A practical, opinionated toolchain for analyzing your own genome, transcriptome, proteome, and biomarker data: which tools to use at each layer, the flags that matter, and where the failure modes are.
A technical comparison of clinical PGx panels, psychiatry combinatorial reports, and calling star alleles from your own whole-genome data, including the tools, flags, and genes that break.
A quantitative high-sensitivity CRP assay, repeated on a schedule and paired with a CBC differential, is the only at-home inflammation measurement worth acting on. Here is how to collect it, when to collect it, and how to analyze the series.
There is no single best vendor. What matters is assay methodology, longitudinal consistency, and whether you can get structured data out. Here is how we compare the options and what a 100-biomarker panel really contains.
A specific panel for athletes, the pre-analytical protocol that makes the numbers comparable over time, and how to compute your own reference intervals instead of relying on population ranges.
A practical comparison of the Abbott and Dexcom sensors for training use, plus how to get the raw glucose stream out and align it with power and heart rate files.
A technical comparison of over-the-counter continuous glucose monitors in the US, including accuracy figures, data export paths, and how to analyze the resulting time series yourself.
How to choose a DNA methylation test by what data it returns, not by the clock names on the marketing page, and how to run the clocks yourself from raw IDAT files.
Pharmacokinetic genotyping of CYP2C19 and CYP2D6 has real clinical guidelines behind it. Panels that claim to predict which antidepressant will work do not. Here is how to get an accurate diplotype call from your own sequencing data.
How 21 CFR part 820 became the Quality Management System Regulation, which sections were removed or reserved, and where each requirement now sits.
A practical guide to QUAL, hard filters, and genotype masking with bcftools filter and bcftools view, including the failure modes that silently drop variants from your own genome.
A working guide to bcftools mpileup and bcftools call on a personal 30x whole-genome BAM, including filtering, normalization, BCF versus VCF, and where this pipeline will mislead you.
A precise account of the Benjamini-Hochberg step-up procedure: the arithmetic, the assumptions, the code, and the failure modes that matter when you run it on your own RNA-seq or proteomics data.
Upload-a-PDF analyzers mostly do OCR and template text. A better setup is structured lab data with units and LOINC codes, your own longitudinal baseline, and a general reasoning model you drive yourself.
A practical answer for people analyzing their own molecular data: use a frontier LLM as a coding agent over your files, use task-specific models for prediction, and verify everything at the file level.
How to choose an at-home C-reactive protein test, why the high-sensitivity assay matters, and how to analyze repeated CRP measurements without fooling yourself with biological noise.
What a manufacturer files under Regulation (EU) 2017/745: Annex II and III technical documentation, the route by class and how notified bodies review it.
A curated list of blood, genomic, transcriptomic, proteomic, and glucose markers that carry real information about one person, with the variability numbers and file formats you need to work with them.
A step-by-step method for deciding whether a failing FastQC module means your library is broken, your sequencer had a bad run, or the plot is simply behaving as designed.
A field-by-field guide to the eleven mandatory SAM/BAM columns, the optional tags that matter, and the commands to read, filter, and visualize your own alignments.