Tag: genomics

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, how to read a single variant end to end, and what a negative result does not rule out.

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 your personal WGS VCF with Ensembl VEP: cache setup, plugin stack (AlphaMissense, CADD, SpliceAI, LOFTEE, dbNSFP), consequence picking, and turning millions of annotated rows into something you can query.

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: panel vs genome, the variant classes short-read pipelines silently miss, the annotation stack we'd run on our own VCF, and where a clinician is non-optional.

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 to going from raw FASTQ to a filtered, benchmarked, annotated single-sample VCF, with the tools, flags, and quality numbers we would use ourselves.

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, which files to demand, and the pipeline we would run on our own 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, which variants matter, where short reads fail, and why 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, not of a sequencing assay. Here is what HIPAA does and does not cover for genetic data, what to read in a lab's contract instead, and how to take custody of your own FASTQ, BAM, and VCF 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.

Get a comprehensive whole exome sequencing review for R&D teams. Explore WES methodology, bioinformatics, limitations, & 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.