What a CGM measures, which sensor to buy over the counter, how to get the raw data out as CSV, and the metrics and failure modes that decide whether the two weeks of data mean anything.
A gene panel is a subset of an exome, and an exome is about 1.5% of a genome. Here's how the three differ in coverage, cost, file sizes, and what you can re-analyze later.
A genome explorer is a coordinate browser plus a query layer over your own alignments and variant calls. Here is the stack we would use on a personal 30x whole genome, with the commands, file formats, and failure modes.
"Genome manager" usually means a vendor's web portal for your sequencing results. Here is what that portal does, what it hides, and how to run your own catalog of VCFs, BAMs, expression matrices, and lab panels with tools you already know.
A step-by-step guide to taking 30x WGS FASTQs from a vendor portal to an annotated, QC-checked variant call set using spot instances, bwa-mem2, DeepVariant, and Nextflow. Includes real costs, memory requirements, and the failure modes that waste a weekend.
A working guide to hs-CRP, ESR, fibrinogen, ferritin, and IL-6: what each marker tracks, how much of a single value is noise, how to handle samples so cytokine numbers mean something, and where affinity proteomics panels go beyond the standard five.
What post-meal glucose looks like in people without diabetes, where the 140 and 180 mg/dL thresholds come from, and a protocol for extracting real numbers from your own CGM export.
A technical guide to getting your DNA sequenced without handing the data to a consumer platform: what each tier of testing can resolve, where the error rates live, and how to store and analyze the files yourself.
How to take a raw whole-genome VCF, normalize it correctly, annotate it with VEP plus population and clinical databases, and flatten it into a table you can query in seconds.
How to download the raw genotype file, convert it to VCF against a reference, lift it to GRCh38, impute, score, and know which conclusions the array can and cannot support.
A direct look at blood-chemistry clocks, methylation clocks, and plasma proteins: which markers carry the signal, how to compute biological age from your own lab data, and how much of the number is noise.
A technical account of what blood-based cancer detection measures, where the numbers come from, and how to read cfDNA methylation tests, tumor markers, and routine CBC results without fooling yourself.
A fitness or gym blood panel is only interpretable if you control what you did in the 72 hours before the draw. Here is the panel we would order, the confounders that corrupt it, and how to analyze the results over time.
A technical read on GRAIL's methylation-based multi-cancer early detection test: how the assay works, stage-specific sensitivity, the positive predictive value math for your age, and why the NHS trial result matters.
Most consumer wellness DNA tests report array genotypes and low-evidence trait associations. Here is what a genome can and cannot tell a healthy person, which file formats and tools matter, and where the real signal is.
A working guide to lifting positions, VCFs, intervals, and probe manifests between GRCh37, GRCh38, and T2T-CHM13, including which tool to use for each file type and how to tell when the lift silently went wrong.
A glycan age score is a regression of IgG N-glycan peak ratios onto chronological age. Here is what the assay measures, how the number is built, where it is informative, and how to work with the raw peak data yourself.
MTHFR variants are common, their effect on depression risk is small, and the clinically useful signal sits in downstream biochemistry rather than the genotype. How to find your own genotype in a VCF and what to measure instead.
A practical map of the software that turns raw multi-omics files into results you can query, with the commands, formats, and failure modes that matter for a single person's data.
A plasma proteomics report measures the relative abundance of hundreds to thousands of circulating proteins. Here is what the numbers mean, how the three main platforms differ, and how to analyze your own data without fooling yourself.