Blog

A technical read of the comprehensive metabolic panel: the fourteen analytes, the derived quantities worth computing, and the common errors.

A SNP is a single base position in the genome where people carry different letters. This is how those positions get called from sequencing data, filtered, annotated, and read without over-reading them.

How FDA classification and special controls compare with IVDR performance evaluation in the evidence a single in vitro diagnostic has to generate.

A practical guide to genome visualization tools for your own sequencing data, from IGV and the UCSC browser to igv.js, with the files each one needs.

85 mg/dL fasting sits comfortably inside the normal range. The more useful question is how much your own fasting glucose varies day to day, which a single venous draw cannot answer and CGM can.

A working guide to gene set enrichment analysis on personal RNA-seq: building a defensible ranked list with DESeq2, running fgsea against MSigDB, reading the enrichment plot, and using GSVA when you have no contrast.

A working guide to aligning and quantifying your own RNA-seq data: reference choice, STAR and Salmon parameters, strandedness, quality thresholds, and the failure modes that silently distort expression estimates.

A working guide to rsIDs: what they mean, how to annotate your own VCF against dbSNP, and how to handle merges and withdrawn records.

A fasting glucose of 93 mg/dL sits comfortably in the normal range. The more useful question is what the rest of your glucose curve looks like, and how to measure it yourself.

A practical guide to continuous glucose monitoring for prediabetes: which device to use, how long to wear it, and which metrics to compute.

How Regulation (EU) 2017/745 governs device claims, label particulars, sterile packaging text and the content of instructions for use.

A technical guide to building a baseline blood panel you can track over years: which analytes to order, how to control preanalytical variation, and the math that tells you whether a change is real.

How to choose a blood test cadence from analyte kinetics and biological variation, how to tell a real change from assay noise using reference change values, and how to structure the data so you can analyze it yourself.

A working guide to turning FASTQ files from your own blood RNA into gene-level expression estimates you can interpret, with the quantification commands, normalization choices, and the failure modes that make people over-read their data.

A measurement-first look at the common MTHFR variants: what the genotype does to enzyme activity, how to pull it from your own VCF, and which blood markers carry real signal instead of a symptom list.

What home genome kits measure, which file formats to insist on, and which variant classes they miss.

A working guide to analyzing your own whole-genome sequence: the file formats, the pipeline we would run, and what the calls support.

A practical guide to choosing between untargeted and targeted metabolomics providers, what you get in the deliverable, and how to process the data yourself in mzML, XCMS, and MetaboAnalyst.

In the US, most clinical genetic tests require a provider's order, but the requirement is procedural and there are several ways to satisfy it. Here is how ordering works, what to specify, and what files to demand back.

A working pipeline for taking personal bulk RNA-seq from raw FASTQ files to quantified expression, quality checks, reference comparison, and longitudinal analysis, with the specific tools, flags, and failure modes.