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21,239 results for “RNA-Seq”
AsaruSim: a single-cell and spatial RNA-Seq Nanopore long-reads simulation workflow
Open the record for dataset details and reuse information.
SQUID: Transcriptomic Structural Variation Detection from RNA-seq -- simulation data part 3
<p>Simulation data part 3 for SQUID software.</p>
SQUID: Transcriptomic Structural Variation Detection from RNA-seq -- simulation data part 2
<p>Simulation data part 2 for SQUID software.</p>
SQUID: Transcriptomic Structural Variation Detection from RNA-seq -- simulation data part 1
<p>Simulation data part 1 for SQUID software.</p>
Example alignment for CRUK-CI RNA-seq course
<p>An alignment file with indices used in our RNA-seq course.<br> It is aligned to chr 1 of mm10 reference genome</p>
Count RNA-seq data used for benchmarking FDR control methods
<p>This submission contains three DESeqDataSet objects with RNA-seq count data.<br> <br> The files "rnaseq-brain-counts.rds" and "gsea-human-counts.rds" contain subsets of the GTEx data. These samples were downloaded using the Short Read Archive Toolkit and mapped to the human reference genome version GRCh38 using STAR v2.4.2a. htseq-count was used to tabulate the number of uniquely mapping reads for each gene. We used the DESeq2 format the data into DESeqDataSet objects. File "rnaseq-brain-counts.rds" contains 20 samples from two regions of the human basal ganglia, the nucleus accumbens and the putamen. FIle "gsea-human-counts.rds" contains 10 samples (6 cerebellum and 4 cortex samples). <br> <br> The file "gsea-human-counts.rds" contains RNA-seq count data from mouse hematopoietic stem cells and multipotent progrenitors (Cabezas-Wallscheid et al. Cell Stem Cell, 2014). The raw fastq data is available through the ArrayExpress database (<a href="http://www.ebi.ac.uk/arrayexpress">http://www.ebi.ac.uk/arrayexpress</a>) under accession number E-MTAB-2262. These data were mapped to the mouse reference genome GRCm38 (ENSEMBL release 69) using the Genomic Short-Read Nucleotide Alignment program (version 2012-07-20). We used htseq-count to count the number of reads overlapping with each gene and used the DESeq2 package to format the data as a DESeqDataSet R object.</p>
Processed data - DegNorm: Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis
<p>Processed data from DegNorm:</p> <ul> <li>"_raw.txt": raw read counts matrix;</li> <li>"_DI.txt": Degradation index score matrix;</li> <li>"_DegNorm.txt": normalized read counts matrix from DegNorm output;</li> <li>"_coverage.Rdata": list of coverage matrix for the sample;</li> <li>"_countsTIN.txt": TIN normalized counts.</li> </ul> <p> </p>
Test data for running snakePipes : RNA-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the RNA-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for mouse (<strong>GRCm38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
FIGURE 8 in The RNA-seq approach to discriminate gene expression profiles in response to Beauveria bassiana and Micrococcus luteus microbial pathogens on Actias selene (Lepidoptera: Saturniidae)
FIGURE 8. Phylogenetic analysis was performed using the MEGA (version 5.0) program based on the four different amino acid sequences from various insects. The phylogenetic tree was constructed using the neighbor-joining algorithm method and bootstrap values (1000 repetitions) of the branches are indicated. The HSP90s from other organisms are: Antheraea pernyi (APX61061.1), Helicoverpa armigera (ATB54999.1), Spodoptera litura (ADK55517.2), Mythimna separata (ATN45250.1), Danaus plexippus plexippus (OWR53421.1), Bombyx mori (NP_001036876.1), Apis mellifera (NP_001153536.1), Plutella xylostella (NP_001296043.1), Drosophila melanogaster (NP_523899.1), Nilaparvata lugens (XP_022202749.1), Bicyclus anynana (XP_023945769.1);The PGRPs from other organisms are: Antheraea pernyi (AME17978.1), Samia ricini (BAF03522.1), Trichoplusia ni (XP_026737257.1), Helicoverpa armigera (AHK59818.1), Antheraea mylitta (ABG72709.1), Manduca sexta (AAO21509.1), Spodoptera litura (XP_022825445.1), Bombyx mori (NP_001036836.1);The Ras homolog genes from other organisms are: Spodoptera litura (XP_022819713.1), Vanessa tameamea (XP_026494920.1), Zootermopsis nevadensis (XP_021927562.1), Drosophila miranda (XP_017145574.1), Drosophila persimilis (XP_002015385.2), Nasonia vitripennis (XP_008209835.1); The MyD88s from other organisms are: Diaphorina citri (NP_001316117.1),Tribolium castaneum (XP_008190563.1), Drosophila melanogaster (NP_610479.1), Nasonia vitripennis (XP_008206894.1), Ceratitis capitata (XP_020716287.1), Bactrocera dorsalis (XP_011205590.1), Bombus terrestris (XP_003394201.1), Bombyx mori (XP_004921572.1), Antheraea pernyi (AHH80650.1).
FIGURE 7 in The RNA-seq approach to discriminate gene expression profiles in response to Beauveria bassiana and Micrococcus luteus microbial pathogens on Actias selene (Lepidoptera: Saturniidae)
FIGURE 7. RT-qPCR analysis of 11 immune-related DEGs in ML vs. PBS group. The y-axis shows relative expression levels (Blue colour represents the PBS control group and yellow colour represents the M. luteus treatment group, * p <0.05, **p <0.01).
FIGURE 6 in The RNA-seq approach to discriminate gene expression profiles in response to Beauveria bassiana and Micrococcus luteus microbial pathogens on Actias selene (Lepidoptera: Saturniidae)
FIGURE 6. RT-qPCR analysis of 19 immune-related DEGs in Bb vs. PBS group. The y-axis shows relative expression levels (Blue colour represents the PBS control group and red colour represents the B. bassiana treatment group, * p <0.05, **p <0.01).
FIGURE 4 in The RNA-seq approach to discriminate gene expression profiles in response to Beauveria bassiana and Micrococcus luteus microbial pathogens on Actias selene (Lepidoptera: Saturniidae)
FIGURE 4. GO classification of annotated differentially expressed genes in Bb vs. PBS group. The DEGs were categorized into 41 functional groups and were summarized in three main categories: biological process, cellular component and molecular function. The X-axis represents the number of genes noted on the GO item, and the Y-axis represents the GO functional classification.
FIGURE 5 in The RNA-seq approach to discriminate gene expression profiles in response to Beauveria bassiana and Micrococcus luteus microbial pathogens on Actias selene (Lepidoptera: Saturniidae)
FIGURE 5. GO classification of annotated differentially expressed genes in ML vs. PBS group. The DEGs were categorized into 41 functional groups and were summarized in three main categories: biological process, cellular component and molecular function. The X-axis represents the number of genes noted on the GO item, and the Y-axis represents the GO functional classification.
FIGURE 3 in The RNA-seq approach to discriminate gene expression profiles in response to Beauveria bassiana and Micrococcus luteus microbial pathogens on Actias selene (Lepidoptera: Saturniidae)
FIGURE 3. KEGG classification map of differentially expressed genes in ML vs. PBS group. The X axis was the number of genes annotated into a certain KEGG Pathway category, and the Y axis was the KEGG Pathway category. Different colors present different metabolic functions.
FIGURE 2 in The RNA-seq approach to discriminate gene expression profiles in response to Beauveria bassiana and Micrococcus luteus microbial pathogens on Actias selene (Lepidoptera: Saturniidae)
FIGURE 2. KEGG classification map of differentially expressed genes in Bb vs. PBS group. The X axis was the number of genes annotated into a certain KEGG Pathway category, and the Y axis was the KEGG Pathway category. Different colors present different metabolic functions.
RNA-Seq data for mice ovaries
<p>Ten days old mice were injected with melatonin to 17 days old, and the ovaries were collected for RNA sequencing. The data were analyzed to study the effects of melatonin on early follicular growth.</p>
Benchmarking Illumina RNA-seq fusion transcript detection methods - simulated 101 base PE reads
<p>Simulated 101 base paired-end reads used for benchmarking Illumina-based fusion detection methods as used in:</p> <p>Haas, B.J., Dobin, A., Li, B. <em>et al.</em> Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. <em>Genome Biol</em> <strong>20</strong>, 213 (2019). https://doi.org/10.1186/s13059-019-1842-9</p> <p> </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>
scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq
<p>Single cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.</p>
MBSeq: Single cell/nuclei DNA- and RNA-seq of LFS Medulloblastoma samples
<p>Processed Data accompanying the manuscript:</p> <p> </p> <p>Multi-omic and single-cell profiling of chromothriptic medulloblastoma reveals genomic and transcriptomic consequences of genome instability</p> <p>Petr Smirnov <em>1,2, Moritz J. Przybilla 2,3,4, Milena Simovic-Lorenz 1, R. Gonzalo Parra 2,3,5, Hana Susak 2,3, Manasi Ratnaparkhe 1, John KL. Wong 6, Verena Körber7, Jan-Philipp Mallm8, George Philippos 1,9, Martin Sill 10,11, Thorsten Kolb 1, Rithu Kumar 1, Nicola Casiraghi 2,3, Konstantin Okonechnikov 10,11, David R. Ghasemi 10,11,12, Kendra Korinna Maaß 10,11,12, Kristian W. Pajtler 10,11,12, Anna Jauch 13, Andrey Korshunov 14, Thomas Höfer 7, Marc Zapatka 6, Stefan M. Pfister 10,11,12, Wolfgang Huber 2, Oliver Stegle</em> #2,3,4, Aurélie Ernst* #1</p> <p>1 Group Genome Instability in Tumors, German Cancer Research Center (DKFZ), and German Cancer Consortium (DKTK), Heidelberg, Germany. </p> <p>2 European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany</p> <p>3 Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany</p> <p>4 Wellcome Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK</p> <p>5 Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain</p> <p>6 Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany</p> <p>7 Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany</p> <p>8 Single-cell Open Lab, German Cancer Research Center (DKFZ) and Bioquant, Heidelberg, Germany</p> <p>9 Faculty of Biosciences, Heidelberg University, Heidelberg, Germany</p> <p>10 Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany. </p> <p>11 Division of Pediatric Neuro-oncology, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany. </p> <p>12 Department of Pediatric Oncology, Hematology, and Immunology, Heidelberg University Hospital, Heidelberg, Germany.</p> <p>13 Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany</p> <p>14 Clinical Cooperation Unit Neuropathology, DKFZ, Department of Neuropathology, Heidelberg University Hospital </p> <p> </p> <p>NOTE: </p> <p>During manuscript preperation, the sample names were changed to coincide better with other published papers on these samples. The following translation applied between sample names used in the manuscript and in the datasets accompanying the paper:</p> <p> </p> <p>STP-Nuclei/PDX = LFS MBP Nuclei / PDX</p> <p>ST1R-Nuclei/PDX = LFS MB1R Nuclei / PDX</p> <p>MB243 = MB243</p> <p>BT084 = BT084</p> <p>RCMB18 = RCMB18 </p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.