Tag: transcriptomics

A working guide to RNA-seq analysis for one person's data: library choices, depth, QC thresholds, and how to analyze repeated samples.

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 guide to the TopHat/TopHat2 spliced alignment pipeline: when to use it, the exact commands, and the HISAT2 and salmon pipelines we would use for a personal transcriptome today.

A step-by-step bioinformatics workflow for taking bulk RNA sequencing FASTQs from your own blood sample through quantification, quality control, normalization, and interpretation, with the tools, flags, and failure modes named.

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 practical pipeline for taking personal bulk RNA-seq FASTQs through quality control, quantification, normalization, and interpretation, with the parameters and failure modes that matter when n=1.

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 file-by-file walkthrough of RNA-seq output: FASTQ, BAM, quant.sf, count matrices, and QC reports, plus the pipeline and the interpretation rules we would use on your own data.

A working guide to building a STAR index, running a two-pass spliced alignment of human RNA-seq, and reading the output files that tell you whether the run is any good.

A working guide to taking raw RNA-seq counts from your own samples through filtering, effective-length correction, TMM or median-of-ratios scaling, and variance stabilization, with the diagnostics that tell you whether the normalization held.

A practical answer to RNA-seq depth: 30-40 million paired-end reads for whole-blood expression profiling, why the number moves with your question, and how to check that you got what you paid for.

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 turning FASTQ files from your own blood RNA into gene-level expression estimates you can interpret, with the quantification commands, normalization choices, and the failure modes that make people over-read their data.

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 working pipeline from a counts matrix to a ranked, deduplicated pathway table for personal RNA-seq, including the n=1 designs that are valid, the blood cell composition problem, and which tools to trust.

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 practical guide to designing, quantifying, and modeling a longitudinal RNA-seq series from one person — sampling schedule, salmon quantification, spline and impulse models, trajectory clustering, and the confounders that will ruin the result if you ignore them.

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 one person's whole-blood RNA-seq. Covers QC, salmon quantification, normalization and pathways.

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. Covers the count matrix, dispersion, 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.