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21,239 results for “RNA-Seq”
Diamond hits: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues
<p>Diamond hits - predicted peptides were compared against NCBI RefSeq protein (NR) databases using Diamond software</p> <p>Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex – PC<br> 1 – horse ECA_UCD_AH1<br> 2 – horse ECA_UCD_AH2</p>
Diamond hits_1: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues
<p>Diamond hits - de novo assembled transcripts were compared against NCBI RefSeq Nucleotide (NT) using Diamond software</p> <p> </p> <p>Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex – PC<br> 1 – horse ECA_UCD_AH1<br> 2 – horse ECA_UCD_AH2</p> <p> </p>
Unmapped reds: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues
<p>Unmapped reds – all RNA-Seq reads that did not map to the reference EquCab3.0 genome using Tophat2 software</p> <p>Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex – PC<br> 1 – horse ECA_UCD_AH1<br> 2 – horse ECA_UCD_AH2</p>
Predicted peptides: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues
<p>Predicted peptides – peptides predicted based on the assembled transcripts using TransDecoder software</p> <p> </p> <p>Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex – PC<br> 1 – horse ECA_UCD_AH1<br> 2 – horse ECA_UCD_AH2</p> <p> </p>
Assembled transcripts: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues
<p>Assembled transcripts – putative transcripts de novo assembled with Trinity software</p> <p> </p> <p>Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex – PC<br> 1 – horse ECA_UCD_AH1<br> 2 – horse ECA_UCD_AH2</p> <p> </p>
Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data"
<p>Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data".</p> <p>Additional mitochondrial gene sequences for Danio rerio, Homo sapiens, and Mus musculus.</p>
Processing single-cell RNA-seq datasets using SingCellaR
<ul> <li>cellranger_output.zip : Zipped files for cellranger output</li> <li>Code.zip: This contains the code for Step2-7</li> <li>Human_genesets.zip: This includes the geneset signatures (.gmt) files we used in the protocol and original study (Roy et al, 2021)</li> <li>Human_HSPC_All.SingCellaR.rdata: The SingCellaR objects generated in Step 4 on a local computer </li> <li>ABM_1.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>eFL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>PBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>meta.data.txt: meta data includes the donor and batch information </li> </ul>
Datasets for the Carpentry-style RNA-seq lesson
<p><strong>Lesson files</strong></p> <p>For all compressed files, go to the Shell and uncompress using `tar -xzvf myarchive.tar.gz`.</p> <p><strong>1) Bioinformatic files: </strong><a href="https://zenodo.org/api/files/436d1854-d638-4fdb-8d6b-7c566ed13af8/bioinformatic_tutorial_files.tar.gz?versionId=a80e4b0b-daf1-4008-8c98-1995ccb33748">bioinformatic_tutorial_files.tar.gz </a></p> <p>This archive contains the following datasets:</p> <p>FASTQ files from Arabidopsis leaf RNA-seq:</p> <ul> <li>Arabidopsis_sample3.fq.gz</li> <li>Arabidopsis_sample1.fq.gz</li> <li>Arabidopsis_sample4.fq.gz</li> <li>Arabidopsis_sample2.fq.gz</li> </ul> <p>Arabidopsis thaliana genome assembly and genome annotation:</p> <ul> <li>AtChromosome1.fa.gz</li> <li>ath_annotation.gff3.gz</li> </ul> <p>The sequence of sequencing adapters in adapters.fasta.</p> <p><strong>2) Gene counts usable with DESeq2 and R: </strong><a href="https://zenodo.org/api/files/436d1854-d638-4fdb-8d6b-7c566ed13af8/tutorial.tar.gz?versionId=17f6c4f9-b013-4806-9d05-9782a0b873ab">tutorial.tar.gz </a></p> <p>This archive contains the following datasets:</p> <ul> <li><strong>raw_counts.csv</strong>: a dataframe of the sample raw counts. It is a comma-separate values file therefore data are separated by commas ','.</li> <li><strong>samples_to_conditions.csv</strong>: a dataframe that indicates the correspondence between samples and experimental conditions (e.g. control, treated). </li> <li><strong>differential_genes.csv: </strong>a dataframe that contains the result of the DESeq2 analysis specifying this contrast in `DESEq2::results()` function: `<code class="language-plaintext">contrast = c("infected", "Pseudomonas_syringae_DC3000", "mock")</code></li> </ul> <p>The<strong> raw_counts.csv </strong>file was obtained by running the `v0.1.1` version of a <a href="https://github.com/KoesGroup/Snakemake_hisat-DESeq/blob/master/README.md">RNA-Seq bioinformatic pipeline</a> on the mRNA-Seq sequencing files from Vogel et al. (2016): <a href="https://www.ebi.ac.uk/ena/data/view/PRJEB13938">https://www.ebi.ac.uk/ena/data/view/PRJEB13938</a>.</p> <p>Please read the original study (Vogel et al. 2016): <a href="https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.14036">https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.14036</a></p> <p> </p> <p>====</p> <p><strong>Exercise files</strong></p> <p><strong>1) NASA spaceflight</strong></p> <p>The NASA GeneLab experiment GLDS-38 performed transcriptomics and proteomics of Arabidopsis seedlings in microgravity by sending seedlings to the International Space Station (ISS).</p> <p>The raw counts, scaled counts and sample to conditions files are available in the <a href="https://zenodo.org/api/files/d8dfab0f-b449-4608-8f60-a6d553e5aea3/NASA_spaceflight_GLDS38.zip">ZIP archive</a></p> <p><strong>2) Deforges 2019 hormone-treatments: </strong><a href="https://zenodo.org/api/files/d4a0b869-8eed-4b24-853c-ca9f7537bb51/deforges_2019.tar.gz">deforges_2019.tar.gz </a></p> <p>This archive contains:</p> <ul> <li>arabidopsis_root_hormones_raw_counts.csv</li> <li>arabidopsis_shoot_hormones_raw_counts.csv</li> <li>arabidopsis_root_hormones_sample2condition.csv</li> <li>arabidopsis_shoot_hormones_sample2condition.csv</li> <li>dataset01_IAA_arabidopsis_root_raw_counts.csv</li> <li>dataset02_ABA_arabidopsis_root_raw_counts.csv</li> <li>dataset03_ACC_arabidopsis_root_raw_counts.csv</li> <li>dataset04_MeJA_arabidopsis_root_raw_counts.csv</li> </ul> <p>The <em>arabidopsis_root_hormones_raw_counts.csv</em> file contains all gene counts from all hormones. Separate datasets were made for each hormone for convenience.</p>
Test-Data for RNA-seq HSA Chr21 all protein coding transcripts
<p>Created with ART</p> <p>ART_Illumina (2008-2016) </p> <p>Q Version 2.5.8 (June 6, 2016)</p> <pre><code>art_illumina -ss MSv3 -i chr21_proteincoding.fa -p -l 75 -f 175 -m 200 -s 10 --minQ 30 </code></pre> <p> </p> <p> </p> <p>high:= higher expression</p> <p>low:= lower expression</p> <p>repA/B:=indicates replicates with slightly different sequencing depths</p> <p>R1/2:= forward/reverse read</p>
RNA-seq data of HepG2 cultured in 2D or as organoids
<p>Cancer cells cultured as organoids could better represent cancer cells grown in vivo and were shown to harbor increased stemness compared with cancer cells in 2D culture. Hep G2 cell line was used as parental cell line to establish a liver cancer organoid line. Hep G2 2D culture and organoid culture were subjected to bulk RNA-seq. </p>
tcga rna-seq
<pre>RNASeq from The Cancer Genome Atlas (TCGA) database</pre>
RNA-Seq data of common woodlice Porcellio scaber
<p>This dataset contains raw transcriptome sequences of common woodlice <em>Porcellio scaber</em> (Crustacea: Isopoda), an important model species in ecotoxicology, developmental biology, immunology and physiology studies. In this context, dataset (transcriptome) provides a cornerstone for all future gene-based research of <em>P. scaber</em>. </p> <p>For transcriptome construction, total RNA was extracted from the whole organism of <em>P. scaber</em>. The extracted RNA was checked for quantity, purity, and integrity and sent to Novogene (United Kingdom) for sequencing. The transcriptome was generated by high-throughput sequencing using an Illumina NovaSeq 6000 platform with 150 bp paired-end option. The dataset contains raw reads recorded in FASTQ format; Ps2_1.fq and Ps2_2.fq, containing Read 1 and Read 2 for paired-end sequencing, respectively.</p>
RNA-seq of green hydra strains' ( Hydra viridissima) response to the removal or exchange of symbionts
<p>The symbiotic hydra <em>Hydra </em><em>viridissima</em> has a stable symbiotic relationship with the green alga <em>Chlorella</em>. This hydra appears to cospeciate with the symbiotic alga, and some strains are known to have strain-specific host/symbiont combinations. To investigate the mechanism of the specificity between host and symbiont, we explored the effect of the removal or exchange of symbionts in two distantly related <em>H. viridissima</em> strains (K10 and M9). In this study, we compared the gene expression of symbiont-removed, symbiont-exchanged hosts for each strain. The data include the raw read fastaq, assembled sequences, and read counts of RNA-seq. We also attached the results of differential gene expression analyses for all combinations of the hosts. </p>
A risk-reward examination of sample multiplexing reagents for Single Cell RNA-Seq
<div> <h2>A Risk-reward Examination of Sample Multiplexing Reagents for Single Cell RNA-Seq</h2> <br> <div>The publication is available open access at https://doi.org/10.1016/j.ygeno.2024.110793</div> </div> <p>Be sure to check the md5 checksum: checklist.chk</p>
Simulated RNA-seq data for differential splicing analysis with covariates
<p>The repository includes alignments of simulated RNA-seq data for evaluating differential splicing detection with covariates. Starting from an empirical transcript expression matrix trained on an RNA-seq data set from lung fibroblasts (GenBank A# SRR493366) and using GENCODE v.41 as reference, 11.5 million 100 bp long paired-end reads were generated per sample, from 2,000 genes with two or more expressed isoforms. RNA-seq data was simulated for one ‘condition’, with values ‘control’, ‘disease’ and ‘stage2’, with one covariate, ‘biological sex’, with values ‘M’ and ‘F’. 5 samples each were simulated for each (condition x sex) category. Changes were simulated in the expression (DE) and/or the splicing ratio (DS) of genes as follows. Changes in expression (DE) were simulated by either halving or doubling the expression level of the gene. Changes in splicing ratios (DS) were simulated by swapping the expression levels of the gene’s top two transcript isoforms. All RNA-seq data was mapped to the hg38 genome with the spliced alignment tool STAR v2.7.10a.</p> <p> <em><u>Pairwise comparison alignment set</u></em>: Differences due to ‘condition’ between two states, ‘control’ and ‘disease’, were simulated at 600 genes, including 200 DE, 200 DS and 200 DE+DS genes. Differences in ‘biological sex’ (covariate) were represented as changes in 300 genes, including 100 from each of the DS, DE and DE+DS categories. Hence, the target gene set for differential splicing ratio (DSR)<em> pairwise comparisons </em>consists of the pooled 200 DS and 200 DS+DE genes differentially spliced between the ‘control’ and ‘disease’ states, while for differential splicing abundance (DSA)<em> pairwise comparisons </em>the target gene set is the set of 600 modified genes, 200 in each of the DS, DE and DS+DE categories.</p> <p> <em><u>Multiway (3-way) comparison alignment set:</u></em> Additional changes between ‘disease’ and ‘stage2’ were made to 100 of the previously modified genes, as well as to a set of 200 additional genes not encountered previously, for each of the categories DE, DS and DE+DS. Therefore, for <em>DSR three-way comparisons</em>, the target gene set represents the 800 genes simulated as being DS or DE+DS between any of the ‘control’, ‘disease’ and ’stage2’ categories, while for the <em>multi-way DSA comparisons</em> the target is the full set of 1,200 genes (400 DE, 400 DS and 400 DE+DS) simulated to have changed between any of the 'control', 'disease’ and ‘stage2’ states.</p> <p> <em><u>Further details:</u></em> See the ‘key’ directories in each package for the gene lists.</p>
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
<p>In this repository we provide the datasets for the results of the Cellstates method as shown in the article: “Identifying cell states in single-cell RNA-seq data at statistically maximal resolution” by Pascal Grobecker, Thomas Sakoparnig, and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: https://doi.org/10.1101/2023.10.31.564980</p> <p>There is a README file that describes the contents and formats of each of the tab-separated values files. There is one subdirectory containing files with Cellstates' results on the dataset of Zeisel et al. (DOI: 10.1016/j.cell.2018.06.021) which is accompanied by another README file describing the formats and contents of these files. </p>
Processed read counts from macrophage RNA-seq and ATAC-seq experiments
<p><strong>RNA-seq files:</strong></p> <ol> <li>RNA_count_matrix.txt.gz - raw read counts</li> <li>RNA_cqn_matrix.txt.gz - read counts quantile normalised with the cqn R package</li> <li>RNA_gene_metadata.txt.gz - information about the genes</li> <li>RNA_sample_metadata.txt.gz - information about the samples</li> </ol> <p><strong>ATAC-seq files:</strong></p> <ol> <li>ATAC_count_matrix.txt.gz - raw read counts</li> <li>ATAC_cqn_matrix.txt.gz - read counts quantile normalised with the cqn R package</li> <li>ATAC_peak_metadata.txt.gz - peak coordinates and other metadata</li> <li>ATAC_sample_metadata.txt.gz - sample metadata</li> <li>ATAC_consensus_peaks.gff3.gz - GFF3 file containing the GRCh38 coordinates of the peaks</li> </ol>
Example RNA-Seq Data
<p>This is an RNA-Seq small example dataset by transforming data originally from Guilgur et al. (2012). It consists of data corresponding to a small selection of Drosophila melanogaster genes. This dataset includes sequencing reads as well as gene annotation.</p>
Simulated RNA-Seq Datasets
<p><strong>The repository contains a simulated RNA-Seq experiment of Saccharomyces cerevisiae. </strong></p> <p> </p> <p><strong>NCBI Repository</strong></p> <p>ftp://ftp.ncbi.nlm.nih.gov/genomes/archive/old_genbank/Fungi/Saccharomyces_cerevisiae_uid128/</p> <p><strong>Sequencing Simulator <em>ART</em></strong></p> <p><a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm</a></p> <p><strong>RNA-Seq Experiment Simulator <em>Polyester</em></strong></p> <p><a href="https://github.com/alyssafrazee/polyester">https://github.com/alyssafrazee/polyester</a></p> <p><a href="https://academic.oup.com/bioinformatics/article/31/17/2778/183245">https://academic.oup.com/bioinformatics/article/31/17/2778/183245</a></p> <p> </p> <p> </p>
Dataset for RNA-seq basic tutorial in Galaxy Australia
<p>Dataset of synthetic RNA-seq data for Drosophila melanogaster: 2 conditions, 3 replicates in each, paired-end reads. Reference genome in GTF format. </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.