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Bioinformatics Scientist interview questions, and how to answer them

A bioinformatics interview tests whether your analysis would survive a skeptical biologist and a picky reviewer at the same time. Expect questions that start with a real dataset and keep pushing until they find the assumption you didn't check. The good news is that honesty about limits scores better here than confident guessing.

11 questions6 minute read

Part 1

1.Walk me through how you'd analyze a bulk RNA-seq experiment from raw FASTQ files to a list of genes.

Why they ask

It's the bread-and-butter workflow, and each step hides a choice they want to hear you make on purpose.

How to answer

  • Start with quality control using FastQC and MultiQC, and say what would make you trim or drop a sample
  • Pick alignment with STAR or quantification with Salmon, and say why for this design
  • Normalize and model counts with edgeR, limma-voom or a comparable method, including the design matrix
  • Check sample relationships with PCA before trusting any contrast
  • Report adjusted p-values and effect sizes, then hand results over with a short note on caveats

2.Your PCA shows samples cluster by sequencing date instead of by treatment. What do you do?

Why they ask

Batch effects wreck more projects than bad code does, and they want to see you catch one before it becomes a paper.

How to answer

  • Confirm it with metadata and look at whether batch and treatment are confounded
  • If they're balanced, model batch as a covariate rather than scrubbing it blindly
  • If they're fully confounded, say plainly that the design can't separate them
  • Tell the bench team early and suggest how to lay out the next run

3.How would you call germline variants from whole-genome sequencing, and how would you know the calls are good?

Why they ask

Variant work is common in clinical and pharma teams, and quality checks separate careful analysts from button pushers.

How to answer

  • Align with BWA, mark duplicates and follow GATK best practices through joint genotyping
  • Filter with variant quality score recalibration or hard filters, depending on cohort size
  • Check the transition to transversion ratio, sex checks and relatedness to catch sample problems
  • Benchmark against a truth set such as Genome in a Bottle where possible
4.Tell me about a pipeline you built. How would someone else rerun it a year later?

Why they ask

Reproducibility is the thing hiring managers complain about most in past hires.

How to answer

  • Name the workflow manager, such as Nextflow or Snakemake, and why you chose it
  • Explain how you pinned software with Docker, Singularity or conda environments
  • Describe config files, logging and how inputs and reference builds are recorded
  • Mention tests or a small example dataset that runs end to end
5.A biologist sends you single-cell data and asks which cluster is the new cell type. How do you respond?

Why they ask

Single-cell clustering invites over-interpretation, and they want to see you slow a bad conclusion down without being difficult.

How to answer

  • Check doublets, ambient RNA and mitochondrial content before trusting clusters
  • Show that the cluster holds up across resolutions and isn't driven by one sample
  • Compare marker genes against known references and literature
  • Suggest a bench validation, like staining or sorting, before anyone calls it new

Part 2

6.How do you handle multiple testing when you're comparing thousands of genes?

Why they ask

It's a fast test of whether your statistics go deeper than running the default function.

How to answer

  • Explain false discovery rate control and why it suits discovery work
  • Contrast it with family-wise error control and when that's the better fit
  • Note that independent filtering of low-count genes changes power
  • Pair adjusted significance with effect size so tiny changes don't mislead
7.Which reference genome and annotation would you use, and what goes wrong if people mix them?

Why they ask

Mismatched builds are a quiet, common source of wrong results, and experienced people have been burned by them.

How to answer

  • Name the current human build and a matching GENCODE or Ensembl annotation
  • Explain coordinate mismatches, chromosome naming differences and liftover risk
  • Say how you record the build in pipeline outputs so nobody guesses later
8.How would you scale an analysis that's too big for your workstation?

Why they ask

Teams run on HPC clusters or cloud accounts, and they need to know you won't burn the budget or the queue.

How to answer

  • Describe using Slurm or a cloud batch service with a workflow manager
  • Explain splitting work by sample or chromosome to parallelize
  • Mention cost and resource tracking, and cleaning up intermediate files
  • Talk about storage formats and indexing for BAM and VCF files
9.Tell me about a result you were excited about that turned out to be wrong.

Why they ask

Everyone in this field has one. They want to see how you found it and whether you told people.

How to answer

  • Describe the result and why it looked convincing at first
  • Explain the check that exposed the problem
  • Say how you told the team and what changed afterward
10.How do you decide when a machine learning model is the right tool for a biology question?

Why they ask

Plenty of candidates reach for deep learning first. They want judgment about sample size and interpretability.

How to answer

  • Start from the question and the number of independent samples, not the method
  • Explain leakage risks, like the same patient in train and test sets
  • Prefer a simpler, interpretable baseline before anything fancier
  • Say what a biologist would need to see to trust the model

Part 3

11.Describe a time you disagreed with a principal investigator or project lead about an analysis.

Why they ask

You'll often be the most junior person who knows the data best. They need to see you hold a line politely.

How to answer

  • Set up the disagreement in concrete terms
  • Show the evidence you brought, such as a plot or a sensitivity check
  • Describe how it was resolved, even if you didn't win

Questions to ask them

Ask at least two. It shows you're picking them too.

  • Who designs the experiments, and how early does bioinformatics get pulled into that conversation?
  • What does the pipeline stack look like, and who maintains it when something breaks?
  • Do you run on an on-site cluster, a cloud account, or both?
  • How are bioinformatics contributions credited on papers, patents or internal reports?
  • What happened with the last analysis that didn't give the answer the team wanted?

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