You had 400 samples. You sequenced 80.
Most genomics studies are designed around the sequencing budget before anyone settles the biological question. Sample sizes shrink, resolution gets traded away, and those compromises rarely make it into the methods section. They show up later, in the variants that were never called and the associations that never reached significance.
The Long-Read is a monthly column on that trade-off: where it hides in study design, what it costs research programs, and what becomes possible when sample size and resolution stop competing for the same budget.
In every issue
The feature. One idea, taught in depth. Study design, reference bias, coverage, cost per answer.
The Bottom Line. The takeaway in a sentence, for when you only have a minute.
From the Lab. What the Veil team is working on, including office hours and new publications.
Related Reading. Selected posts and papers worth your time.
Published monthly. Written by scientists, for scientists.
Past Issues
Issue 004: Where the missing heritability went
October 2026
Pedigree says a trait is heritable, but the markers explain only part of it. Why some of the variants responsible were never genotyped, why imputation copies the gap instead of closing it, and what changes when structural variation is measured directly.
Issue 003: One genome, standing in for a species
September 2026
Every variant is called against a reference, and a reference is one individual. What a single genome fails to count, why diversity panels lose the most, and how pangenome graphs give divergent reads somewhere to land.
Issue 002: Same depth, same cost, a different genome
August 2026
The same 127 peanut lines, sequenced two ways at the same depth. Long reads covered more than three times as much of the genome. Here’s what that gap means for the variants a breeding program never gets to count.
Issue 001: Your sequencing budget is making scientific decisions for you
July 2026
Sample size is usually set by the budget before the science starts. What an undersized cohort quietly removes from a study, and why the gaps never show up in the results file.