172 posts
You can collect a sample at home, but you cannot get clinical-grade whole-genome data from a benchtop device. Here is what each option produces, what the files look like, and how to analyze them yourself.
A concrete software stack for calling, checking and annotating SNPs from your own sequencing data, with the reference genome decision explained.
A working stack for going from a personal WGS VCF to a reviewable variant list: Ensembl VEP for annotation, slivar for filtering, InterVar for ACMG classification, and the checks that keep the whole thing from lying to you.
A practical ranking framework for consumer and clinical whole genome sequencing: what coverage, platform, file formats, and data rights to demand, and how to verify what you received.
A technical comparison of consumer genotyping arrays, exomes, short-read whole genomes, and long-read sequencing, judged on the data files you receive and what you can compute from them.
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 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.
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 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.
A practical guide to opening and inspecting BAM alignment files: samtools on the command line, IGV for visual review, and the checks worth running before you trust a variant call.
30x means an average of 30 reads covering each base of your genome, which is enough for confident germline SNV and small-indel calling across roughly 90-95% of the reference. Here is the coverage math, the files you should insist on, and the QC steps we would run before trusting a single variant.
How to get a useful AI-generated report from your own DNA data, which file formats matter, and where the interpretation breaks down.
A practical guide to DeepMind's AlphaGenome model: what it outputs, how to call the API on your own variants, and how to check its predictions.
For germline variant calling, 30x is sufficient and 100x adds little. Deep coverage pays off for mosaicism, mitochondrial heteroplasmy, structural variants, and cell-free DNA. Here is how to tell which case you are in, and what to check in your own BAM.
A plain account of what sequencing your own genome produces, which files and tools matter, and where the genome stops being informative.
A practical guide to setting up the Integrative Genomics Viewer for personal whole-genome, RNA-seq, and copy-number data, including file indexing, pileup interpretation, batch scripting, and the failure modes that produce false confidence.
An AA call at rs1801133 is the same variant as 677TT, reported on the other strand. How to verify it in your own data, and what to measure next.
A practical guide to obtaining your own genome sequence: what to order, what coverage and file formats to insist on, what it costs, and how to pull reference gene sequences from public databases.
A step-by-step pipeline that turns your own whole-genome data into a normalized and annotated variant set, with the tools and filters we would use.
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 breakdown of WGS pricing by depth, platform, and deliverable, from $200 research-core genomes to $5,000 clinical reports, plus the downstream storage and compute costs nobody quotes.
The public genome sites worth using, what each is good for, and how to go from a reference genome to a file that describes your own.
A practical guide to the three things people mean by genome mapping, the aligners and flags we would use on a personal whole-genome dataset, and what the compute costs.
A working pipeline for taking raw whole-genome sequencing output (FASTQ, CRAM, VCF) through quality control, annotation, and variant triage, with the specific tools, flags, and thresholds we would use and the failure modes to watch for.
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 working pipeline for calling deletions, duplications, inversions, and insertions from your own short-read whole-genome sequencing data, including which callers to run, how to merge them, and how to judge whether a call is real.
A practical guide to annotating your personal VCF with population allele frequencies from gnomAD, 1000 Genomes, and population-specific resources, computing frequencies yourself, and using Hardy-Weinberg equilibrium as a quality-control filter.
A practical walkthrough of what a sequencing lab hands you, how to verify it before you trust it, and how to go from raw reads to an annotated variant table you can query yourself.
A practical walkthrough of working with raw sequencing reads: reading the format, checking quality, aligning to a reference, and knowing when the data is good enough to call variants.
A technical comparison of genotyping arrays, exome sequencing and whole-genome sequencing, with the quality metrics to demand from each.
A pharmacogenetic test genotypes a small set of drug-metabolism genes and converts them into star-allele diplotypes and metabolizer phenotypes. Here is what that pipeline looks like, which genes are hard to call, and what the result does and does not tell you.
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 technical walkthrough of the NIH All of Us genomic dataset: sequencing specs, callset formats, Researcher Workbench access tiers, and what a participant receives from their own DNA.
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 practical software stack that takes a personal 30x whole genome from FASTQ to annotated variants, with the failure modes that waste days.
A direct account of WGS applications, from rare-disease diagnosis to pharmacogenomics, plus the file formats, tools, and flags you need to analyze a 30x genome yourself.
A whole genome array genotypes or copy-number-profiles preselected positions across all 24 chromosomes. Here is what the platforms resolve, the file formats you get, and how the data compares to 30x whole-genome sequencing.
Genome mapping means at least four different things: historical linkage maps, aligning your reads to a reference, optical genome mapping for structural variants, and the colloquial sense of getting your genome sequenced. Here is how to tell them apart, what each costs, and the pipeline we would run on your own data.
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.
What a genome analyzer is at the file level, from FASTQ to CRAM to VCF, and how to measure your own error floor against GIAB.
How to go from a whole-genome VCF to CPIC-grade star allele diplotypes for CYP2C19, CYP2D6, DPYD, TPMT and others, which callers to run, and the failure modes that make a clean-looking result wrong.
Array-based consumer kits genotype a few hundred thousand preselected sites accurately and everything else badly. If you want data you can analyze yourself, you want 30x whole-genome sequencing with FASTQ, BAM/CRAM, and gVCF returned.
A working pipeline for taking 30x PCR-free FASTQs to aligned CRAM, a filtered and annotated variant set, structural variants, and the QC numbers that tell you whether any of it is trustworthy.
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 practical guide to loading your own whole-genome alignments, variant calls, and RNA coverage into a local genome browser, indexing the files correctly, and querying annotation programmatically instead of clicking through a web UI.
Hemoglobin is a concentration, not a mass, and that fact explains most of the confusion around a single result. Here is what the assay does, how much of a change is real, and what to pair it with.
MCHC is a computed ratio of hemoglobin to hematocrit, and most high values are analyzer artifacts rather than biology. How to tell the difference using your own CBC history, and the few red cell conditions that genuinely raise it.
Where polygenic tests come from, how to compute scores yourself from a VCF, and the failure modes that silently corrupt a score.
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 practical pipeline for taking raw DNA data — array export, VCF, or CRAM — and turning it into something you can query, annotate, and trust, plus what the file cannot tell you.
A working guide to loading your own BAM/CRAM, VCF, and RNA-seq files into IGV and igv.js: reference builds, indexing, annotation tracks, batch screenshots, and the visual checks that catch bad variant calls.
How to pull rs1801133 out of your own VCF without a strand error, what the C677T variant does to the enzyme, and which blood measurements tell you whether it matters for you.
A whole genome is every base of your DNA, read at some depth, delivered as files. Here is what the files contain, which tools to run on them, and where short-read WGS quietly fails.
A practical pipeline for taking raw whole-genome sequencing data from FASTQ to annotated variants, structural variants, pharmacogenomic diplotypes, and polygenic scores, with the QC steps that decide whether any of it means anything.
What the A1298C variant is, how to pull it correctly out of a VCF or a consumer array file without getting burned by strand, and which blood measurements tell you more than the genotype does.
What a personal genome service delivers, which file formats to insist on, and the limits of interpreting a genome in a healthy person.
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.
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 guide to going from raw sequencing reads to an annotated, filtered variant list you can reason about: file formats, strand conventions, QC thresholds, annotation commands, and the failure modes that produce confident nonsense.
A concrete stack for analyzing your own whole-genome data: alignment, variant calling, annotation, pharmacogenomics, and polygenic scores, with the flags, file sizes, and failure modes that matter.
A working pipeline for turning 30x whole-genome sequencing reads into an aligned, called, benchmarked, annotated and filtered variant set, plus the triage rules that keep the short list short.
Genotyping arrays, exomes, and whole genomes fail in different ways and in different places. Here is where the errors live, how to check your own data, and what accuracy numbers mean per-variant rather than per-test.
A working pipeline for taking raw whole-genome sequencing reads of your own DNA through alignment, variant calling, annotation, and a queryable table you can hand to an analysis agent.
Whole exome sequencing covers roughly 1-2% of the genome at high depth. What the files contain, which tools we use, and where the coverage gaps are.
A whole-genome sequencing sample is usually 4–10 mL of EDTA blood or a saliva kit; what comes back is 80–120 Gbp of reads, a CRAM, and a VCF with roughly 4–5 million variants. Here is how to judge the sample, the files, and the costs.
A working pipeline from FASTQ to an annotated variant list, with the QC steps and the filters that make 4.5 million variants readable.
A genome reader is really three things: the sequencer that produces reads, the aligner and variant caller that turn reads into a genome, and the browser that lets you look at it. Here is what each step produces, which tools we would run, and where they fail.
A working pipeline from FASTQ to annotated VCF for one human genome, with DeepVariant and GATK calling and benchmarking against GIAB truth sets.
A practical comparison of whole-genome and whole-exome sequencing, with the pipeline, tools, flags, and failure modes we would use to analyze either one.
The reagent cost of a 30x human genome is now in the low hundreds of dollars, but the price you pay depends on coverage, data delivery, and whether the lab is CLIA-certified. Here is the full cost stack, including storage and compute.
A step-by-step guide from raw FASTQ to an annotated variant table on your own machine, covering alignment, calling, QC thresholds and annotation.
The three kinds of genetic testing for disease, which platform detects what, and how to work with the raw files yourself.
How exome capture works, the analysis pipeline for your own FASTQs, and the failure modes that decide whether an exome is the right assay.
A step-by-step guide to taking raw whole-genome FASTQs through alignment, variant calling, validation, and annotation, with the specific tools, flags, and quality thresholds we use.
A working end-to-end path from raw FASTQ to an annotated, QC'd variant set using GATK, DeepVariant, and VEP, with the flags, file sizes, runtimes, and failure modes that matter.
A SNP microarray genotypes a fixed panel of several hundred thousand predefined positions and, from probe intensities, infers copy number and runs of homozygosity. Here is the data it produces, the tools to process it, and the variant classes it cannot see.
What structural variants are, which callers to run on short-read and long-read whole-genome data, the flags that matter, and how to tell a real call from an alignment artifact.
A working guide to variant effect predictors: what they score, how to run VEP with the right plugins, and which scores to trust.
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.
A working pipeline for a personal whole genome: alignment, variant calling, annotation, and the QC steps that catch the errors most people miss. Tool names, flags, file formats, and failure modes.
A working pipeline that turns a personal whole-genome VCF into an annotated variant table you can query, using VEP, gnomAD and ClinVar.
A working list of the tools that matter for analyzing your own genome, transcriptome, proteome, and biomarker data, with formats, flags, and the failure modes that bite.
A working pipeline for whole-exome data from FASTQ to VCF, with the filters that matter and the places where exome capture loses to a whole genome.
What whole exome sequencing covers, where capture fails, and how to check the quality of an exome you already own.
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.
A working pipeline that turns 30x whole-genome FASTQ or CRAM into a filtered and annotated variant call set, with the tools and checks we would use.
A technical comparison of array-based consumer DNA tests, consumer whole-genome sequencing, and clinician-ordered panels: file formats, error modes, prices, and what to do with the raw data.
A price breakdown of array genotyping, panels, exomes, and whole genomes, what each price includes, and what you get back as files.
A technical review of consumer and clinical whole genome sequencing: coverage depth, file formats, variant calling limits, and what the data can and cannot tell you.
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 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.
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.
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.
Two different tests share the name methylation testing: genotyping of one-carbon metabolism genes like MTHFR, and direct measurement of DNA methylation across the genome. This explains what each one measures, how to extract the data yourself, and where the interpretation stops.
A1c and glucose disagree when either your glucose sampling is unrepresentative or your red cell biology and hemoglobin assay distort the glycation measurement. Here is how to separate the two with CGM data, a CBC, an assay method check, and a look at your genome.
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.
A practical walkthrough of going from FASTQ, BAM/CRAM, and VCF files to an actual nucleotide sequence you can read, translate, and check against the reads it came from.
A comparison of the companies that will sequence or map your genome, what data each one hands back, and how to judge an offer by coverage, read length, and file deliverables rather than marketing copy.
What whole-genome sequencing results contain, which files matter, how to interpret a variant classification, and where the analysis stops and a clinician starts.
A working guide to building the handful of plots that matter for a personal molecular dataset: coverage tracks, variant pileups, copy-number profiles, expression MA and volcano plots, and a genome-wide circular summary. With real commands, file formats, and the failure modes that produce misleading pictures.
What array, exome, and whole-genome testing each measure, which files to insist on, and a concrete pipeline for calling and annotating your own variants.
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 kit is worth buying only if it ships you 30x PCR-free whole-genome sequencing and the underlying files. Here is the spec sheet, the QC commands to run on delivery, and what drugstore DNA kits measure.
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 step-by-step guide to annotating a personal WGS VCF with Ensembl VEP. Covers cache setup, the plugin stack and consequence picking.
A working pipeline for going from RNA-seq FASTQs to junction counts, PSI values, and a shortlist of splice-altering variants from your own genome, with the tools and parameters we would use.
A practical guide to DNA analysis tools: what works on a 23andMe-style genotype file, what requires FASTQ/BAM/VCF from whole-genome sequencing, and the commands we would run at each step.
A practical comparison of DeepVariant, GATK HaplotypeCaller, bcftools mpileup, Strelka2, and Clair3 for calling SNPs from your own whole-genome data, with commands, benchmarks, and the failure modes that matter.
A practical spec for buying whole-genome sequencing: coverage, library prep, file formats, the QC you should run yourself, and what WGS still misses.
A working guide to setting up an R and Bioconductor project around a personal molecular profile: loading your VCF, quantifying your own RNA-seq across timepoints, normalizing proteomics, and joining biomarker and glucose time series into one analyzable object.
A step-by-step guide to exporting raw genotype files from Ancestry, 23andMe, and MyHeritage, converting them to a reference-correct VCF, lifting to GRCh38, imputing, and understanding what an array file can and cannot tell you.
A working stack for analyzing your own whole-genome sequencing data: alignment with BWA-MEM2 or Dragen, variant calling with DeepVariant, annotation with VEP, and interpretation limits you should respect.
A working guide to comparing genomes — your own against the reference and against benchmark truth sets, assembly against assembly, and hundreds of bacterial genomes at once — with the tools, flags, and failure modes that matter.
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 practical guide to resolving rsIDs against your own genotypes: normalizing a VCF, querying dbSNP and Ensembl, annotating with consequence, frequency, and ClinVar, and reading the result without over-interpreting it.
A step-by-step guide from raw FASTQ to a filtered, benchmarked and annotated single-sample VCF. Covers the tools, flags and quality numbers.
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.
The All of Us Research Program stopped returning research DNA results and removed ancestry and trait reports from participant accounts. What you were given, what you never had, and how to get raw genomic data you control.
A practical guide to privacy in DNA testing: why genomes resist anonymization, what to ask a provider before you send a sample, and how to hold raw sequence data safely once you have it.
Cloning tools like SnapGene and Geneious answer a different question than personal genome analysis. A concrete stack for going from FASTQ to annotated variants, with the tools, flags, file formats, and failure modes that matter.
A technical answer to which genetic sites are worth uploading raw DNA to, what the array file contains, and how to run the same analyses yourself with plink, bcftools, and an imputation server.
A map of the multi-omics vendor market in four layers, what files each layer hands back, and how to evaluate a provider if you want data you can analyze yourself.
A palm-sized nanopore sequencer will sequence your DNA on your desk, but getting a usable human genome out of one takes several flow cells, a GPU, and a clear view of what long reads do well and badly. Here is the realistic path, with formats, inputs, and numbers.
A line-by-line breakdown of exome sequencing prices in 2026: raw-data-only versus clinical CLIA reports, what drives the spread, and what the files look like when they land.
A working guide to array genotyping versus whole-genome sequencing, the files you get, the tools to run on them, and the failure modes that make confident-looking results wrong.
A practical walkthrough for taking a whole-genome VCF, keeping only the variants your caller flagged as PASS, normalizing them, and flattening the result into a table you can query. Includes the failure modes that silently drop real variants.
A practical guide to ordering whole-genome sequencing for yourself: what coverage and read length to insist on, which files to demand, how to check quality yourself, and what the data can and cannot tell you.
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.
A concrete stack for working with personal genome, transcriptome, proteome, and biomarker data: file formats, tools, commands, and where each approach breaks down.
Whole exome sequencing reads the ~1-2% of your genome that codes for protein. What it finds, what it silently misses, and how to read the files yourself.
Why array-based consumer kits cap out fast, what a 30x whole genome resolves and which files to request. Includes a pipeline for analyzing the data.
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.
Why genotyping arrays like 23andMe and AncestryDNA answer a narrow question, what whole-genome sequencing gives you instead, and how to work with the files yourself.
A technical comparison of consumer genotyping arrays, clinical panels, and whole-genome sequencing, including the raw file formats each returns and what you can do with them.
A working pipeline for analyzing your own DNA with open source tools, from array raw data to whole-genome FASTQ, including the annotation databases, the commands, and the failure modes that produce wrong answers.
What a WGS service delivers, what the files look like, what coverage and platform choices change, and how to tell a real provider from a reseller.
A technical answer to what makes genetic testing private: who holds the raw files, what the consent terms permit, how re-identifiable the data is, and how to store and analyze a genome yourself.
How to genotype ABO and RhD from whole-genome sequencing data, and where short reads fail. A genomic call is not a transfusion-grade answer.
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 breakdown of the genomic testing market by category — clinical labs, direct-to-consumer arrays, research-grade sequencing providers, and tumor profiling — with the file formats, coverage depths, and failure modes that decide whether the data is useful to you.
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.
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.
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.
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.
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 practical comparison of Oxford Nanopore, Illumina, Element, PacBio, and Sanger for sequencing one person's genome: why we would choose nanopore long reads, what to ask a provider for, and the pipeline we would run on the output.
A FoundMyFitness report is a literature annotation layer on top of a consumer genotyping array. Here's how to audit the underlying file, rebuild the analysis yourself, and understand where array data runs out.
A working guide to the free DNA upload sites worth using, what each one does with your file, and how to run the same annotation, polygenic scoring, and relative-matching analyses yourself on your own machine.
A step-by-step method for pulling rs1801133 and rs1801131 out of a 23andMe, AncestryDNA, or WGS file yourself, getting the strand right, and understanding what the result does and does not tell you.
A working guide to interpreting raw genotype files from 23andMe or Ancestry, converting them to VCF, annotating variants, and knowing where array data stops being useful.
A working guide to taking a 23andMe/Ancestry export or a whole-genome FASTQ, converting it to an annotated VCF, filtering it sensibly, and knowing which results are real.
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.
Celebrate World DNA Day on April 25. Learn its history, significance, and get ideas for your lab to engage with the future of DNA engineering and genomics.
Explore next generation DNA sequencing technologies. Learn about platforms, workflows, and selection criteria for biotech & pharma R&D applications in 2026.
A whole exome sequencing review for R&D teams. Covers WES methodology, bioinformatics, limitations and data integration with models.
An overview of how in-silico approaches are accelerating the drug development pipeline, reducing costs, and improving success rates in clinical trials.