Blog

You can draw blood on any cycle day. The question is which analytes move with cycle phase, how much, and how to record the phase so your own longitudinal data stays interpretable.

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 ordering whole-genome sequencing for yourself: what coverage and read length to insist on, which files to demand, how to check quality yourself, and what the data can and cannot tell you.

Levine's PhenoAge is a nine-marker blood algorithm you can compute yourself in about twenty lines of Python. Here is the formula, the unit conversions that break it, and how it compares to the methylation clocks from the same lab.

A plain answer to what a genome is, what whole-genome sequencing produces, and how to work with your own FASTQ, CRAM, and VCF files without fooling yourself.

A concrete stack for working with personal genome, transcriptome, proteome, and biomarker data: file formats, tools, commands, and where each approach breaks down.

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

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.

The 56-biomarker performance panels sold as a full-body MOT test the wrong thing in the wrong way. Here is the panel we would order, how to control preanalytics, and how many repeats you need before a number means anything.

A working pipeline for annotating a personal whole-genome VCF: normalize with bcftools, annotate with Ensembl VEP offline, join custom datasets with vcfanno or slivar, then filter down to something you can read.

Whole exome sequencing reads the ~1-2% of your genome that codes for protein. What it finds, what it silently misses, and how to read the files yourself.

A working guide to verifying, converting and analyzing a personal 30x whole-genome file. Covers the reference build, CRAM to BAM and coverage QC.

Why array-based consumer kits cap out fast, what a 30x whole genome resolves and which files to request. Includes a pipeline for analyzing the data.

Why arrays and consumer health reports fail on rare variants, what to demand from a sequencing provider, and how to run QC and annotation on your own genome.

Why genotyping arrays like 23andMe and AncestryDNA answer a narrow question, what whole-genome sequencing gives you instead, and how to work with the files yourself.

A practical guide to direct-access lab testing: which channels will draw you without a referral, how to design a panel that answers a question, how to control preanalytical variables, and how to get the results out as structured data you can analyze.

DNA.Land stopped accepting uploads and shut down its reports. This guide shows how to take the same raw genotype file, convert it to VCF, impute it, annotate it, and decide which remaining upload sites are worth your data.

Self-pay pharmacogenomic panels run roughly $200-$500, Medicare and commercial plans cover them only under narrow conditions, and a single whole-genome sequence answers the same questions permanently. Here is the pricing, the billing codes, and how to run the interpretation yourself.

A technical comparison of consumer genotyping arrays, clinical panels, and whole-genome sequencing, including the raw file formats each returns and what you can do with them.