Wet Lab to Dry Lab:
Your Transition Guide
You've mastered pipetting and PCR. Now learn to code, analyze data, and transition to the high-demand world of computational biology.
Why Make the Switch?
The demand for computational skills in biology has exploded. Sequencing costs have dropped 10,000x, generating massive datasets that need analysis. Meanwhile, wet lab positions face increasing competition and lower salaries. Here's the reality:
Wet Lab Challenges
- ⚠ Research Associate salaries: $50K-70K
- ⚠ Limited positions, high competition
- ⚠ Repetitive benchwork
- ⚠ Slower career progression
- ⚠ Lab closures and funding cuts
Dry Lab Advantages
- ✓ Bioinformatics salaries: $90K-180K+
- ✓ High demand, talent shortage
- ✓ Remote work opportunities
- ✓ Faster career growth
- ✓ Transferable tech skills
Your wet lab experience is a major advantage!
In pharma and biotech, you'll work in matrix environments alongside bench scientists. Your wet lab background gives you leverage that pure computational people lack:
- ✓ Credibility with experimentalists - You've been in their shoes. They trust you.
- ✓ Ask the right questions - You know what's biologically meaningful vs. statistical noise.
- ✓ Understand constraints - Sample limitations, batch effects, what's actually feasible.
- ✓ Translate both directions - Explain computational results to biologists, and biological context to data scientists.
- ✓ Design better experiments - You can suggest controls and conditions that matter for analysis.
Bottom line: A computational biologist who's done benchwork has a better view of the full picture than someone who's only seen data as files. This makes you more valuable, not less.
Skills You Need to Learn
Don't spread yourself thin. You don't need to learn both R and Python right away. Focus on a problem that interests you or needs to be solved, then learn the best tool for that problem. Doing RNA-seq? Start with R/DESeq2. Building pipelines? Start with Python. Let your project guide your learning.
Priority 1 Essential Skills (Learn First)
Python
The #1 language for bioinformatics. Used for data analysis, pipelines, and ML.
R / Bioconductor
Essential for statistical analysis, RNA-seq, and genomics visualization.
Linux / Command Line
Most bioinformatics tools run on Linux. Essential for HPC and cloud.
Statistics
Understanding p-values, multiple testing, regression is crucial.
Priority 2 Important Skills (Learn Next)
Git & GitHub
Version control for code. Every job requires this. Also your portfolio.
SQL
Optional for most bioinformatics roles. More relevant for clinical data or data engineering.
NGS Data Analysis
RNA-seq, WGS, WES - the bread and butter of genomics.
Priority 3 Specialization (Pick 1-2)
Machine Learning
scikit-learn, PyTorch. Hot area in drug discovery and genomics.
Single-Cell Analysis
Seurat, Scanpy. Rapidly growing field with high demand.
Cloud Computing
AWS, GCP. Increasingly required for large-scale analysis.
Pipeline Development
Nextflow, Snakemake. Build reproducible workflows.
Free Learning Resources
Python for Biologists
R for Genomics
Bioinformatics Courses
AI Tools (Game Changer for Learning)
LLMs have made learning to code dramatically easier. Use them to:
Important: The barrier to learning has never been lower, but focus on understanding, not just copying code. Ask the AI to explain concepts, walk through logic, and teach you the "why" - not just give you working code. You need to understand what you're doing to troubleshoot, interpret results, and grow.
Lean into AI - the future is different. AI won't replace bioinformaticians, but bioinformaticians who use AI will outperform those who don't. Get comfortable using LLMs as part of your daily workflow now. The scientists who thrive will be those who can leverage AI to move faster, explore more hypotheses, and focus their human judgment on what matters most - the biology.
GitHub: Your Learning Journal & Portfolio
Start a GitHub account early and commit everything. It serves multiple purposes:
Tip: Create repos for each project, even small ones. Write clear READMEs explaining what you did and why. Your GitHub is proof of your journey - messy early code is fine, it shows growth.
Transition Timeline
Here's a realistic timeline for transitioning while working your current job:
Foundation
- ✓ Complete Python basics (Codecademy or similar)
- ✓ Learn Linux command line basics
- ✓ Set up your development environment
- ✓ Start a GitHub account
Bioinformatics Basics
- ✓ Work through Rosalind.info problems
- ✓ Learn pandas for data manipulation
- ✓ Start R basics and Bioconductor
- ✓ Analyze a public RNA-seq dataset
Build Portfolio
- ✓ Complete 2-3 portfolio projects on GitHub
- ✓ Analyze data from your own lab if possible
- ✓ Take a Coursera/edX genomics course
- ✓ Learn Git version control properly
Specialize & Network
- ✓ Pick a specialization (single-cell, ML, etc.)
- ✓ Attend bioinformatics meetups/conferences
- ✓ Connect with bioinformaticians on LinkedIn
- ✓ Offer to help with computational tasks in your lab
Job Search
- ✓ Update resume highlighting computational skills
- ✓ Apply to entry-level bioinformatics positions
- ✓ Practice coding interviews
- ✓ Target "hybrid" roles that value wet lab experience
Pro tip: If you can negotiate computational time at your current job (e.g., "I'll analyze the lab's RNA-seq data"), you'll learn faster AND have a real project for your portfolio.
The Best Strategy: Add Bioinformatics to Your Current Projects
The most effective way to transition isn't learning in isolation - it's incorporating computational analysis into the experimental work you're already doing. This approach:
Why This Works Better
- ✓ You understand the biology deeply
- ✓ You know what questions matter
- ✓ Real data is more motivating than tutorials
- ✓ Results directly benefit your lab
- ✓ Potential co-authorship on papers
How to Start
- ✓ Offer to analyze your lab's RNA-seq data
- ✓ Automate repetitive analysis tasks
- ✓ Build visualizations for lab meetings
- ✓ Re-analyze public data related to your project
- ✓ Collaborate with the lab's bioinformatician
Example: From Western Blots to RNA-seq
Say you're studying a gene's role in cancer using knockdowns and Western blots. You could expand this by:
- Proposing RNA-seq on your knockdown vs control cells
- Learning to analyze the differential expression yourself
- Identifying pathways affected (adds depth to your story)
- Publishing with both wet and dry lab components
Result: You've learned bioinformatics while advancing your actual research, and you have a publication demonstrating both skills.
First Roles to Target
Not all bioinformatics roles are equal. These are good entry points that value your wet lab background:
Good First Roles
-
Bioinformatics Analyst →
Entry-level, often values domain knowledge. $70K-100K
-
Research Associate (Computational)
In labs transitioning to computational work. $60K-80K
-
NGS/Genomics Analyst →
Focused role, easier to learn. $75K-95K
-
Entry-Level Positions →
Diagnostic labs value biology knowledge. $80K-110K
Target Later (More Experience)
-
Bioinformatics Scientist →
Requires more independent work. $100K-140K
-
Computational Biologist →
Often requires PhD + computational training. $110K-160K
-
ML Engineer (Biotech) →
Requires strong CS/ML background. $130K-180K
Hybrid roles are your advantage: Look for positions that explicitly mention "wet lab experience a plus" or "interface between wet and dry lab." These roles value your unique combination of skills.
Leverage Your Wet Lab Experience
Your experimental background is a superpower, not a handicap. Here's how to position it:
Biology Knowledge
You understand what's biologically meaningful vs. statistically significant. Pure CS people often miss this.
Experimental Design
You know about batch effects, controls, and technical replicates. Essential for proper analysis.
Lab Communication
You can talk to wet lab scientists in their language. Critical for collaborative roles.
Resume Bullet Examples
- ✓ "Designed and executed NGS experiments, then transitioned to computational analysis of resulting data"
- ✓ "Collaborated with bioinformatics team to optimize RNA extraction protocols based on data quality metrics"
- ✓ "Bridged wet lab and dry lab teams, translating biological questions into computational analyses"
Networking & Community
Transitioning is easier when you're not alone. Connect with others in computational biology:
Online Communities
- ✓ Bioconductor Slack - Active, helpful community
- ✓ Reddit r/bioinformatics - Questions, career advice
- ✓ Twitter/X #bioinformatics - Follow researchers, trends
- ✓ LinkedIn groups - Computational biology networks
Finding Mentors
- ✓ Reach out to bioinformaticians at your institution
- ✓ Ask for informational interviews on LinkedIn
- ✓ Attend local meetups and introduce yourself
- ✓ Contribute to open source - maintainers often mentor
Conferences to Attend
Major Conferences
- ✓ ISMB/ECCB - Premier bioinformatics conference
- ✓ RECOMB - Computational biology research
- ✓ ASHG - Human genetics (great for networking)
Workshops & Smaller Events
- ✓ Bioconductor conferences - R/genomics focused
- ✓ Galaxy Community Conference - Accessible tools
- ✓ Local biotech meetups - Check Meetup.com
Tip: Many conferences have travel awards for trainees. Apply early!
Portfolio Project Ideas
Build 2-3 solid projects that demonstrate your skills. Quality over quantity.
RNA-seq Analysis
Download public GEO dataset, run differential expression (DESeq2), pathway analysis, create visualizations. Write up findings.
Single-Cell Analysis
Use Scanpy or Seurat on a public 10x dataset. Clustering, cell type annotation, marker genes. Hot skill right now.
Reproducible Pipeline
Build a Nextflow or Snakemake pipeline for a common workflow. Containerize with Docker. Shows engineering skills.
Your Own Research Data
Best option if available. Analyze data from your lab. Real biological questions, potential for publication.
What makes a good portfolio project: Clear README explaining the biology question, clean code, good visualizations, and interpretation of results. Show you understand the science, not just the code.
Interview Tips for Career Changers
Interviewers will ask about your transition. Here's how to frame it positively:
"Why are you switching from wet lab to computational?"
Good answer: Focus on pull factors (excited by data, scale of impact, seeing the field move this direction) not push factors (tired of pipetting, failed experiments). Show genuine enthusiasm for computational work.
"You don't have a CS degree - can you handle the technical work?"
Good answer: Point to your portfolio projects, specific tools you've learned, and emphasize that you understand the biology deeply. Many successful bioinformaticians are self-taught.
"Tell me about a computational project you've done."
Good answer: Structure as: biological question → approach → tools used → results → what you learned. Emphasize the "why" behind your choices.
Your advantage in interviews: When asked about experimental design, controls, or biological interpretation - this is where you shine vs. pure CS candidates. Lean into your wet lab experience.
Remote Work Opportunities
Good news: computational roles have far more remote options than wet lab positions.
Fully Remote
Many biotech companies now hire remote bioinformaticians
Hybrid
2-3 days in office, common at larger pharma companies
Location Flexibility
Don't need to live in Boston/SF to work in biotech
This is a major perk of going computational: You're no longer tied to where the lab equipment is. This can be life-changing for work-life balance, family situations, or cost of living.
Are Certifications Worth It?
Generally Not Required
- ✓ Most employers care about skills, not certificates
- ✓ Portfolio projects demonstrate ability better
- ✓ PhD/MS already signals learning ability
- ✓ Money often better spent elsewhere
Can Be Helpful For
- ✓ Structure if you struggle with self-study
- ✓ Cloud certs (AWS) for infrastructure roles
- ✓ Completing courses shows commitment
- ✓ Some HR filters look for keywords
Bottom line: Don't pay for expensive bootcamps or certifications expecting them to get you hired. Free courses + real projects on GitHub will serve you better. If you do a course, the learning matters more than the certificate.
Option: Computational Postdoc
If you want a structured environment to learn computational skills deeply, a computational postdoc can be an excellent path - especially if you're coming from a wet lab PhD.
Advantages
- ✓ Dedicated time to learn (not squeezing it in)
- ✓ Mentorship from computational experts
- ✓ Access to large datasets and compute resources
- ✓ Publications in computational methods
- ✓ Credibility when applying for dry lab roles
- ✓ Network in computational biology community
Considerations
- ⚠ Lower pay than industry ($55K-70K)
- ⚠ 2-3 year commitment
- ⚠ Delays industry entry (opportunity cost)
- ⚠ Need to find the right lab/mentor
- ⚠ May not be necessary if self-motivated
What to Look for in a Computational Postdoc
- ✓ Mentor who values training: Some PIs just need labor; find one invested in your development
- ✓ Projects that match your goals: If you want industry, avoid purely theoretical work
- ✓ Collaborative environment: Labs that work with experimentalists help you bridge both worlds
- ✓ Track record: Where do former postdocs end up? Industry? Faculty? Stuck?
Best of both worlds: Some postdocs are explicitly designed for wet-to-dry transitions. Look for positions that say "computational training provided" or "wet lab background welcome." Labs doing multi-omics or systems biology often want people who understand experiments.
Common Challenges & Solutions
"I don't have a CS degree"
Neither do most bioinformaticians! Many have biology/biochemistry PhDs. Your domain knowledge + self-taught coding is often preferred over CS + no biology.
"I feel like an imposter"
Everyone does at first. You don't need to know everything - just enough to be useful and keep learning. Start with specific tools (DESeq2, Seurat) rather than trying to learn "all of programming."
"I don't have time to learn while working"
Even 30 minutes daily adds up. Use your commute for podcasts/videos. Weekend projects compound quickly. Many successful transitioners took 12-18 months part-time.
"Should I go back to school?"
Usually not necessary. A Master's in Bioinformatics can help but costs time and money. Self-study + portfolio projects often work just as well. Consider it if you want a structured path or visa sponsorship.
Frequently Asked Questions
How long does it take to transition from wet lab to bioinformatics?
With dedicated effort, most wet lab scientists can make the transition in 6-12 months. Part-time learning while working takes longer, typically 12-18 months.
Do I need a PhD to transition to bioinformatics?
No, a PhD is not required. Many successful bioinformaticians have Master's degrees or even Bachelor's with strong computational skills.
What programming language should I learn first?
Let your problem guide you. Doing RNA-seq or statistics? Start with R. Building pipelines or doing ML? Start with Python. Don't try to learn both at once - focus on solving a real problem.
Can I transition without going back to school?
Yes! Many successful transitions happen through self-study, online courses, and building portfolio projects. A formal degree helps but isn't required.
Ready to Start Your Transition?
Browse bioinformatics jobs and find roles that value your wet lab experience.