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530 results for “RNA splicing”
Supplementary Data for MOCCASIN: A method for correcting known and unknown confounders in RNA-Seq-based splicing analysis
<p>Contents</p> <ol> <li><strong>moccasin_paper_env.yaml</strong>: conda environment file with R and Python packages and modules needed to reproduce analyses.</li> <li><strong>FigureReproduction.zip</strong>: data and code to reproduce main and supplemental figures.</li> <li><strong>MOCCASIN_ExampleDataset.zip</strong>: A small subset of the simulated data with example code to run MOCCASIN.</li> <li><strong>encode_corrected.zip</strong>: Folder with batch-corrected ENCODE differential splicing quantifications (dPSI).</li> </ol> <p> </p> <p> </p> <p>(1) <strong>moccasin_paper_env.yaml</strong></p> <p>Use the moccasin_paper_env.yaml file to create a conda environment from which all analyses for the paper can be reproduced.</p> <pre><code class="language-bash"># need to first install conda. See here: # https://docs.conda.io/en/latest/miniconda.html # Next, create a conda environment: conda env create --name moccasin_paper_env --file moccasin_paper_env.yaml --force # Activate the environment: conda activate moccasin_paper_env</code></pre> <p><br> The only Python packages not included in this environment are MAJIQ & VOILA. Please see majiq.biocipers.org for installation instructions.</p> <p> </p> <p> </p> <p>(2) <strong>FigureReproduction.zip</strong></p> <p>Within FigureReproduction are folders with code and data to reproduce the main and supplemental figures of the publication. Each folder contains data, script(s) and a README.txt with instructions on how to reproduce figures.</p> <p> </p> <p> </p> <p>(3) <strong>MOCCASIN_ExampleDataset.zip</strong></p> <p>Within this folder is an example dataset to test MOCCASIN. The README.txt file contains detailed line-by-line instructions for how to run MOCCASIN and do post-MOCCASIN analyses. In this example, we show how to run MOCCASIN on a group of .majiq samples with one known confounding effect. Also demonstrated is how to run an "explore unknown residuals" analysis as described in the detailed methods in the supplemental of the paper. </p> <p> </p> <p> </p> <p>(4) <strong>encode_corrected.zip</strong></p> <p>Includes a file called ENCODE_BeforeAndAfterMOCCASIN.voila.tsv.zip which includes LSV quantifications before and after MOCCASIN. Each row in the file represents a junction from an LSV. Each column header starts with the prefix "BeforeMOCCASIN" or "AfterMOCCASIN" and headers ending in dPSI corresponds to the dPSI of an ENCODE knockdown vs control experiment. </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>
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>
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>
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>
RNA-seq and RT-qPCR data showing MDF role in RNA splicing and gene expression control in Arabidopsis
<p>Plants respond to environmental stresses through controlled stem cell maintenance and meristem activity. One level of transcriptional control is RNA alternative splicing. However the mechanistic link between stress, meristem function and RNA splicing is poorly understood. The MERISTEM-DEFECTIVE (MDF)/DEFECTIVELY ORGANIZED TRIBUTARIES (DOT2) gene of Arabidopsis encodes a SR-related family protein, required for meristem function and leaf vascularization, and is the likely orthologue of the human SART1 and yeast snu66 splicing factors. MDF is required for the correct splicing and expression of key transcripts associated with root meristem function. We identified RSZ33 and ACC1, both known to regulate cell patterning, as splicing targets required for MDF function in the meristem. MDF expression is modulated by osmotic and cold stress, associated with differential splicing and specific isoform accumulation and shuttling between nucleus and cytosol, and acts in part via a splicing target SR34. We propose a model in which MDF controls splicing in the root meristem to promote stemness and repress stress response and cell differentiation pathways.</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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RNA-seq and RT-qPCR data showing MDF role in RNA splicing and gene expression control in Arabidopsis
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Paired datasets to study alternative splicing regulation by individual RNA-binding proteins
<p>This project stores datasets generated to study the regulation of alternative splicing using deep learning models (e.g., SpliceAI). In particular, these datasets were used to perform ablation studies (sequence perturbations at motif locations) to evaluate their effects on the deep learning model.</p> <p><span>I </span><span>used public RNA-Seq data from the ENCODE consortium to identify exons sensitive to the knockdown of RNA-binding proteins (RBPs). The idea is that exons sensitive to RBP knockdowns are more likely to be directly or indirectly regulated by such RBPs, hence providing hints on their regulation mechanisms. Importantly,</span><span> I </span><span>also generated paired control exons, which were not alternatively spliced upon RBP knockdown but have similar GC composition and length compared to the knockdown-sensitive exons (target exon and surrounding introns). These control sets were generated to account for potential confounding factors of gene architecture features and, therefore, focus only on RBP binding motifs and their regulatory logic.</span></p> <p><strong>Information about the files</strong></p> <p>After uncompressing the 'paired_dataset.tar.gz' file, a directory with multiple files will be created with the following structure:</p> <ul> <li><em>0_rMATS_ES_events.tsv.gz</em><em>: </em>Summary tables of differential splicing analysis, with deltaPSI estimates referring to Ctrl - Knockdown groups. Important columns: 'target_coordinates' refers to the 1-based coordinates of the alternatively spliced exon, and 'group' indicates the individual knockdown experiments where the exon was observed to be alternatively spliced.</li> <li><em>0_rMATs_ES_non_changing_events.tsv.gz:</em> Summary tables of differential splicing analysis, but in this case, contains all non-changing events (dPSI < |0.025|).</li> <li> <p><em>1_KD_exons_dPSI0.1.tsv.gz:</em> Table with knockdown-sensitive exons along with values for gene architecture features along the exon triplet (exon upstream, intron upstream, cassette exon, intron downstream exon downstream).</p> </li> <li> <p><em>1_Ctrl_exons_dPSI0.025.tsv.gz:</em> Same as '1_KD_exons_dPSI0.1.tsv.gz', but for all non-changing events.</p> </li> <li> <p><em><strong>2_paired_datasets.tsv.gz:</strong></em> Paired datasets in tidy format, where Knockdown-sensitive exons and their Control pairs come in consecutive lines. The 'rbp_name' column refers to the individual knockdown experiment where that exon was observed.</p> </li> <li><em>2_paired_datasets_negative_dPSI.tsv.gz, 2_paired_datasets_positive_dPSI.tsv.gz:</em> Same as '2_paired_datasets.tsv.gz', but knockdown-sensitive exons are split according to the direction of dPSI observed in the RNA-Seq data (along with the respective control pair).</li> <li><em>2_paired_datasets_individualRBPs</em>: This folder contains the paired datasets in wide format, where a single line contains both the knockdown-sensitive and control pair. In addition, each paired dataset (knockdown of individual RBP) is written in a separate file.</li> </ul> <div><strong>Details of the sh knockdown RNA-Seq analysis</strong></div> <div>Because in the ENCODE study (Van Nostrand E.L. et al., 2020), authors analyzed knockdown RNA Seq data using an older version of the human genome (hg19) along with old genome annotations (GENCODE v19), I reanalyzed ENCODE data aligned to the hg38 genome build. I used rMATS v4.1.2 on each RBP knockdown experiment to detect differentially spliced events between the two knockdown replicates vs the two control replicates. rMATS was run with GENCODE annotations v44 and specifically tweaked with <em>--cstat 0.05</em>. <div> </div> <div>Significant knockdown-sensitive events were identified with a deltaPSI > |0.1|, using a False Discovery Rate cutoff of 0.05. Non-changing events, assumed as knockdown-agnostic controls, were defined as those exhibiting negligible deltaPSI variation (< |0.025|). To ensure the high quality of the exon sets, further analytical steps were performed. First, I applied a read coverage filter, by retaining events where the median coverage across replicates per condition for the isoform with more read counts was higher than 7. Then, I exclusively focused on exon skipping events in protein-coding genes, and filtered out unannotated exons (pseudoexons) as well as first or last exons of genes. In addition, I excluded duplicate exon skipping events by picking the transcript with the highest biological importance (based on the presence of transcript flags such as MANE selected, CCDS, or APPRIS). A total of 15,235 events were detected across all RBP knockdown experiments (N=72, splicing-associated RBPs with data available for the HepG2 cell line), covering 6,659 unique exons.</div> </div> <div> <div> </div> </div>
Supplementary data for paper "RNA in situ conformation sequencing reveals novel long-range RNA interactions that impact splicing"
<p>Supplementary data for paper "RNA in situ conformation sequencing reveals novel long-range RNA structures with impact on splicing".</p> <p>Data showing support for PCCR (<a href="https://www.nature.com/articles/s41467-021-22549-7">paper</a>) in RIC-seq data (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190214">archive</a>).</p> <p> </p> <p>S1: for each supported PCCR lists numbers of experiments with inner and outer support and number of supporting reads in each experiment</p> <p>S2: for select PCCR lists supporting and non-supporting cell lines, total number of supporting reads and mean PSI in each group, and difference in mean PSI between the groups</p>
Back-spliced RNA from retrotransposon binds to centromere and regulates centromeric chromatin loops in maize
<p>In most plants, centromeric DNA contains highly repetitive sequences, including tandem repeats and retrotransposons; however, the roles of these sequences in the structure and function of the centromere are unclear. Here, we found that multiple RNA sequences from centromeric retrotransposons (CRMs) were enriched in maize (<i>Zea mays</i>) centromeres and back spliced RNAs were generated from CRM1. We identified three types of CRM1-derived circular RNAs with the same back-splicing site based on the back-spliced sequences. These circular RNAs bound to the centromere through R-loops. Two R-loop sites inside a single circular RNA promoted the formation of chromatin loops in CRM1 regions. When RNAi was used to target the back-splicing site of the circular CRM1 RNAs, the levels of R-loops and chromatin loops formed by these circular RNAs decreased, while the levels of R-loops produced by linear RNAs with similar binding sites increased. Linear RNAs with only one R-loop site could not promote chromatin loop formation. Higher levels of R-loops and lower levels of chromatin loops in the CRM1 regions of RNAi plants led to a reduced localization of the centromeric H3 variant (CENH3). Our work reveals centromeric chromatin organization by circular CRM1 RNAs via R-loops and chromatin loops, which suggested that CRM1 elements might help build a suitable chromatin environment during centromere evolution. These results highlight that R-loops are integral components of centromeric chromatin and proper centromere structure is essential for CENH3 localization.</p>
Datasets for the paper "Regulation of endocrine cell alternative splicing revealed by single-cell RNA sequencing in type 2 diabetes pathogenesis."
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RNA-sequencing reveals strong predominance of THRA splicing isoform 2 in the developing and adult human brain
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Back-spliced RNA from retrotransposon binds to centromere and regulates centromeric chromatin loops in maize
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Data from: Effect of light exposure on circular RNA and alternative splicing in Camelina sativa
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Specific inhibition of splicing factor activity by decoy RNA oligonucleotides
GEO Series GSE126503. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Systematic identification of RNA-binding proteins and tethered domains that activate exon splicing inclusion [eCLIP-seq]
GEO Series GSE232597. Homo sapiens. 20 samples. Type: Other.
Long non-coding RNA NEAT1 promotes sarcoma metastasis by regulating RNA splicing pathways
GEO Series GSE139574. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
A total RNA-Seq screen reveals that the splicing inhibitor Isoginkgetin blocks transcription elongation
GEO Series GSE86857. Homo sapiens. 80 samples. Type: Expression profiling by high throughput sequencing.
Specific modulation of HIV RNA splicing and upregulation of anti-inflammatory miR-124 by the new drug candidate ABX464
GEO Series GSE116073. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
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