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857 results for “alternative splicing;”
Light-regulated gene expression and alternative splicing data from rice seedlings.
<p>This data contains analyzed data from the experiment conducted on rice seedlings under dark and light conditions. Seeds of rice (Oryza sativa spp. japonica cv. Nipponbare) were sown in the dark and germinated on day 2 and continued to grow in the dark for another 6 days. 3 biological replicates of the dark-grown etiolated shoots were harvested on day 8 after sowing. The remaining dark-grown seedlings were exposed to continuous white light at 120 mol/m2/sec for 48 hours or another 2 days (Days 9 and 10 after sowing). Three replicates of the light-treated green-colored seedling samples were harvested at the end of day 10. Harvested samples were frozen in liquid nitrogen and stored at -80C until further processing.</p>
The functional impact of alternative splicing in cancer
<p>This dataset contains information on alternative splicing isoform switches as described in the related paper "Alternative splicing changes as drivers of cancer" (pre-print available). It contains the list of the observed switches, altogether with their functional implications (effect on the protein, on the PPI network, etc.) and related measures of relevance (links to mutational data, etc.). This dataset was thoroughly explored in a related Jupyter notebook.</p>
SHAPE datasets from manuscript: Modulation of pre-mRNA structure by hnRNP proteins regulates alternative splicing of MALT1
<p>Normalized SHAPE reactivity for the MALT1 M1 minigene RNA constructs (wildtype, variant 1, and variant 2) reported in the manuscript titled 'Modulation of pre-mRNA structure by hnRNP proteins regulates alternative splicing of <em>MALT1'</em> .</p> <p> </p>
Data for manuscript "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data"
<p>Output files generated by rMATS-turbo for the two example datasets described in the manuscript titled "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data".</p> <table> <tbody> <tr> <td>File</td> <td>Description</td> <td>Cell lines</td> <td>BioProject</td> </tr> <tr> <td>PC3E-GS689.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 1 described in the manuscript</td> <td>PC3E and GS689 cell lines</td> <td>PRJNA438990</td> </tr> <tr> <td>CCLE.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 2 described in the manuscript</td> <td>1,019 CCLE human cancer cell lines</td> <td>PRJNA523380</td> </tr> </tbody> </table> <p>A detailed description of the output files is available in the manuscript and the rMATS-turbo software GitHub repository (https://github.com/Xinglab/rmats-turbo).</p>
Alternative splicing and genetic variation of MHC-E: Implications for rhesus cytomegalovirus-based vaccines
<p>We used long-read sequencing to interrogate rhesus macaque (RM) MHC-E (Mamu-E) alternative splicing and genetic variations. Full-length Mamu-E RNA isoforms were recovered using the PacBio Iso-Seq method. Incomplete 5' ends of Mamu-E isoforms were confirmed using Sanger sequencing, where we identified three additional isoforms. Full-length human MHC-E (HLA-E) isoforms were also recovered using the PacBio Iso-Seq method. Isoform sequences and annotations are provided for both Mamu-E and HLA-E in addition to Mamu-E Sanger sequencing data. HLA-E annotations are reported using the hg38 reference, while Mamu-E annotations are shown using rhesus MHC Class I and II assemblies previously generated using Bacterial Artificial Cloning (BAC) technology (https://www.ncbi.nlm.nih.gov/nuccore/AC148696.1).</p> <p>Using PacBio Long Amplicon Analysis, we sequenced complete Mamu-E coding regions of 59 RMs and additionally captured 3' UTR polymorphism using mRNA-seq haplotype phasing analysis. The complete genotyping data for these animals are provided as well as animal metadata. Genotyping data is shown using the Mamu-E canonical isoform (Mamu-E1 from Iso-Seq analysis) as reference.</p>
Systematic analysis of alternative splicing in time course data using Spycone
<p>Spycone is available as a python package that provides systematic analysis of time course transcriptomics data. Figure 1 shows the workflow of Spycone. It uses gene or isoform expression and a biological network as an input. It employs the sum of changes of all isoforms relative abundance (total isoform usage) across time points to detect IS events. It further provides downstream analysis such as clustering by total isoform usage, gene set enrichment analysis, network enrichment, and splicing factors analysis.</p> <p>The SARS-Cov-2 infection and cancer dataset are used as an application demonstration for our Spycone tool and a simulation dataset is used for benchmark analysis. </p> <p>The rhinovirus dataset and SARS-Cov-2 infection (3 time points) for the tutorial in the documentation are included here. </p> <p>The simulated dataset from the 2 models described in the manuscript are uploaded as zen_simdata_{model}_{noise}.csv.</p> <p>A gtf file used in the splicing factor analysis, both in the manuscript and tutorial. Derived from ensembl GRCh38.99.</p>
The impact of biological sex on alternative splicing
<p>These files were pulled from the results obtained through the execution of the <a href="https://www.nextflow.io">Nextflow</a> workflow <a href="https://github.com/lifebit-ai/rmats-nf">rmats-nf</a> and through numerous notebooks. How the data were generated are noted for each file.</p> <ol> <li>fromGTF.tar.gz - One for each splicing type, generated within <a href="https://github.com/lifebit-ai/rmats-nf">rmats-nf</a>: fromGTF.A3SS.txt, fromGTF.A5SS.txt, fromGTF.MXE.txt, fromGTF.RI.txt,fromGTF.SE.txt.</li> <li>gtex.tar.gz. This is a a tar'd and gzipped version of the corrected gtex.corrected.rds file that is constructed by the `differentialGeneExpressionAnalysis.ipynb` jupyter notebook. A cautionary note, the latest version of GTEx is through AnViL -- see note regarding latest access ability to (GTEx data)[GTEx.md].</li> <li>rmats_final.tar.gz. For each splicing type, we have 5 files for there is a matrix of all included junction (ijc) counts, inclusion lengths (inclen), percent spliced in as calculated by rMATS 3.2.5, skipped junction counts (sjc) and skipped junction lengths (skiplen) for each junction and for each sample (SRR) generated by <a href="http://github.com/lifebit-ai/rmats-nf">rmats-nf</a>.</li> <li>SraRunTable.txt.gz. Old way of obtaining GTEx accessions.</li> <li>srr.tar.gz - This file contains the Sequence Run (SRR) data merged with phenotype data, generated by <a href="http://github.com/TheJacksonLaboratory/sexBiasedAlternativeSplicing/jupyter/differentialGeneExpressionAnalysis.ipynb">differentialGeneExpressionAnalysis.ipynb</a></li> </ol>
MARVEL for alternative splicing analysis
<p>Data and codes to reproduce the analysis included in https://www.biorxiv.org/content/10.1101/2022.08.25.505258v1 (now accepted for publication by Nucleic Acid Research)</p>
Two-step mixed model approach to analyzing differential alternative RNA splicing: Datasets and R scripts for analysis of alternative splicing
<p>Changes in gene expression can correlate with poor disease outcomes in two ways: through changes in relative transcript levels or through alternative RNA splicing leading to changes in relative abundance of individual transcript isoforms. The objective of this research is to develop new statistical methods in detecting and analyzing both differentially expressed and spliced isoforms, which appropriately account for the dependence between isoforms and multiple testing corrections for the multi-dimensional structure of at both the gene- and isoform- level. We developed a linear mixed effects model-based approach for analyzing the complex alternative RNA splicing regulation patterns detected by whole-transcriptome RNA-sequencing technologies. This approach thoroughly characterizes and differentiates three types of genes related to alternative RNA splicing events with distinct differential expression/splicing patterns. We applied the concept of appropriately controlling for the gene-level overall false discovery rate (OFDR) in this multi-dimensional alternative RNA splicing analysis utilizing a two-step hierarchical hypothesis testing framework. In the initial screening test we identify genes that have differentially expressed or spliced isoforms; in the subsequent confirmatory testing stage we examine only the isoforms for genes that have passed the screening tests. Comparisons with other methods through application to a whole transcriptome RNA-Seq study of adenoid cystic carcinoma and extensive simulation studies have demonstrated the advantages and improved performances of our method. Our proposed method appropriately controls the gene-level OFDR, maintains statistical power, and is flexible to incorporate advanced experimental designs.</p>
Characterisation of the Alternative Splicing Landscape in Breast Cancer
<p>Supplementary data for Project 2 titled : "Characterisation of the Alternative Splicing Landscape in Breast Cancer"</p>
Supporting data for manuscript "Alternative splicing programs of tumor development and progression"
<p>Contents:</p> <ul> <li>Pre-processed input objects for reproducible notebooks </li> <li>Raw noteebook outputs</li> <li>Supplementary Data Tables</li> </ul>
Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model
<p><span><strong>data.tar.gz </strong>contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:</span></p> <p> </p> <p><span><strong>./marvel: </strong>- </span><span>This directory contains rds and Rdata objects that were created using the MARVEL R package</span></p> <p><span>cell_type_goresults.rds - This is the go results split by cell type</span></p> <p><span>marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting</span></p> <p><span>normalized_sj_expression.Rds - This object is the normalized splice junction expression</span></p> <p><span>Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run</span></p> <p><span>significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type</span></p> <p><span>sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type</span></p> <p><span>sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition</span></p> <p> </p> <p><strong><span>./seurat: </span></strong><span>- This directory contains all intermediate and final Seurat single-cell gene expression objects</span></p> <p><span>annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses.</span></p> <p><span>clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers.</span></p> <p><span>filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA.</span></p> <p><span>filtered_brain_samples.rds - This is the filtered Seurat object, before PCA.</span></p> <p><span>integrated_brain_samples.rds - This the integrated Seurat object, before other steps.</span></p> <p> </p> <p><span><strong>./star: </strong>- </span><span>All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space.</span></p> <p><span>J1/ - This directory contains outputs for brain sample J1</span></p> <p><span>J13/ - This directory contains outputs for brain sample J13</span></p> <p><span>J15/ - This directory contains outputs for brain sample J15</span></p> <p><span>J2/ - This directory contains outputs for brain sample J2</span></p> <p><span>J3/ - This directory contains outputs for brain sample J3</span></p> <p><span>J4/ - This directory contains outputs for brain sample J4</span></p> <p><span>K1/ - This directory contains outputs for kidney sample K1</span></p> <p><span>K2/ - This directory contains outputs for kidney sample K2</span></p> <p><span>K3/ - This directory contains outputs for kidney sample K3</span></p> <p><span>K4/ - This directory contains outputs for kidney sample K4</span></p> <p><span>K5/ - This directory contains outputs for kidney sample K5</span></p> <p><span>K6/ - This directory contains outputs for kidney sample K6</span></p> <p> </p> <p><span><strong>./star/genome:</strong> - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>chrLength.txt</span></p> <p><span>chrNameLength.txt</span></p> <p><span>chrName.txt</span></p> <p><span>chrStart.txt</span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>Genome</span></p> <p><span>genomeParameters.txt</span></p> <p><span>Log.out</span></p> <p><span>SA</span></p> <p><span>SAindex</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p> <p> </p> <p><span><strong>./star/J1:</strong> - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>Log.final.out</span></p> <p><span>Log.out</span></p> <p><span>Log.progress.out</span></p> <p><span>SJ.out.tab</span></p> <p><span>Solo.out/</span></p> <p><span>STARgenome/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out:</strong>- This directory contains the outputs used for downstream analysis</span></p> <p><span>Barcodes.stats</span></p> <p><span>GeneFull_Ex50pAS/</span></p> <p><span>SJ/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS: </strong>- This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns)</span></p> <p><span>Features.stats</span></p> <p><span>filtered/</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p><span>UMIperCellSorted.txt</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: </strong>- This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages)</span></p> <p><span>barcodes.tsv.gz - This file contains filtered cell barcodes</span></p> <p><span>features.tsv.gz - This file contains filtered features (genes)</span></p> <p><span>matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/raw: </strong>- This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered.</span></p> <p><span>barcodes.tsv</span></p> <p><span>features.tsv</span></p> <p><span>matrix.mtx</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ: </strong>- This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>Features.stats</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ/raw:</strong> - This directory contains the raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>barcodes.tsv - This file contains filtered cell barcodes</span></p> <p><span>features.tsv - This file contains filtered features (splice junctions)</span></p> <p><span>matrix.mtx - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/_STARgenome:</strong> - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p>
Global signaling profiling in a human model of tumorigenic progression indicates a role for alternative RNA splicing in cellular reprogramming.
<p>Data was collected using a LTQ-XL mass spectrometer (Thermo). Phosphopeptides were enriched from cell extracts from 3 independent biological replicates, and each replicate was analyzed as 3 technical replicates for a total of 9 LC/MS/MS runs per cell line. Cell lines are based on the MCF-10A lineage of human mammary epithelial cells, and include MCF-10A (10A), MCF-10AT (AT), MCF-10ATG3B (TG) and MCF-10ACA1a (CA).</p>
Do 5' regions of human protein-coding genes contain the blueprints for alternative splicing?
<p>Datasets generated during the comparative analysis of multiple transcript isoform genes (MISOG) and single transcript isoform genes (SISOG). All data were based on the raw RefSeq GFF file (version <a href="https://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/annotation/annotation_releases/109.20210514/GCF_000001405.39_GRCh38.p13/GCF_000001405.39_GRCh38.p13_genomic.gff.gz">GCF_000001405.39</a>).Tables containing the Type CDS corresponds to TER (Translated Exonic Regions) in the manuscript.</p> <p>Code can be found here : <a href="https://github.com/weber8thomas/MISOG_SISOG">[Github]</a></p>
Alternative splicing analysis benchmark with DICAST
<p><strong>Alternative splicing is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools have been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools, which arguably provide the most detailed insights into the alternative splicing process. DICAST offers a modular and extensible framework for analysing alternative splicing, integrating eleven splice-aware mapping and eight event detection tools. We benchmark all tools extensively on simulated as well as whole blood RNA-seq data. STAR and HISAT2 demonstrated the best balance between performance and run time. The performance of event detection tools varies widely, with no tool outperforming all others. DICAST allows researchers to employ a consensus approach to consider the most successful tools jointly for robust event detection. Furthermore, we propose the first reporting standard to unify existing formats and to guide future tool development. </strong></p>
Data from: Bromodomain-containing protein 4 regulates innate inflammation via modulation of alternative splicing (images and qPCR data)
<p class="MsoNormal">Bromodomain-containing Protein 4 (BRD4) is a transcriptional regulator which coordinates gene expression programs controlling cancer biology, inflammation, and fibrosis. In the context of virus-infection, BRD4-specific inhibitors (BRD4i) block the release of pro-inflammatory cytokines and prevent downstream epithelial plasticity<a>. </a>Although the chromatin modifying functions of BRD4 in inducible gene expression have been extensively investigated, its roles in post-transcriptional regulation are not well understood. Given BRD4's interaction with the transcriptional elongation complex and spliceosome, we hypothesize that BRD4 is a functional regulator of mRNA processing. To address this question, we combine data-independent analysis - parallel accumulation-serial fragmentation (diaPASEF) with RNA-sequencing to achieve deep and integrated coverage of the proteomic and transcriptomic landscapes of human small airway epithelial cells exposed to viral challenge and treated with BRD4i. The transcript-level data was further interrogated for alternative splicing analysis, and the resulting datasets were correlated to identify pathways subject to post-transcriptional regulation. We discover that BRD4 regulates alternative splicing of key genes, including Interferon-related Developmental Regulator 1 (IFRD1) and X-Box Binding Protein 1 (XBP1), related to the innate immune response and the unfolded protein response. These findings extend the transcriptional elongation-facilitating actions of BRD4 in control of post-transcriptional RNA processing in innate signaling.</p>
NOTES OF USE OF WEB APPLICATION TO VISUALIZE ALTERNATIVE SPLICING FOR BIOMEDICINE
<p>This is examples files of output generated by GeneAPPExplorer. GeneAPP(registered software) is a web-based application for integrating, exploring, and visualizing alternative splicing data. It consists of a client-server pipeline suite that provides users with the resources to integrate raw outputs from differential alternative splicing (DAS) analyses from different tools with multiple biological databases to provide graphical representations and robust results that guide the selection of candidate targets for experimental validation.</p>
Two-step mixed model approach to analyzing differential alternative RNA splicing: Datasets and R scripts for analysis of alternative splicing
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Data from: Bromodomain-containing protein 4 regulates innate inflammation via modulation of alternative splicing (images and qPCR data)
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Transcriptome-wide alternative mRNA splicing analysis reveals post-transcriptional regulation of neuronal differentiation
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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