20 posts
A step-by-step pipeline for turning bulk RNA-seq FASTQ files into a gene-level count matrix, differential expression results, and pathway scores, with the specific tools, flags, and QC thresholds we use.
A step-by-step guide to turning your own blood RNA-seq FASTQ files into a normalized expression matrix, cell-type estimates, and pathway scores you can track across timepoints.
A working pipeline for taking personal bulk RNA-seq from FASTQ to quantified transcripts, quality-controlled expression matrices, and interpretable gene-level signals, with the commands and parameters we use.
A working guide to going from FASTQ files to a gene-level count matrix you trust: reference choice, STAR two-pass alignment, Salmon selective alignment, strandedness, and the QC numbers that tell you whether the run is usable.
A practical pipeline for taking your own whole-blood RNA-seq from FASTQ to a quantified, QC'd expression matrix you can track over time, with the parameters, checks, and failure modes that matter.
A working guide to turning raw RNA sequencing reads from your own blood into quality-controlled gene and transcript quantifications, with the specific tools, flags, and reference files we would use, plus the failure modes that silently corrupt results.
A working guide to aligning and quantifying your own RNA-seq data: reference choice, STAR and Salmon parameters, strandedness, quality thresholds, and the failure modes that silently distort expression estimates.
A working pipeline for taking personal bulk RNA-seq from raw FASTQ files to quantified expression, quality checks, reference comparison, and longitudinal analysis, with the specific tools, flags, and failure modes.
A step-by-step guide to going from FASTQ files to gene-level counts, cell-composition estimates, and longitudinal comparisons using your own transcriptome data.
A practical walkthrough from FASTQ to gene-level counts, cell-composition estimates, and pathway scores, written for someone analyzing their own blood RNA-seq rather than a two-group experiment.
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 one person's whole-blood RNA-seq. Covers QC, salmon quantification, normalization and pathways.
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.