A working pipeline from FASTQ to a publication-grade expression heatmap: salmon quantification, tximport, variance stabilization, gene selection, row scaling, clustering, and the sanity checks that keep you from reading noise.
A practical spec for buying whole-genome sequencing: coverage, library prep, file formats, the QC you should run yourself, and what WGS still misses.
What a post-meal glucose of 83, 87, or 94 mg/dL means, why the timing of the reading matters more than the number, and how to measure your own postprandial response properly with CGM and blood work.
Whole-genome sequencing processed with PharmCAT and Cyrius gives you more pharmacogenetic information than any branded psychiatric panel, and you keep the data. Here is how to run it and what the evidence supports.
A working guide to setting up an R and Bioconductor project around a personal molecular profile: loading your VCF, quantifying your own RNA-seq across timepoints, normalizing proteomics, and joining biomarker and glucose time series into one analyzable object.
A step-by-step guide to exporting raw genotype files from Ancestry, 23andMe, and MyHeritage, converting them to a reference-correct VCF, lifting to GRCh38, imputing, and understanding what an array file can and cannot tell you.
A working stack for analyzing your own whole-genome sequencing data: alignment with BWA-MEM2 or Dragen, variant calling with DeepVariant, annotation with VEP, and interpretation limits you should respect.
A working guide to comparing genomes — your own against the reference and against benchmark truth sets, assembly against assembly, and hundreds of bacterial genomes at once — with the tools, flags, and failure modes that matter.
A fasting glucose of 106 mg/dL places you just inside the prediabetes range by ADA criteria, but a single number carries more measurement noise than most people assume. Here is how to interpret it, what to measure next, and how to read your own CGM data.
What continuous glucose monitoring shows about post-meal glucose in people without diabetes: typical peak height, time to peak, return to baseline, and how to measure your own response properly rather than reacting to one alarming number.
Cash prices for pharmacogenetic panels run from about $50 to over $2,000 for largely the same gene list. Here is what drives the spread, what insurance pays, and how to derive the same star-allele calls from whole-genome data yourself.
A working guide to genetic testing in asymptomatic people: panel vs genome, the variant classes short-read pipelines silently miss, the annotation stack we'd run on our own VCF, and where a clinician is non-optional.
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 practical guide to resolving rsIDs against your own genotypes: normalizing a VCF, querying dbSNP and Ensembl, annotating with consequence, frequency, and ClinVar, and reading the result without over-interpreting it.
A step-by-step guide to going from raw FASTQ to a filtered, benchmarked, annotated single-sample VCF, with the tools, flags, and quality numbers we would use ourselves.
A working guide to variant interpretation software: what each layer of the stack does, which tools we would use for a personal whole genome, and where the interpretation breaks.
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.
The All of Us Research Program stopped returning research DNA results and removed ancestry and trait reports from participant accounts. What you were given, what you never had, and how to get raw genomic data you control.
A practical guide to choosing a continuous glucose monitor as a non-diabetic athlete, getting the raw data out, and analyzing it without over-reading sensor noise.
A practical guide to privacy in DNA testing: why genomes resist anonymization, what to ask a provider before you send a sample, and how to hold raw sequence data safely once you have it.