18 posts
A working guide to quantifying transcript isoforms from short-read RNA-seq: building a Salmon index, running selective alignment, importing with tximport, testing differential transcript usage with DRIMSeq and DEXSeq, and deciding when the answer requires long reads.
A concrete walkthrough of bulk RNA-seq: where to get a real example dataset, what the files contain, how to quantify with salmon, and how to get to a gene-level table you can interpret, including the case where you only have your own samples.
A practical pipeline for turning your own bulk RNA-seq FASTQs into quantified gene and transcript expression, with the tool choices, flags, QC thresholds, and failure modes that matter for a single-person longitudinal dataset.
A working transcriptome analysis workflow for a single person's whole-blood RNA-seq: QC, selective-alignment quantification with salmon, gene-level counts, normalization across timepoints, cell-composition correction, and pathway interpretation.
Web platforms are good at the downstream half of RNA-seq (differential expression, enrichment, plots) and bad at the upstream half (FASTQ to counts). Here is the split we'd use, with the commands, parameters, and failure modes.
A concrete walkthrough of the RNA-seq workflow from FASTQ to interpreted results: quality control, pseudoalignment with salmon, count import with tximport, differential expression with DESeq2, and the sanity checks that catch most errors.
A working pipeline for going from RNA-seq FASTQs to junction counts, PSI values, and a shortlist of splice-altering variants from your own genome, with the tools and parameters we would use.
A working guide to clustering bulk and single-cell RNA-seq: normalization choices that decide your dendrogram, gene-module discovery across timepoints, choosing k defensibly, and the failure modes that produce confident nonsense.
A practical guide to Gene Ontology analysis of RNA-seq: building a defensible gene list, correcting for length and expression bias, running overrepresentation tests and GSEA in R, and collapsing redundant terms into something you can read.
Read depth in RNA-seq is the number of reads assigned to your sample and, per gene, the number of reads supporting each transcript. This page explains how to compute it from your own files, why 30x means nothing here, and what depth to ask for.
A working guide to taking personal RNA-seq FASTQ files through quality control, quantification, sample sanity checks, and longitudinal within-person expression analysis, with the commands and the failure modes.
A practical workflow for taking your RNA-seq FASTQ files to transcript quantification, then using web-based analysis tools for the parts they do well. Includes commands, parameters, cost estimates, and the failure modes that waste the most time.
A working pipeline for taking whole-blood RNA-seq FASTQ files to a gene-level count matrix, differential expression across timepoints, pathway scores, and cell-type composition, with the parameters and failure modes that matter.
A working pipeline from salmon quantification files to a differential expression table and pathway results, written for one person's longitudinal blood transcriptome rather than a two-group lab experiment.
A step-by-step guide to quantifying, normalizing, modeling, and interpreting your own RNA-seq data in R, from FASTQ files through differential expression, pathway scoring, and blood cell deconvolution.
A working guide to running DESeq2 on bulk RNA-seq counts: building the count matrix, filtering, size factors, dispersion, Wald versus likelihood ratio tests, log fold change shrinkage, and the sanity checks that catch most mistakes.
A working pipeline from FASTQ files to gene-level counts, differential expression, and enrichment, using salmon, pytximport, and PyDESeq2, with the failure modes specific to blood RNA from a single person.
Master RNA sequencing library preparation with our guide. Learn workflows, QC steps, and how prep choices impact analysis for optimal results.