Technical interview guide

Gene Expression Analysis & RNA-Seq

Quantifying which genes are active and by how much — read counting, normalization, and differential expression testing.

Read: 49 min
Practice MCQs: 25
Interview QA: 25
Edition: v5
Editorial status: Reviewed
Relevant for: Computational Biologist

Scope: DESeq2, edgeR, voom, STAR, Salmon, kallisto, featureCounts, tximport and MultiQC source publications; GENCODE, GA4GH and ENCODE guidance reviewed 2026-09-04.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-09-10 · Reviewed: 2026-10-04

QA-1

Design replication for a two-condition RNA-seq study.

QA-2

Lay out samples across extraction batches and lanes.

QA-3

Diagnose a confounded expression study.

QA-4

Model pre/post samples from the same donors.

QA-5

Validate RNA-seq library metadata.

QA-6

Define pre-analysis RNA-seq QC.

QA-7

Configure splice-aware alignment.

QA-8

Quantify isoforms responsibly.

QA-9

Create a durable count matrix schema.

QA-10

Count fragments for gene-level differential analysis.

QA-11

Choose expression units for two analysis goals.

QA-12

Validate RNA-seq normalization.

QA-13

Explain a gene-level count model.

QA-14

Diagnose abnormal dispersion estimates.

QA-15

Construct a model with condition, batch, and sex.

QA-16

Define treatment effects with a genotype interaction.

QA-17

Prioritize genes by effect size.

QA-18

Set differential-expression decision rules.

QA-19

Investigate an RNA-seq sample outlier.

QA-20

Use PCA to assess an RNA-seq cohort.

QA-21

Handle batch in differential expression and visualization.

QA-22

Run interpretable pathway enrichment.

QA-23

Interpret an inflammatory tissue signature.

QA-24

Package a reproducible expression analysis.

QA-25