß pathway consistently had the highest normalized enrichment score in pairwise comparisons between Tg-SwDI vs WT and Tg-SwDI vs Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>of the three rescued pathways. These data suggest that USP16 may regulate neural precursor cell function in part through the BMP pathway.</p>
RNA modification detection using direct RNA sequencing and nanoDoc2
<p><strong>The core of nanoDoc2 includes a machine-learning algorithm in which a 6-mer segmented raw current signal is compared by Deep-One-Class classification using a Wavenet-based neural network. As an output, an RNA modification is detected by a statistical score in each candidate position. Herein, we describe the detailed instructions on how to use nanoDoc2 for signal segmentation, train/test the neural network and finally predict RNA modifications present in nanopore direct RNA sequence data.</strong></p>
Discovering molecular regulators of ageing using mixture models with RNA-sequencing data
<p>Identifying the molecular regulators that control ageing is challenging because the ageing process is influenced by a combination of genetic and environmental factors which makes it difficult to source the contribution of a single gene. Multiple studies have demonstrated that as humans age, increased gene expression heterogeneity results in the dysregulation of key regulators and pathways. Given the dynamic nature of gene expression, it is vital that this data be modelled by statistical approaches that can appropriately account for changes in variability to understand the contribution of heterogeneity during the aging process and properly identify its regulators. This study demonstrates the utility of using mixture models to model biological variability of gene expression occurring during ageing and how novel potential regulators of ageing can be identified.</p> <p>Our mixture modelling approach was applied to gene expression data from the Genotype-Tissue Expression (GTEx) cohort. For every gene, the expression profile was modelled using a mixture model across the cohort where the subset of donors corresponding to each mode was tested for a significant change in age group. The multi-tissue aspect of GTEx was leveraged to find ageing regulators based on this mixture model approach genes that were common across multiple tissues, suggesting that the regulation of ageing may also be controlled through a set of genes that have non-tissue-specific activity.</p> <p>Our approach identified well-documented ageing regulators <em>mTOR </em>and <em>RICTOR</em> and other potential ageing regulators such as <em>IL4</em> and <em>GPR4</em> which were detected only by our approach. Genes identified by edgeR, DESeq2 and the mixture model-based approach were enriched for similar biological pathways. This suggests that while the specific ageing regulators identified from our approach may be distinct, they generally belong in the same pathways as the genes identified by standard approaches. Overall, these results indicate that modelling gene expression variability using mixture models in conjunction with standard differential gene expression can help uncover new regulators that have a potential role for understanding human ageing.</p> <p>I</p>
Script and single cell RNA sequencing datasets of Biomphalaria glabrata hemocyte
<p>The results of the gene and cell barcode counts (feature-barcode matrices) are available in the file "filtered_feature_bc_matrix_Naive". These data have been processed by cellRanger v3.1.0 and can be used with the script "scRNAseq_Biomphalaria_naive" which gathers all the analyses done for publication.</p> <p> </p>
Supplementary Data for "The shaky foundations of simulating single-cell RNA sequencing data"
<p>Supplementary Data for "The shaky foundations of simulating single-cell RNA sequencing data"</p> <p>See description.txt, Supplementary Text, Methods and https://github.com/HelenaLC/simulation-comparison for further description of the files available here.</p>
Simulated data from: Reference-free assembly of long-read transcriptome sequencing data with RNA-Bloom2
<p><span>Long-read sequencing technologies have improved significantly since their emergence. Their read lengths, potentially spanning entire transcripts, is advantageous for reconstructing transcriptomes. Existing long-read transcriptome assembly methods are primarily reference-based and to date, there is little focus on reference-free transcriptome assembly. We introduce RNA-Bloom2, a reference-free assembly method for long-read transcriptome sequencing data. </span>RNA-Bloom2 is available on GitHub at: <a href="https://github.com/bcgsc/RNA-Bloom">https://github.com/bcgsc/RNA-Bloom</a>.</p> <p><span>We benchmarked the assembly quality and the computational performance of RNA-Bloom2 on simulated data. We prepared two mouse simulated datasets with Trans-NanoSim</span><span> for the cDNA and dRNA sequencing protocols model on experimental ONT data</span><span>. The datasets were simulated </span><span>based on the mouse ENSEMBL annotation for GRCm39.</span><span> To investigate the effect of sequencing depth, we subsampled each dataset to 2, 10, and 18 million reads, resulting in a total of six sets of reads for our benchmarking experiments. Using the simulated data, w</span><span>e showed that the transcriptome assembly quality of RNA-Bloom2 is competitive to those of reference-based methods.</span></p>
Exploring cell diversity and fidelity in crustacean limb regeneration using single-nucleus RNA sequencing
<p>R objects containing datasets generated by snRNA-seq on Parhyale hawaiensis limbs. These datasets have been generated during my Phd thesis.</p>
Datasets accompanying "Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA-sequencing"
<p>Datasets include two seurat objects: one with all unfiltered cells and raw expression data (seu_unfiltered.rds) and one dataset with normalised expression and latest annotations (differentiation_object_latest.rds). Additionally, a single-cell object containing post-(py)SCENIC analysis is added (ipsc_scenic.h5ad).</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>
Illumina RNA-Sequencing fastq data from insecticide resistant Anopheles gambiae s.l
<p>This is a dataset of Illumina RNA sequencing reads, for a project investigating resistance to Pirimiphos-methyl in the major malaria vectors, Anopheles gambiae and Anopheles coluzzii. There are four biological replicates for the following conditions:</p> <p> </p> <p>Ngousso (susceptible)</p> <p>Kisumu (susceptible)</p> <p>Bouake gambiae unexposed</p> <p>Bouake gambiae PM survivors</p> <p>Bouake coluzzii unexposed </p> <p>Bouake coluzzii PM survivors </p> <p> </p> <p>SRA submission: SUB14596876</p> <p> </p>
single-nucleus RNA sequencing data of Minimal Change Disease and Focal Segmental Glomerulosclerosis patients
<p>single-nucleus RNA sequencing data of Minimal Change Disease and Focal Segmental Glomerulosclerosis patients</p>
Data for 'Comparative Analysis of Single-Cell RNA Sequencing Methods'
<p>Raw sequencing data to "Comparative Analysis of Single-Cell RNA Sequencing Methods". </p> <p>https://www.ncbi.nlm.nih.gov/pubmed/28212749</p> <p> </p> <p>In addition to the GEO submission https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790, you can find here raw bam files for UMI-methods tagged with cell barcode and UMI sequences.</p> <p>MD5 checksum: f10825509952fffd9c4dc0c1dcb9eb8e</p>
Chronic social defeat stress induces meningeal neutrophilia via type I interferon signaling: single cell RNA sequencing data
<p>Meningeal single cell RNA sequencing data</p> <p>Meningeal samples were collected from both dorsal and ventral skull, avoiding inclusion of choroid plexus. Samples were digested in 2.5 mg/mL Collagenase D (Cat. #11088858001; Roche) and 12.5 μL of 0.5 mg/mL DNAseI (Cat. #L5002139; Worthington), put on a shaker at 370C for 30 m, diluted with cold HBSS + 0.1% BSA, and mashed through a 70 μm cell strainer prior to sorting.</p> <p>Data represent live, nucleated, singlet cells (DAPI-DRAQ5+) sorted on a BD FACS Aria Fusion into HBSS + 10% FBS prior to droplet encapsulation using 10x Genomics’ Drop-seq platform (Chromium v2).</p> <p>10X chip lane is indicate by 'group' column</p> <p>Group 1 = 4 pooled homecage control (unstressed) mice</p> <p>Group 2 = 4 pooled homecage control (unstressed) mice</p> <p>Group 3 = 4 pooled mice exposed to chronic social defeat for 14 days; tissue was collected 2 hours following final defeat</p> <p>See the following repositories for data processing:</p> <p><a href="https://github.com/maryellenlynall/2019_bcell_stress/blob/master/bcellstress20.Rmd">https://github.com/maryellenlynall/2019_bcell_stress/</a> (processing from raw files starts at bcellstress020.Rmd)</p> <p><a href="https://github.com/staceykigar/meningeal_neut/">https://github.com/staceykigar/meningeal_neut/</a></p> <p>We also provide a processed dataset (processed.RData) with assays 'counts' and 'logcounts' which is the processed single cell object saved at line "# Save object for upload to Zenodo" in script <a href="https://github.com/staceykigar/meningeal_neut/">https://github.com/staceykigar/meningeal_neut/</a>neutrophilstress01.Rmd </p> <p>Cluster annotations are in sce$Annotation</p> <p>Neutrophil subcluster annotations are in sce$Subcluster</p> <p>Sample condition is in sce$cond, where "HC" indicates homecage control and "SD" indicates chronic social defeat</p> <p>10X chip lane is in sce$group</p>
Fig. 4 in Some Unusual Small-Subunit Ribosomal RNA Sequences of Metazoans
Fig. 4. Variable region (V7) of the 18S rRNA locus of 17 species of centipedes.
Deep sequencing datasets from: Witnessing the structural evolution of an RNA enzyme
<p>An RNA polymerase ribozyme that has been the subject of extensive directed evolution efforts has attained the ability to synthesize complex functional RNAs, including a full-length copy of its own evolutionary ancestor. During the course of evolution, the catalytic core of the ribozyme has undergone a major structural rearrangement, resulting in a novel tertiary structural element that lies in close proximity to the active site. Through a combination of site-directed mutagenesis, structural probing, and deep sequencing analysis, the trajectory of evolution was seen to involve the progressive stabilization of the new structure, which provides the basis for improved catalytic activity of the ribozyme. Multiple paths to the new structure were explored by the evolving population, converging upon a common solution. Tertiary structural remodeling of RNA is known to occur in nature, as evidenced by the phylogenetic analysis of extant organisms, but this type of structural innovation had not previously been observed in an experimental setting. Despite prior speculation that the catalytic core of the ribozyme had become trapped in a narrow local fitness optimum, the evolving population has broken through to a new fitness locale, raising the possibility that further improvement of polymerase activity may be achievable.</p> <p> </p>
B-other ALL classification by Targeted RNA-sequencing
<p>We present a comprehensive genetic study of 144 pediatric B-other Acute Lymphoblastic Leukemia cases diagnosed and treated at Boldrini Children's Hospital (Brazil). We performed a targeted RNA-sequencing to evaluated the benefits of introducing genomic technologies into routine diagnostics. Targeted RNA-sequencing further classified 66.7% B-other cases. All 'classical' and novel ALL subgroups, except for iAMP21, hyper- and hypodiploid cases were identified. In addition, clinically important genetic alterations as druggable lesions and prognostic factors were found. Here, we uploaded files that contain: </p> <p>1. Gene expression data from the entire cohort studied, n=184 (Log2+1 TPM normalized gene expression data). Tab-delimited file (.csv) that contains a row for gene, a column for each sample, and expression values for each gene in each sample. Annotation labels are in the first three rows and in the first column.</p> <p>2. A subset of 23 BAM (Binary Alignment/Map) files for representative ALL cases, including BCR-ABL1, ETV6-RUNX1, TCF3-PBX1, KMT2A-r, High-Hyperdiploidy; DUX4-r, iAMP21, PAX5-driven, ABL-class fusion, JAK2-fusion, EPOR-fusion, CRLF2-high, ZNF384-r, MEF2D-r, NUTM1-r, B-'rest', IKZF1del, and ERGdel.</p> <p>3- Genetic information of the 23 BAM files provided (tab-delimited file, .txt). </p>
ScienceDex guides
Understand access before you commit
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.