10 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 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.
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 guide to turning personal RNA sequencing FASTQs into gene- and transcript-level expression estimates: reference choice, selective alignment with Salmon, spliced alignment with STAR, QC thresholds, and the checks that catch a swapped or degraded sample.
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 practical guide to picking a human genome build and annotation for RNA-seq, building STAR and salmon indexes correctly, and checking that the reference you built is the one your quantification assumes.
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.