A working guide to going from raw whole-genome reads to a filtered, annotated set of single-nucleotide variants you can query, including the tools, flags, quality checks, and the places this pipeline quietly fails.
A practical map of omics data types, the public databases worth querying, and how to structure your own multi-omics data so annotation queries return something useful.
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 step-by-step WGS pipeline for a single human genome: alignment with bwa-mem2, variant calling with DeepVariant, structural variants with Manta, annotation with VEP, and the QC numbers that tell you whether any of it worked.
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.
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.
How to tell Ensembl the database from ensemble methods in bioinformatics, then use both on your own whole-genome VCF: offline VEP annotation, plugin scores, and consensus calling across pipelines.
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.
A step-by-step pipeline for taking a 4-5 million variant single-sample VCF and reducing it to a few dozen candidates, with the actual bcftools, ANNOVAR, and slivar commands, plus where the filters lie to you.
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 setup for looking up any SNP in your own sequencing data. Covers indexing the VCF, annotating with rsIDs and verifying genotypes in the reads.
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 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 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 working guide to calling, normalizing, annotating, and querying SNPs from your own sequencing or array data, with the specific tools, flags, and failure modes that matter.
A working pipeline for annotating a personal whole-genome VCF: normalize with bcftools, annotate with Ensembl VEP offline, join custom datasets with vcfanno or slivar, then filter down to something you can read.
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 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 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.