Bioinformatics and omics

RNA-seq interview questions

Transcript quantification, experimental design and differential expression.

6 questions, each with a worked answer

Why is normalization required for RNA-seq count data? Fundamentals Concept
What are common causes of batch effects in RNA-seq? Intermediate Troubleshooting
How would you design RNA-seq with batch balanced across treatment groups? Intermediate Scenario
What is the difference between normalized counts and log-fold change estimates? Intermediate Concept
Why does differential expression testing need biological replicates? Intermediate Concept
How would you interpret a large fold change with a wide confidence interval? Advanced Scenario

About RNA-seq

RNA sequencing (RNA-seq) measures which genes are expressed in a sample and how strongly, by sequencing the RNA present. A typical analysis aligns or maps reads, counts them per gene, normalises and tests for differential expression between conditions. Replicates, batch effects and library quality decide whether those differences are real.

It is a common readout in target discovery, mechanism studies and biomarker work.

Fundamentals: 1
Intermediate: 4
Advanced: 1

What these questions test

Analyze differential expression: Model counts and biological comparisons with appropriate design and multiple-testing control.

References

NCBI Bookshelf. National Library of Medicine. ncbi.nlm.nih.gov/books.
bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html.

Practice material written by TheBench.Bio and checked against the references above, reviewed October 23, 2026. Written to practise with, not collected from real interviews.