Genomic medicine and microbiology · BEGINNER LESSON
Whole-genome sequencing
Whole-genome sequencing (WGS) is a method for reading an organism’s entire DNA sequence in one run, so researchers can look for coding, noncoding, mitochondrial, and structural variants. In medicine, it measures genetic differences that may help explain disease, infection, or drug resistance.
What it is and what it measures
Whole-genome sequencing (WGS) is a sequencing method that examines an organism’s full genome, meaning all of its DNA. The main output is sequence data that can be checked for genetic variants, including changes in protein-coding regions, noncoding regions, mitochondrial DNA, and larger structural changes. In practice, WGS measures which sequence differences are present, and those differences may be compared with a person’s symptoms, an infectious outbreak pattern, or a drug-resistance phenotype, which is the observable effect of genes on a trait.
[1] Reinterpreting National Whole-genome Sequencing Data [2] Applications of Genome Sequencing in Infectious Diseases - PMC [3] A Systematic Review and Meta-Analysis - PMC
How it works in medicine
WGS helps clinicians and researchers by finding many kinds of variants at once instead of testing one gene at a time. In rare disease diagnosis, this broad view can shorten a long diagnostic journey, especially when phenotype-guided reanalysis is used. A phenotype is the set of observable features, such as symptoms and test findings, and variant prioritization means ranking genetic changes that best fit those features. In infectious disease, WGS can identify pathogen strains, track transmission chains, and detect resistance genes or mutations that may predict antimicrobial susceptibility, which is whether a microbe is likely to respond to a drug. In tuberculosis, WGS can also help distinguish relapse from reinfection.
[1] Reinterpreting National Whole-genome Sequencing Data [2] Applications of Genome Sequencing in Infectious Diseases - PMC [3] A Systematic Review and Meta-Analysis - PMC
Why interpretation is difficult
WGS produces large amounts of data, so analysis depends on bioinformatics, which means using computational tools to process biological sequence data. Interpretation can be limited by high cost, missing infrastructure, and too few trained specialists. Results can also be uncertain because some variants are hard to classify, because databases do not represent every population equally, and because genotype and phenotype do not always match. In infection testing, sequence detection alone cannot tell whether DNA came from a live pathogen, a dead organism, a harmless colonizer, or contamination. In resistance prediction, plasmids, which are mobile DNA elements, and horizontal gene transfer can move resistance genes between organisms and weaken simple predictions from sequence alone.
[1] Reinterpreting National Whole-genome Sequencing Data [2] Applications of Genome Sequencing in Infectious Diseases - PMC [3] A Systematic Review and Meta-Analysis - PMC
Limits of performance
WGS is powerful, but it is not perfect. Accuracy depends on the specimen, the sequencing platform, the analysis pipeline, and the quality of the underlying reference data. Short-read sequencing is accurate and efficient for many routine uses, but it can be weak in repetitive regions and in resolving plasmids or structural variation. Long-read sequencing can better resolve those features, but method choice still affects cost, workflow, and turnaround time. For acute infections, results may need to return within 24 to 48 hours, which requires strong laboratory capacity and automation. In tuberculosis resistance prediction, tool performance varies across studies, so WGS often complements rather than fully replaces phenotypic drug-susceptibility testing.
[2] Applications of Genome Sequencing in Infectious Diseases - PMC [3] A Systematic Review and Meta-Analysis - PMC
Simple takeaway
WGS is a genome-wide test that can uncover many useful genetic changes in one assay. Its value comes from combining broad detection with careful interpretation, not from sequence data alone.
[1] Reinterpreting National Whole-genome Sequencing Data [2] Applications of Genome Sequencing in Infectious Diseases - PMC [3] A Systematic Review and Meta-Analysis - PMC
SEE THE IDEA
How whole-genome sequencing is used
Read DNA across the whole genome in one run.
Find coding, noncoding, mitochondrial, and structural changes.
Use symptoms, outbreak data, or resistance databases.
Account for cost, database gaps, and biology that can blur predictions.
Each step builds on the previous one: broad sequencing creates data, and interpretation turns those data into medical or public-health meaning.
WGS starts with genome-wide sequencing and ends with evidence-based interpretation, but each step adds uncertainty and depends on context.
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Sources and further reading
This general lesson was generated from public source material. It explains a concept; it does not interpret a private paper or provide medical advice.