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 palm-sized nanopore sequencer will sequence your DNA on your desk, but getting a usable human genome out of one takes several flow cells, a GPU, and a clear view of what long reads do well and badly. Here is the realistic path, with formats, inputs, and numbers.
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.
A blood panel for athletes and the timing rules that make its numbers interpretable. Covers reference change arithmetic and exporting lab data.
The FASTQ layout, naming and read structure Cell Ranger expects. Covers producing the files from BCLs, verifying them and a run that finds nothing.
DNA age tests read cytosine methylation at a few hundred CpG sites and regress it onto chronological age. Here is how the assay works, which clocks are worth running, what the error bars really are, and how to process the raw data yourself.
Heavy lifting and long endurance work can push ALT and AST into the hundreds with a healthy liver. Here is how to separate muscle-derived enzyme release from hepatic injury using GGT, CK, ALT/AST ratio, and a controlled retest.
A consumer epigenetics kit measures DNA methylation at CpG sites on an array, then feeds those values into a regression model that outputs an age-like number. Here is what the assay does, what the error bars are, and how to work with the raw IDAT files yourself.
A line-by-line breakdown of exome sequencing prices in 2026: raw-data-only versus clinical CLIA reports, what drives the spread, and what the files look like when they land.
A working guide to array genotyping versus whole-genome sequencing, the files you get, the tools to run on them, and the failure modes that make confident-looking results wrong.
Why total testosterone and SHBG rise together, how to work out whether your free testosterone is genuinely high, low, or normal, and which measurements are worth paying for.
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 working pipeline for taking a personal WGS VCF into R: GDS conversion, quality metrics, ancestry PCA against 1000 Genomes, variant annotation, and polygenic score computation, with the failure modes that silently corrupt each step.
A working guide to calling, normalizing, annotating, and querying SNPs from your own sequencing or array data, with the specific tools, flags, and failure modes that matter.
A practical walkthrough for taking a whole-genome VCF, keeping only the variants your caller flagged as PASS, normalizing them, and flattening the result into a table you can query. Includes the failure modes that silently drop real variants.
A working list of blood biomarkers that change decisions, why panel size is a poor proxy for value, and how to handle biological variation so you don't chase noise.
Most online biological age calculators are quizzes. Here is what the published estimators take as input, how to compute them from your own blood panel or methylation array, and how much weight the output deserves.