A step-by-step pipeline for small RNA sequencing data, from adapter chemistry and UMI handling through hierarchical annotation, isomiR-level quantification, and differential expression, with the biofluid-specific failure modes that break naive runs.
A working guide to reading, normalizing, filtering, annotating, and querying a Variant Call Format file from whole-genome sequencing, including how to get a slice of it into a spreadsheet without breaking it.
A breakdown of WES pricing: research-grade versus CLIA-clinical, what drives the per-sample cost, how trios are billed, and why the sequencing is the cheap part of the project.
Consumer 30x WGS runs $300-600, clinical CLIA-reported WGS runs roughly $1,000-3,000 out of pocket, and the difference is depth, deliverables, and interpretation rather than sequencing chemistry. Here is how to read a price and verify what you received.
A breakdown of consumer, research-core, and clinical whole genome sequencing prices, what each tier gets you in files and coverage, and where the hidden costs sit.
An essay prompted by Alan Kay's 2011 talk on why the Internet and living systems scale while most software does not, with his numbers checked against real sources, and what the argument implies for a personal molecular data system built from a genome, a blood panel, and a glucose trace.
A working guide to diagnosing and handling batch effects in bulk RNA-seq, from design and TMM normalization through limma, ComBat-seq, and RUV, with the checks that tell you whether the correction helped.
What biological age tests measure and which clocks are worth running. Covers computing them from IDATs and a blood panel, and how much is noise.
An essay prompted by Carolyn Bertozzi's lecture on bioorthogonal chemistry: what the reactions are, the second-order rate constants that decide what can be imaged in a living animal, and why the glycome is still a missing data layer in most molecular profiles.
How to use R and Bioconductor as the annotation, statistics, and integration layer for a personal molecular dataset: VCFs, RNA-seq counts, proteomics, and continuous glucose data, with the parts you should not do in R.
Why the data structure chosen for a genome, a variant set, an expression matrix, or a phylogeny decides which questions can be asked and what runs in reasonable time, prompted by a Boundary talk on coding agents.
A technical account of the privacy properties of consumer and clinical DNA testing: what data exists, who holds it, what re-identification attacks work, and a concrete setup for keeping your own sequence data under your control.
In the US, FreeStyle Libre 2 and 3 still require a prescription; Libre Rio, Dexcom Stelo, and Abbott Lingo do not. What each one costs, how they differ, and how to get the raw glucose data out.
Genetic Genie's methylation panel reads about two dozen genotypes out of a consumer array file. This guide shows how to extract and verify those calls yourself, what they can and cannot tell you, and how to measure actual DNA methylation if that is what you were after.
Pharmacogenomic panels marketed for ADHD medication selection rest on thin evidence for stimulants. Here is what the genotypes predict, which variants have real pharmacokinetic support, and how to extract them from your own sequencing data.
A postprandial reading of 97 mg/dL is a normal, unremarkable value in a person without diabetes. Here is what the number tells you, what it does not, and how to read postprandial glucose properly from CGM or fingerstick data.
How to run a genome-wide association study in R end to end, from VCF to QC to association testing to polygenic scores, and what you can and cannot do with a single genome.
HIPAA compliance is a property of the entity holding your data. No sequencing assay has it. What HIPAA covers, and how to take custody of your files.
Dehydration concentrates plasma by a few percent, which is far too small to explain most ALT and AST elevations. Here is what usually causes them, how to draw a clean measurement, and how to read enzymes against your own genomic and proteomic baseline.
A working pipeline for taking Olink, SomaScan, or DIA-NN output into R: QC, missingness, normalization, and longitudinal within-person modeling of a single individual's plasma proteome.