The Sampling Blind Spot
Dr. Jez Lim Marston, MD, PhD
Interactive · companion to “Cracking the Glycan Shield”

The sampling blind spot.

Watch a virus population evolve resistance under antibody pressure, generation by generation, then watch most of that resistance disappear the moment realistic sequencing depth is applied.

← Back to the main reporttoy population ~4811 generationsreal simulator 10,000
Not peer reviewedThis page is part of independent computational work and has not been through peer review. I welcome scrutiny. If you work on HIV antibodies, glycan shields, or viral escape and you find an error, a questionable assumption, or a better way to test a result, please write to jezlim.marston@gmail.com.
The instrument

Run a population, then sequence it

Run to generation
Sequencing depth 48 / 48
wild-type lineage
escape lineage
escape, not sequenced
generation 0 / 11
Population, this generation
1
wild-type + escape lineages alive right now
Independent origins: full population
0
distinct mutation events, if every genome were sequenced
Independent origins: actually caught
0
at the current sequencing depth
Reference

The same mechanism, at real scale

This toy runs a population of ~48 over 11 generations so it fits on screen. The project’s real simulator runs a population of 10,000 and hit the identical wall.

Real patientsTable 7, dense multi-timepoint sequencing1,2
21.5
Simulator, full populationevery one of 10,000 genomes “sequenced”
16.0
Simulator, realistic depthsame population, sampled the way real sequencing actually samples
0.0
References

Sources

  1. 1.Romero EV, et al. Recurrent mutations drive rapid HIV escape from two broadly neutralizing antibodies in vivo. Proc Natl Acad Sci U S A 2026;123:e2524021123. doi:10.1073/pnas.2524021123.
  2. 2.Caskey M, et al. Antibody 10-1074 suppresses viremia in HIV-1-infected individuals. Nat Med 2017;23:185–191. doi:10.1038/nm.4268