Tag: variant-interpretation

A technically specific guide to choosing a whole-genome test: what files you should demand, what coverage and platform choices change, and how to analyze the data yourself.

A working guide to interpreting a genetic test result yourself, from verifying the file to knowing which findings need a clinician.

A working guide to reading a genetic report and your own VCF: variant nomenclature, ACMG classification, penetrance, coverage checks, and the commands to verify a call yourself.

A practical guide to the web servers and databases that hold up when you point them at your own genome, transcriptome, and proteomics data, and the steps you should run yourself instead.

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.

A genetic report is a short curated document about a handful of variants. The data underneath it is a CRAM and a VCF with about five million variants. Here is how to read both, and how to check the report yourself.

A working pipeline for going from a raw germline VCF to a short list of variants you can reason about: normalization, annotation, frequency and evidence filters, pharmacogenomic star alleles, and the failure modes that make most self-interpretation wrong.

A clinical whole-genome sequencing report is a short curated document about a handful of variants. The FASTQ, CRAM, and VCF behind it contain roughly four to five million. Here is what each one gives you and how to work with the data yourself.

A working guide to downloading ClinVar, matching it against your own VCF with bcftools, reading review-status stars correctly, and re-checking your variants as classifications change.

A plain answer to what genetic screening is, how it differs from diagnostic testing, and how to analyze a whole-genome VCF yourself: gene lists, callers, annotation, coverage failure modes, and when you need a clinician.

A moderate-penetrance cancer risk gene with a high VUS rate and a well-known set of analysis failure modes. How to find an ATM variant in your own WGS, classify it and know what the risk estimates do and do not say.

Clinical genome means two things: a sequencing test run and interpreted under lab standards, and ClinGen, the public resource that decides which genes and variants are real. Here is how both work and how to use them on your own data.

A working pipeline that classifies the variants in a personal VCF under ACMG/AMP rules, and why automated calls disagree with expert curation.

The three kinds of genetic testing for disease, which platform detects what, and how to work with the raw files yourself.

A working pipeline for searching the 4 to 5 million variants in your genome: annotate, filter to a few dozen candidates, then check each by hand.

What the five ACMG tiers mean on a BRCA report, how to pull the same calls from your own VCF, and what short-read sequencing misses.

What whole-genome screening in a healthy adult finds, what it misses, which files and tools to use, and where the interpretation gets hard enough that you need a clinician.

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 technical guide to preventive genetic testing: which variants have actionable evidence, why sequencing depth and file format matter, and how to work with your own VCF without over-reading it.

What whole-genome sequencing results contain, which files matter, how to interpret a variant classification, and where the analysis stops and a clinician starts.

What is in a VCF, how to filter it down to variants worth reading and how to read a single variant end to end. Covers the limits of a negative result.

A genome report is a filtered summary built on top of your variant calls. Here is what goes into one, which files matter more than the PDF, and what sequencing costs in 2026.

A working guide to genetic testing in asymptomatic people. Covers panel versus genome, variant classes short reads miss and when a clinician is needed.

A working guide to variant interpretation software: what each layer of the stack does, which tools we would use for a personal whole genome, and where the interpretation breaks.

A plain answer to what a genome is, what whole-genome sequencing produces, and how to work with your own FASTQ, CRAM, and VCF files without fooling yourself.

Why arrays and consumer health reports fail on rare variants, what to demand from a sequencing provider, and how to run QC and annotation on your own genome.

A worked walkthrough of what real DNA results look like: array text files, VCF lines field by field, coverage and callability checks, annotation with VEP, and what a 'positive' result does and does not mean.

A technical account of whole genome sequencing as a data product: coverage and chemistry, file formats, a pipeline we would run, and the questions a genome can and cannot answer.