Tag: rna-seq

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 step-by-step guide to processing your own whole-genome and RNA sequencing data, and to validating the pipeline against a truth set.

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 guide to turning raw FASTQ files from whole-genome and RNA sequencing into aligned, called, annotated, and validated results on your own hardware, with the specific tools, flags, and quality thresholds we use.

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 practical guide to the web servers and databases that hold up when you point them at your own genome, transcriptome, and proteomics data, and the steps you should run yourself instead.

A practical guide to turning an RNA-seq counts matrix into a ranked pathway table, with fgsea and g:Profiler run correctly.

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 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 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 practical walkthrough of building a STAR index, running --quantMode GeneCounts, choosing the correct strand column in ReadsPerGene.out.tab, checking alignment quality, and converting raw counts to TPM.

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 estimating immune and tissue cell-type proportions from your own bulk RNA-seq: getting the expression matrix into the right space, choosing a reference, running marker- and regression-based methods, and checking the output against a blood count.

A specific, opinionated toolchain for a single person's whole-genome, RNA-seq, proteomics, and CGM data: the tools we would run, the flags that matter, and the failure modes that silently corrupt results.

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 the files a sequencer produces and what to do with them: FASTQ through alignment, variant calling, annotation, and RNA quantification, with the commands, parameters, and failure modes that matter.

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 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 practical setup for loading your own WGS alignments, variant calls, and RNA-seq coverage into IGV and igv.js, with the indexing, reference-build, and chromosome-naming details that usually break first.

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 GATK pipeline for calling SNPs and indels from RNA-seq reads, with filters suited to transcriptomic data and a check against genome calls.

A step-by-step guide to taking raw whole-genome FASTQs through alignment, variant calling, validation, and annotation, with the specific tools, flags, and quality thresholds we use.

A functional biomarker measures how well a system is working — a ratio, a rate, or a response to a challenge — rather than a single resting concentration. Here is how to build them from CGM, blood panels, proteomics, and RNA-seq, and where they break.

A working list of the tools that matter for analyzing your own genome, transcriptome, proteome, and biomarker data, with formats, flags, and the failure modes that bite.

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 practical guide to correlating genes, samples, and timepoints in personal RNA-seq data: which transform to use before computing a coefficient, how to filter, how to handle confounders, and how to correlate expression against proteomics, labs, and continuous glucose data.

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 guide to building the handful of plots that matter for a personal molecular dataset: coverage tracks, variant pileups, copy-number profiles, expression MA and volcano plots, and a genome-wide circular summary. With real commands, file formats, and the failure modes that produce misleading pictures.

A working guide to running KEGG enrichment on personal RNA-seq: quantification, identifier mapping, GSEA versus over-representation, reading the maps, and the failure modes that make most pathway results meaningless.

A working guide to turning RNA-seq quantifications into an annotated, versioned gene table. Covers choosing an annotation, tx2gene and biotypes.

A working pipeline from FASTQ to a ranked pathway table in R. Covers salmon, DESeq2 and fgsea, plus the confounders behind most false pathway hits.

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 practical guide to adapter, quality, poly-G and poly-A trimming for RNA-seq FASTQs: which tools to use, the exact flags, how much trimming is enough, and how to verify you did not damage the data.

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.

A step-by-step guide to going from raw FASTQ files to an aligned CRAM, a benchmarked variant call set, an annotated shortlist, and an RNA-seq expression table, with the actual commands, parameters, and quality thresholds we use.

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 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 pipeline from FASTQ to a publication-grade expression heatmap. Covers salmon quantification, gene selection, row scaling and clustering.

A step-by-step guide to turning transcript quantifications into plots that tell you something: sample-level QC, longitudinal gene trajectories, differential expression views, and pathway summaries, with the R and command-line code to produce them.

A step-by-step guide to producing a volcano plot from a count matrix with DESeq2 and ggplot2, choosing thresholds that mean something, and reading the resulting shape correctly, including the single-cell and mass-spectrometry variants.

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 working guide to going from FASTQ or a count matrix to a UMAP embedding you can defend: normalization, PCA, neighbor graph settings, initialization, and what the picture does and does not tell you.

A practical trimming workflow for personal whole-genome and RNA-seq FASTQs: how to find the real adapter, which tool and flags we use, how gently to trim, and how to verify that trimming helped rather than shifted your expression estimates.

The FASTQ layout, naming and read structure Cell Ranger expects. Covers producing the files from BCLs, verifying them and a run that finds nothing.

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 guide to turning RNA-seq FASTQs into the handful of plots that matter: sample PCA, distance heatmaps, MA and volcano plots, and longitudinal gene trajectories, with the commands, parameters, and failure modes.

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 practical walkthrough of differential expression analysis in R, from transcript quantification through DESeq2 and limma models, with the design decisions and failure modes that determine whether the gene list means anything.

A practical guide to computing principal components from RNA-seq counts in R. Covers the transform, gene selection and what each component means.

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 step-by-step pipeline for small RNA sequencing data, from adapter chemistry and UMI handling through hierarchical annotation, isomiR-level quantification, and differential expression, with the biofluid-specific failure modes that break naive runs.

A working guide to diagnosing and handling batch effects in bulk RNA-seq, from design and TMM normalization through limma, ComBat-seq, and RUV, with the checks that tell you whether the correction helped.

How to use R and Bioconductor as the annotation, statistics, and integration layer for a personal molecular dataset: VCFs, RNA-seq counts, proteomics, and continuous glucose data, with the parts you should not do in R.

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.

A post hoc |log2FC| > 1 filter is the wrong tool. Test against a fold-change threshold directly with DESeq2's lfcThreshold or edgeR's glmTreat, shrink effect sizes before ranking, and pick the threshold from your own replicate structure.

A practical guide to building violin plots from bulk and single-cell RNA-seq counts: which normalization to plot, how to set kernel bandwidth and trimming, and how to read the shapes without fooling yourself.