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6,617 results for “RNA Sequencing”
Single-cell RNA sequencing of mouse embryonic cells from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages
<p>STRT-N is a newly optimized single-cell RNA sequencing method for studies of early genome activation in mammalian preimplantation development. Single embryos from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages were sampled for experiments and were sequenced using STRT-N method. Here is the raw data from STRTN-seq. FASTQ files are available in <a href="https://www.ebi.ac.uk/biostudies/studies/S-BSST976">BioStudies database</a>.</p>
Sequencing summaries & Nanopolish eventalign outputs for downstream analysis of yeast RNA and synthetic oligonucleotides
<p>This dataset contains:</p> <ol> <li>A set of text files from running the tool Nanopolish eventalign on several nanopore direct RNA sequencing data sets produced by Jay Hesselberth's lab at the University of Colorado (BioProject accession number <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA910992">PRJNA910992</a>), as well as external sequencing data sets from <a href="https://pubmed.ncbi.nlm.nih.gov/34893601/">PMID: 34893601</a> (synthetic oligonucleotides from Leger et al) and <a href="https://pubmed.ncbi.nlm.nih.gov/35252946/">PMID: 35252946</a> (yeast rRNA data from Stephenson et al).</li> <li>"Sequencing summary" files produced by MinKNOW, from nanopore sequencing of yeast mRNA and synthetic RNA oligos in the Hesselberth lab. We analyze these files in an associated manuscript to determine the "end status" of each read during the sequencing run.</li> </ol> <p>These files can be used as inputs to the R markdown documents at <a href="https://github.com/hesselberthlab/RNARePore">https://github.com/hesselberthlab/RNARePore</a> to reproduce the figures in the associated manuscript.</p>
Scaled RNA sequencing counts accompaining the publication Singh and Gollapalli 2023
<p>RNA-Seq analysis of WT and taurine-deficient osteoblasts.</p> <p>RNA was extracted from the mouse primary calvarial osteoblasts prepared from day 3–5 neonates of WT or <em>Slc6a6</em>-/- genotypes using the RNeasy extraction kit (Qiagen). RNA sequencing libraries were prepared with Illumina-compatible NEBNext Ultra<sup>TM</sup> II Directional RNA library kit at Genotypic Technology, Inc. The raw data were trimmed for adapter sequences and low-quality bases (<q30) using Cutadapt with default parameters [DOI:10.14806/ej.17.1.200] and checked for quality using FastQC [Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data]. Mouse raw reads were aligned to the mouse reference genome (mm9) using Hisat2(<em>24</em>) with default parameters. HTSeq [G Putri, S Anders, PT Pyl, JE Pimanda, F Zanini Analyzing high-throughput sequencing data with HTSeq 2.0 arXiv:2112.00939 (2021)] was used to estimate gene abundance. Downstream analyses were done using the analysis framework tidybulk (<em>25, 26</em>). The filterByExpr functionality of edgeR (<em>27</em>) was used to identify the abundant gene-transcripts included in downstream analyses using default parameters and the knock-out phenotype as the factor of interest. The algorithm trimmed mean of M valued (TMM) values identified sample-wise scaling to compensate for differences in sequencing depth across samples (<em>28</em>). For aiding data exploration, we reduced the data dimensionality using principal component analysis (PCA). Differential expression analyses were performed using the robust likelihood ratio implementation of edgeR, testing for differences in gene transcript abundance greater than 1 log-fold (<em>29, 30</em>). The ppcseq method was used to investigate the presence of outlier observation among the significant results to avoid biases in the statistics (<em>31</em>). The ggplot2 software produced most of the data visualizations, whereas tidyHeatmap was used for heatmap visualization (<em>32-34</em>). We performed gene-set enrichment analyses using the MSigDB C2 experimentally derived gene set, the clusterProfiler algorithm, and the visualization tool enrichplot (cran.r-project.org/) (https://rdrr.io/cran/msigdbr/) (<em>35</em>). An aging signature was composed of a union of gene sets from MSigDB and a set of genes derived from aging datasets identified in this study through gene set enrichment analysis and published literature (<em>36</em>). The <em>p</em>-values of gene set analyses were corrected for multiple testing using the Benjamini Hochberg correction (<em>37</em>).</p> <p> </p>
Additional raw data in `Cell-type-specific co-expression inference from single cell RNA-sequencing data'.
<p>This repository holds the additional raw data used to generate figures in the publication "<strong><em>Cell-type-specific co-expression inference from single cell RNA-sequencing data</em></strong>" (preprint version: <a href="http://source%20code%20repo%20for%20%60cell-type-specific%20co-expression%20inference%20from%20single%20cell%20rna-sequencing%20data%27./">https://www.biorxiv.org/content/10.1101/2022.12.13.520181v1</a>).</p> <p>Table of contents:</p> <ul> <li>Figure_1B.rds: <ul> <li>raw data of Figure 1B </li> <li>co-expression estimates of 500*499/2 gene pairs across 100 replicates for 7 methods under two settings of sequencing detph variations</li> </ul> </li> <li>Supplementary_Figure_1B.rds: <ul> <li>raw data of Supplementary Figure 1B </li> <li>co-expression estimates of 500*499/2 gene pairs across 100 replicates for 7 methods under two settings of sequencing detph variations</li> </ul> </li> <li>Supplementary_Figure_2.rds: <ul> <li>raw data of Supplementary Figure 2 </li> <li>empirical power evaluated for 4999 gene pairs and 6 methods</li> </ul> </li> <li>Figure_3B.rds: <ul> <li>raw data of Figure 3B</li> <li>co-expression estimates of a network of 500 genes for 9 methods across 100 replicates</li> </ul> </li> <li>Additional_Raw_Data.xlsx <ul> <li>raw data of Figure 3A: (geometric mean expression levels, co-expression estimates) for 4999 gene pairs and 11 methods</li> <li>raw data of Figure 3C: running times for 11 methods</li> <li>raw data of Supplementary Figure 3: (geometric mean expression levels, co-expression estimates) for 4999 gene pairs and 11 methods under two settings of sequencing detph variations</li> </ul> </li> </ul>
Fastq sequence files supporting: Assessing the degradation of environmental DNA and RNA based on genomic origin in a metabarcoding context
<p>Molecular tools of species identification based on eNAs (environmental nucleic acids; eDNA and eRNA) have the potential to greatly transform biodiversity science. However, the ability of eNAs to obtain "real-time" biodiversity estimates may be complicated by the differential persistence and degradation dynamics of the molecular template (eDNA or eRNA) and the barcode marker used. Here, we collected water samples over a 28-day period to comparatively assess species detection using eDNA and eRNA metabarcoding of two distinct barcode markers—a mitochondrial mRNA marker (COI) and a nuclear rRNA marker (18S)—following complete removal of <em>Arthropoda </em>taxa in a semi-natural freshwater system. Our findings demonstrate that <em>Arthropoda </em>community composition was largely influenced by marker choice, rather than molecular template, individual microcosm, or sampling time point. Further, although eRNA may capture similar species diversity as the established eDNA method, this finding may be marker dependent. Although we found little to no difference in decay rates observed among sample groups (COI eDNA, COI eRNA, 18S eDNA, 18S eRNA), this result is likely due to limitations in the ability of eNA-based metabarcoding to provide a strong correlation between true eNA copy numbers present in the environment and final read counts obtained (following the metabarcoding workflow). Collectively, our findings provide further support for the use of multi-marker assessments in metabarcoding surveys to unravel the broadest taxonomic diversity possible, highlight the limitations of eNA metabarcoding methods in providing accurate decay rate estimates, as well as establish the need for further comparative studies using both metabarcoding and single-species detection methods to assess the persistence and degradation dynamics of eNAs for a diverse range of taxa.</p>
Characterising neutrophil subtypes in cancer using human and murine single-cell RNA sequencing datasets
<p>Single cell RNA sequencing data generated by 10xGenomics for Neutrophils derived from colorectal cancer (CRC) KPN tumours (CRC_KPN_counts.csv) and normalised counts (CRC_KPN_NormalisedCounts.csv) as well as from other mouse models of CRC carrying AKPT, BPN, BP and KP mutations (CRC_other_counts.csv and CRC_other_NormalisedCounts.csv), together with the relevant metadata (CRC_KPN_metadata.csv and CRC_other_metadata.csv).</p>
Single Cell RNA sequencing data of ADT treated Prostate cancer patients
<p>The data was generated from a study that was conducted according to guidelines approved by the Review Board at the University of Texas Southwestern Medical Center. We procured patient biopsy samples from two distinct studies. The first is titled "Tissue Collection and Results Gathering for Radiotherapy Patients & Healthy Individuals" (STU 072010-098), and the second is a Phase I Clinical Study on Stereotactic Ablative Radiotherapy (SABR) for Pelvic and Prostate Areas in High-Risk Prostate Cancer Patients (STU062014-027). The single-cell RNA sequencing (scRNA-seq) took place in Dr. Douglas Strand's laboratory, adhering to the method outlined in Henry et al<sup>1</sup>. We used a 1-hour treatment with 5mg/ml of collagenase type I, 10mM of ROCK inhibitor, and 1mg of DNase. Barcode labeling for 3' GEX was done using a 10X machine, and the sequencing process utilized an Illumina NextSeq 500 device.</p> <p> </p> <p>1. Henry, G. H., Malewska, A., Joseph, D. B., Malladi, V. S., Lee, J., Torrealba, J., ... & Strand, D. W. (2018). A cellular anatomy of the normal adult human prostate and prostatic urethra. <em>Cell reports</em>, <em>25</em>(12), 3530-3542.</p>
Dataset for: mRNA vaccine quality analysis using RNA sequencing
<p>The success of mRNA vaccines has been realised, in part, by advances in manufacturing that enabled billions of doses to be produced at sufficient quality and safety. However, mRNA vaccines must be rigorously analysed to measure their integrity and detect contaminants that reduce their effectiveness and induce side-effects. Currently, mRNA vaccines and therapies are analysed using a range of time-consuming and costly methods. Here we describe a streamlined method to analyse mRNA vaccines and therapies using long-read nanopore sequencing. Compared to other industry-standard techniques, VAX-seq can comprehensively measure key mRNA vaccine quality attributes, including sequence, length, integrity, and purity. We also show how direct RNA sequencing can analyse mRNA chemistry, including the detection of nucleoside modifications. To support this approach, we provide supporting software to automatically report on mRNA and plasmid template quality and integrity. Given these advantages, we anticipate that RNA sequencing methods, such as VAX-seq, will become central to the development and manufacture of mRNA drugs.</p>
Small RNA sequencing
<p>The innovation of large-scale, next-generation sequencing has exponentially increased knowledge of RNA biology, with regard to the diversity, abundance, and function of various RNA molecules. <a href="https://rna.cd-genomics.com/small-rna-sequencing.html">Small RNA-seq</a> is a powerful tool for analyzing small RNAs such as miRNAs, siRNAs, and piRNAs in a single sequencing run, allowing the evaluation and discovery of novel small RNA molecules and the prediction of their functions. These RNA-seq methods have provided an even more complete characterization of small RNA and promised further applications.<br><br>Our technologies at single-base resolution allow for small RNA detection from very small amounts of cellular materials, which can help you detect pre-known small RNA, discover new small RNA, and examine all small RNA for differential expression in any sample. We generate small RNA sequencing libraries directly from total RNA and Capture the complete range of small RNAs, to understand the roles they play. this will provide you with a comprehensive and efficient approach to understand post-transcriptional regulation and discovering novel biomarkers.</p>
RNA sequencing data for polyphenic and monophenic Manduca sexta strains
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Single-cell and spatial RNA sequencing identify divergent microenvironments and progression signatures in early- versus late-onset prostate cancer
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Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models
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Paired human macrophage RNA sequencing data
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Fastq sequence files supporting: Assessing the degradation of environmental DNA and RNA based on genomic origin in a metabarcoding context
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RNA sequencing of AML cells treated with milademetan, selinexor and the combination of milademetan and selinexor
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Deep sequencing datasets from: Witnessing the structural evolution of an RNA enzyme
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Simulated data from: Reference-free assembly of long-read transcriptome sequencing data with RNA-Bloom2
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Single cell RNA sequencing provides clues for the developmental genetic basis of Syngnathidae’s evolutionary adaptations
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RNA seq of 4-week-old SAB23KO mutants and wild-type plants soaked for 0 or 1 day, and 6 ChIP libraries sequence (two DJ and four SAB23-GFP)
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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
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.