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7 results for “RNA-seq data simulation”
Simulated RNA-seq data
<p>Simulated RNA-seq data shows that histograms from p value sets with around one hundred true effects out of 20,000 features can be classified as 'uniform'. RNA-seq data was simulated with polyester R package <a href="https://doi.org/10.1093/bioinformatics/btv272">(Frazee, 2015)</a> on 20,000 transcripts from human transcriptome using grid of 3, 6, and 10 replicates and 100, 200, 400, and 800 effects for two groups. Fold changes were set to 0.5 and 2. Differential expression was assessed using DESeq2 R package <a href="https://doi.org/10.1186/s13059-014-0550-8">(Love, 2014)</a> using default settings and group 1 versus group 2 contrast. Effects denotes in facet labels the number of true effects and N denotes number of replicates. Red line denotes QC threshold used for dividing p histograms into discrete classes. Workflow and code used to run this simulation is available on <a href="https://github.com/rstats-tartu/simulate-rnaseq">rstats-tartu/simulate-rnaseq</a>.</p> <p> </p> <p>Files</p> <ul> <li>de_simulation_results.csv -- merged and processed DE analysis results of simulated data.</li> <li>simulate-reads-2021-01-25.tar.gz -- raw DE analysis results on 20,000 transcripts from human transcriptome using grid of 3, 6, and 10 replicates and 100, 200, 400, and 800 effects for two groups. Fold changes were set to 0.5, 1, and 2. Differential expression was assessed using DESeq2 with default settings.</li> <li>simulate-rnaseq.tar.gz -- snakemake workflow and input fasta file to simulate RNA-seq data with polyester and analyse results with DESeq2. Adjust settings in config.yaml to customise simulation. Includes software to run workflow on Linux, given that <a href="https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh">Conda</a> and <a href="https://snakemake.readthedocs.io/en/stable/index.html">snakemake</a> are installed.</li> </ul> <p>The simulate-rnaseq.tar.gz archive can be re-executed on a vanilla machine that only has Conda and Snakemake installed via:</p> <pre><code class="language-bash">tar -xf simulate-rnaseq.tar.gz snakemake --use-conda -n</code></pre> <p> </p> <p> </p>
Isosceles paper simulated ovarian cell line ONT data (bulk RNA-Seq)
<div> <p>Simulated ovarian cell line ONT data (bulk RNA-Seq) for the Isosceles paper - more details can be found in the <a href="https://github.com/Genentech/Isosceles_Paper" target="_blank" rel="noopener">Isosceles_Paper</a> repository.</p> </div>
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>
ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data (repository for simulated ONT RNA-seq data)
<p>Simulated ONT direct RNA and 1D cDNA sequencing data of varying sequencing depths (0.5 million, 1 million, 3 million, and 5 million simulated reads) used for benchmark evaluations of transcript discovery and quantification in our paper "ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data". All details can be found in the <strong>Materials and Methods</strong> section of the paper. </p> <p><em>HEK293T_DirectRNA.transcriptome_quantification.tsv</em> and <em>HEK293T_DirectRNA.transcriptome_quantification.tsv </em>are tab-separated files containing estimated raw read counts and normalized abundance values (in TPM) of transcripts annotated in GENCODE v34lift37. Transcript quantification was done using NanoSim (version 3.1.0). </p> <p><em>HEK293T_DirectRNA.NanoSim_500k.fastq.gz</em>,<em> </em><em>HEK293T_DirectRNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_DirectRNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_DirectRNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT direct RNA sequencing reads respectively. </p> <p><em>HEK293T_1DcDNA.NanoSim_500k.fastq.gz</em>,<em> HEK293T_1DcDNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_1DcDNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_1DcDNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT 1D cDNA sequencing reads respectively. </p>
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>
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