From Wet Lab to Dry Lab: A Biologist's Roadmap to Bioinformatics

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• Prioritize Python, Unix command line, and statistical literacy as your first computational skills, since these form the core toolkit for most bioinformatics work.
• Frame your wet-lab protocols as computational workflows by documenting data generation steps, sample tracking, and quality control metrics you already use.
• Start with a small automation project on your existing data, such as a script that reformats your lab's spreadsheets, before pursuing formal coursework or certification.

Understanding the Transition from Bench to Computation

The shift from wet-lab biology to bioinformatics represents a career move that many life-scientists consider at some point. The U.S. Bureau of Labor Statistics groups bioinformatics roles under broader occupational categories, with computer and information research scientists and biological technicians representing two ends of the spectrum. The transition is not about abandoning your laboratory skills but about adding a new layer of analytical capability that complements your existing expertise.

Bioinformatics work in the United States spans multiple settings. Federal agencies such as the National Science Foundation employ computational biologists and data scientists to support scientific programs, while academic institutions and private companies hire bioinformatics analysts to process and interpret genomic data. The National Institutes of Health Office of Intramural Training and Education describes research careers that increasingly require computational skills, and the American Society for Microbiology notes that microbiology careers now frequently involve data analysis and computational approaches.

Your wet-lab experience provides a foundation that many pure computer science graduates lack. You understand sample preparation, experimental design, and the biological questions that drive data collection. The challenge is translating that understanding into computational skills that employers recognize and value.

At a Glance: Skill Priorities for Wet-Lab Scientists

The table below outlines the core computational skills you should prioritize, the typical tasks they support, and the time investment you should expect for each.

Why Wet-Lab Experience Matters in Bioinformatics

Your time at the bench is not wasted when you move to computational work. The U.S. Department of Labor's O*NET OnLine database describes the work of biological technicians as involving laboratory analysis, data recording, and quality control. These are the same skills that bioinformatics employers need, just applied to different tools.

Experimental Design Knowledge

You know how to design an experiment with proper controls, replicates, and sample sizes. This knowledge is critical in bioinformatics because computational analysis cannot fix a poorly designed experiment. When you move to a bioinformatics role, you will be able to identify whether a dataset was generated with appropriate biological replicates and whether the experimental design supports the conclusions being drawn.

Data Quality Awareness

Wet-lab scientists understand that data quality starts at the bench. You know what happens when a sample is degraded, when a reagent is contaminated, or when a protocol is not followed precisely. This awareness translates directly to computational work, where you will need to assess sequencing quality scores, check for batch effects, and evaluate whether your data meet the assumptions of your analysis methods.

Protocol Translation

Your experience following protocols gives you a framework for learning computational pipelines. A bioinformatics pipeline is essentially a protocol that runs on data instead of samples. You already know how to follow a protocol, troubleshoot when something goes wrong, and document your steps. These habits are directly applicable to running and maintaining computational workflows.

Core Computational Skills to Prioritize

The bioinformatics field is broad, and the specific skills you need will depend on the role you want. However, certain skills appear consistently across job descriptions and professional guidance from organizations like the American Society for Microbiology and the National Institutes of Health.

Python as Your Primary Language

Python is the most widely used programming language in bioinformatics. It has a large ecosystem of libraries for data analysis, including pandas for data manipulation, NumPy for numerical computing, and Biopython for biological data. Python is also the language used in many bioinformatics training programs and is the first language recommended for scientists who are new to programming.

Start by learning the basics of Python syntax, data structures, and control flow. Then move to working with data files, such as CSV or Excel files, using pandas. Finally, learn how to write functions and scripts that automate your analysis.

Unix Command-Line Skills

Most bioinformatics work happens on Linux servers or high-performance computing clusters. You need to be comfortable navigating the command line, managing files, and running programs. The Unix command line is also where you will run many bioinformatics tools that do not have a graphical interface.

Learn the basic commands for file navigation, file manipulation, and text processing. Practice working with compressed files, which are common in bioinformatics. You should also learn how to use a text editor on the command line, such as Vim or Nano, so you can edit scripts and configuration files.

Statistics for Data Interpretation

Bioinformatics is a quantitative field, and you need a solid foundation in statistics to interpret your results correctly. You should understand the basic concepts of hypothesis testing, p-values, confidence intervals, and multiple testing correction. You should also be familiar with common statistical tests used in bioinformatics, such as the t-test, ANOVA, and regression.

The National Institutes of Health Office of Intramural Training and Education offers statistics courses and workshops for researchers, and many universities offer free online statistics courses. You can also learn statistics by working through the analysis of your own experimental data.

Version Control with Git

Git is a version control system that tracks changes to your code. It is essential for working on collaborative projects and for keeping a record of your analysis. You can use Git to track changes to your scripts, to share your code with collaborators, and to revert to previous versions when something goes wrong.

GitHub is a popular platform for hosting Git repositories. You can use GitHub to share your code, to contribute to open-source projects, and to build a portfolio that demonstrates your skills to employers.

Translating Wet-Lab Protocols into Computational Workflows

Your experience with wet-lab protocols is a direct asset when you move to bioinformatics. The way you think about a protocol, from the starting materials to the final output, is the same way you should think about a computational workflow.

Documenting Your Workflow

In the lab, you document your protocols in a lab notebook. In bioinformatics, you document your workflows in code and in README files. The same principles apply: you need to record what you did, why you did it, and what the results were.

When you start a bioinformatics project, create a directory structure that organizes your data, your scripts, and your results. Write a README file that explains the purpose of the project and how to run the analysis. This documentation is essential for reproducibility and for collaborating with other researchers.

Quality Control at Every Step

In the lab, you check the quality of your samples at every step. In bioinformatics, you should do the same with your data. Before you start any analysis, you should check the quality of your raw data. For sequencing data, this means checking the quality scores, the read lengths, and the number of reads. For other types of data, it means checking for missing values, outliers, and formatting errors.

The quality control steps in bioinformatics are analogous to the quality control steps in the lab. You are looking for problems that could affect your results, and you are documenting the steps you take to address them.

Reproducibility and Versioning

In the lab, you record the lot numbers of your reagents and the exact conditions of your experiments. In bioinformatics, you record the versions of your software and the parameters of your analysis. This information is essential for reproducibility, and it is a key part of a professional bioinformatics workflow.

Use a requirements file to record the versions of the Python packages you use. Use Git to track changes to your scripts. Document the parameters you use for each analysis. This level of documentation is what separates a reproducible analysis from a one-off script.

Practical Steps to Build Your Bioinformatics Portfolio

A portfolio of projects is the most effective way to demonstrate your skills to employers. Your portfolio should show that you can take a biological question, analyze the data, and present the results in a clear and reproducible way.

Start with Your Own Data

The best way to start is to work with data you already have. If you have generated sequencing data, expression data, or any other type of data in your lab, use that data for your first bioinformatics project. You know the experimental design, you know the data quality, and you know what the results should look like.

If you do not have your own data, you can use public datasets from repositories like the NCBI Sequence Read Archive or the Gene Expression Omnibus. These repositories contain a large amount of data that you can use to practice your skills.

Replicate a Published Analysis

Another good way to build your portfolio is to replicate a published analysis. Find a paper in your field that includes a bioinformatics analysis, and try to reproduce the results using the data and methods described in the paper. This will give you practice with the tools and methods, and it will help you understand the analysis process.

Automate a Repetitive Task

Look for a task in your lab that is repetitive and time-consuming, and write a script to automate it. This could be a task like reformatting data, generating a report, or processing a set of files. Automating a task is a practical way to learn programming, and it provides a concrete example of the value of your skills.

Options and Tradeoffs in Bioinformatics Career Paths

Bioinformatics is a broad field, and there are several different career paths you can take. The right path for you depends on your interests, your skills, and your career goals.

Academic Research

Academic bioinformatics positions are often in research laboratories or core facilities. In a research laboratory, you might work on a specific biological question, developing and applying computational methods to answer it. In a core facility, you might provide bioinformatics support to multiple research groups, helping them with data analysis and interpretation.

Academic positions often require a graduate degree, and they are often funded by grants. The work is intellectually stimulating, and you have the opportunity to contribute to scientific discoveries. The tradeoff is that academic positions can be less stable than industry positions, and the pay is often lower.

Industry and Biotechnology

Industry bioinformatics positions are in biotechnology companies, pharmaceutical companies, and diagnostic companies. In industry, you might work on drug discovery, clinical trials, or product development. The work is often more applied than academic work, and the pay is often higher.

Industry positions often require a graduate degree, but they also value practical experience. The work can be more structured than academic work, and you may have less freedom to choose your projects. The tradeoff is that the work can be more focused on the company's goals, and you may have less time for basic research.

Government and Federal Agencies

Federal agencies like the National Science Foundation and the National Institutes of Health employ bioinformatics professionals for a variety of roles. These roles can include scientific program management, data analysis, and research support. The work is often focused on supporting the agency's mission, and it can be very stable.

Government positions often require a graduate degree, and they can be competitive. The work is often less flexible than academic or industry work, but it can be very rewarding. The tradeoff is that the work can be more bureaucratic, and you may have less control over your projects.

Building a Professional Network and Finding Mentors

Networking is an important part of any career transition, and it is especially important when you are moving into a new field. The American Society for Microbiology provides career resources and networking opportunities for microbiologists, and the National Institutes of Health Office of Intramural Training and Education offers mentoring and career development programs.

Join Professional Organizations

Professional organizations like the American Society for Microbiology and the International Society for Computational Biology offer networking opportunities, career resources, and professional development. These organizations can help you connect with other bioinformatics professionals, learn about job opportunities, and stay up to date on the latest developments in the field.

Attend Conferences and Workshops

Conferences and workshops are a great way to learn about the latest research and to meet other professionals in the field. Many conferences have career fairs and networking events, and some offer travel awards for students and postdocs. Attending a conference can be a significant investment of time and money, but it can be a valuable investment in your career.

Find a Mentor

A mentor can provide guidance and support as you navigate your career transition. A mentor can help you identify the skills you need to develop, connect you with opportunities, and provide feedback on your work. You can find a mentor through your professional network, through your institution, or through a formal mentoring program.

Common Failure Patterns in the Transition

Many wet-lab scientists make the same mistakes when they try to move into bioinformatics. Understanding these common failure patterns can help you avoid them.

Trying to Learn Everything at Once

Bioinformatics is a broad field, and it is easy to feel overwhelmed by the number of tools and techniques you need to learn. Trying to learn everything at once is a common mistake. Instead, focus on a small set of core skills and build from there. Start with Python, Unix, and statistics, and then add other skills as you need them.

Neglecting Statistics

Many wet-lab scientists focus on learning programming and neglect statistics. This is a mistake because statistics is essential for interpreting your results. You need to understand the statistical concepts that underlie your analysis, and you need to be able to communicate your results to others.

Not Building a Portfolio

A portfolio is the best way to demonstrate your skills to employers. Many wet-lab scientists spend time learning skills but do not build a portfolio to show what they can do. Start building a portfolio early, and add to it as you learn new skills.

Not Networking

Networking is important for any career transition, but it is especially important when you are moving into a new field. Many wet-lab scientists do not network enough, and they miss out on opportunities. Start networking early, and build relationships with people in the field.

Records and Measurements for Your Transition

As you work through your transition, you should keep records of your progress. These records will help you track your learning, build your portfolio, and demonstrate your skills to employers.

Track Your Skills

Keep a list of the skills you have learned and the projects you have completed. This list will help you identify gaps in your skills and will be a useful reference when you are applying for jobs.

Document Your Projects

For each project you complete, document the problem you were trying to solve, the data you used, the methods you applied, and the results you obtained. This documentation will be the basis of your portfolio, and it will help you explain your work to employers.

Measure Your Progress

Set goals for your learning and track your progress against those goals. For example, you might set a goal to complete a certain number of Python exercises each week, or to finish a project by a certain date. Tracking your progress will help you stay motivated and will help you identify areas where you need to focus.

Professional Escalation and When to Seek Help

There are times when you will need to seek help from a more experienced professional. Knowing when to escalate a problem is an important skill in bioinformatics.

When You Are Stuck

If you are stuck on a problem for more than a few hours, it is time to seek help. You can ask a colleague, post a question on a forum, or consult a mentor. Getting help early can save you a lot of time and frustration.

When You Need a Different Perspective

Sometimes you need a different perspective on a problem. A colleague or mentor can provide a fresh perspective and help you see the problem in a new way. This is especially useful when you are working on a complex analysis and you are not sure if your approach is correct.

When You Are Considering a Major Decision

If you are considering a major decision, such as a job offer or a change in your career path, it is a good idea to seek advice from a mentor or a career counselor. They can help you think through the decision and provide a perspective that you might not have considered.

Frequently Asked Questions

Do I need a graduate degree to work in bioinformatics?

Many bioinformatics positions require a graduate degree, but not all of them do. The U.S. Bureau of Labor Statistics notes that some positions require a master's degree or higher, while others may accept a bachelor's degree with relevant experience. Your specific requirements will depend on the employer and the role.

What is the salary for a bioinformatics professional?

The salary for a bioinformatics professional varies widely depending on the role, the employer, and the location. The U.S. Bureau of Labor Statistics provides salary data for related occupations, such as information and research scientists and biological technicians. You should research the specific role you are interested in to get a more accurate salary estimate.

How long does it take to transition from wet lab to bioinformatics?

The time it takes to transition depends on your background and the time you can dedicate to learning. A reasonable timeline is 6 to 12 months of consistent effort to build the core skills and a portfolio. The more time you can dedicate, the faster you can make the transition.

What is the best programming language for bioinformatics?

Python is the most widely used programming language in bioinformatics, and it is a good choice for most people. R is also widely used, especially for statistical analysis. You should learn Python first, and then add R if you need it for your work.

Do I need to know how to use a high-performance computing cluster?

Many bioinformatics jobs require you to work on a high-performance computing cluster, but not all of them do. If you are working with large datasets, you will likely need to use a cluster. You can learn the basics of cluster computing on the job, but it is helpful to have some experience before you start.

How do I build a portfolio if I do not have my own data?

If you do not have your own data, you can use public datasets from repositories like the NCBI Sequence Read Archive or the Gene Expression Omnibus. You can also replicate a published analysis using the data and methods described in the paper. These are good ways to build a portfolio without your own data.

What are the most important skills for a bioinformatics job?

The most important skills for a bioinformatics job are programming, statistics, and data analysis. You should be comfortable with Python or R, you should understand the statistical concepts that underlie your analysis, and you should be able to work with large datasets. You should also be able to communicate your results clearly.

How do I find a mentor for my career transition?

You can find a mentor through your professional network, through your institution, or through a formal mentoring program. The National Institutes of Health Office of Intramural Training and Education offers mentoring and professional development programs, and the American Society for Microbiology provides career resources for networking. You can also reach out to people in your field who you admire and ask for advice.

Using the Evidence

Related Articles

• Computational Biologist Job Description: Skills, Deliverables, and Team Context
• Bioinformatics vs. Computational Biology: What's the Difference (and Which Should You Choose)?
• How to Learn Bioinformatics from Scratch: A Complete Roadmap (From a Biologist Who Couldn't Code)
• Bioinformatics vs Computational Biology: Which Degree Path Is Right for You?
• Bioinformatics Scientist Jobs

References and Further Reading

• Occupational Outlook Handbook. U.S. Bureau of Labor Statistics.
• O*NET OnLine. U.S. Department of Labor.
• Office of Intramural Training and Education. National Institutes of Health.
• Careers at NSF. U.S. National Science Foundation.
• Career Services. American Society for Microbiology.
• AAVMC Career Resources. Association of American Veterinary Medical Colleges.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.