Tag: annotation

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 working pipeline that turns raw CNV calls from a personal genome into an annotated and prioritized table, with visual confirmation.

A working pipeline for human exome data with BWA-MEM, GATK HaplotypeCaller and VEP, with the flags that matter and the failure modes.

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 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 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 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.

A working guide to rsIDs: what they mean, how to annotate your own VCF against dbSNP, and how to handle merges and withdrawn records.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

A working pipeline from FASTQ to an annotated variant list, with the QC steps and the filters that make 4.5 million variants readable.

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.

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 that turns a personal whole-genome VCF into an annotated variant table you can query, using VEP, gnomAD and ClinVar.

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 practical workflow for going from an rsID to a genotype you can trust. Covers resolving coordinates, querying your VCF and checking the reads.

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 working guide to turning RNA-seq quantifications into an annotated, versioned gene table. Covers choosing an annotation, tx2gene and biotypes.

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 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.