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. Covers panel versus genome, variant classes short reads miss and when a clinician is needed.
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 from raw FASTQ to a filtered, benchmarked and annotated single-sample VCF. Covers the tools, flags and quality numbers.
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.
A breakdown of real 2026 prices for SNP arrays, exomes, and whole-genome sequencing, what each one measures, and the storage, compute, and interpretation costs that follow.
Cloning tools like SnapGene and Geneious answer a different question than personal genome analysis. A concrete stack for going from FASTQ to annotated variants, with the tools, flags, file formats, and failure modes that matter.
Pharmacogenetic testing reliably predicts how you metabolize a few dozen drugs, and says almost nothing about which antidepressant will work. Here is what the genes cover, how to call the star alleles yourself from whole-genome data, and where the calls break.
A technical answer to which genetic sites are worth uploading raw DNA to, what the array file contains, and how to run the same analyses yourself with plink, bcftools, and an imputation server.
A working method for turning a raw Libre or Dexcom export into an ambulatory glucose profile, per-meal response curves, and metrics you can defend — including what the numbers can and cannot tell you.
A map of the multi-omics vendor market in four layers, what files each layer hands back, and how to evaluate a provider if you want data you can analyze yourself.
A working guide to pulling your lab data out of patient portals, normalizing it into one machine-readable table, computing derived values yourself, and telling a real change from assay noise.