Taking the leap: Transitioning Your Career to Bioinformatics

Clinicians and bench scientists (a.k.a., wet lab researchers) are at the forefront of scientific achievement. However, most, if not all contemporary projects involve robust scientific analysis, where the tools of exploration aren’t just microcentrifuges, thermocyclers, high throughput sequencers, etc., but also computational hardware and software. Over the years, there has been an increase in bioinformatics career opportunities due to scientific advancement and the wave of associated big data. Bioinformatics careers often feature benefits such as a greater work-life balance, flexibility in workplace arrangements (e.g., hybrid and remote work options), and a change in scenery that many find appealing, prompting some to wonder what it takes to transition from bench work to computer work?

Transitioning from a wet lab to a bioinformatics role requires a significant shift in skill set. While most wet lab scientists have a strong foundation in biology, which is a necessity for any bioinformatician, a transition to bioinformatics also requires a robust understanding of computational methods, statistics, and programming (likely R, Python, Bash). This understanding is best acquired through a combination of education and experience, whether formal or informal.

Formal programs emphasizing bioinformatics skills are becoming increasingly more common at a bachelor’s level (e.g., UMBC, UA, IIT), and there are now many master’s and certificate programs that offer in-depth training in bioinformatics tools and concepts (e.g., Center for Alzheimer’s and Related Dementias Data Science/UMBC Master’s degree). Additional and less formal resources for learning include online courses and tutorials, bootcamps, and textbooks. Textbooks, in particular, are fantastic resources for understanding underlying statistical or computational methods and algorithms (Search the NIH library for books by subject).

Here are some resources for acquiring bioinformatics skills:

• Online Courses and Tutorials Outside of NIH:
  • Coursera, edX, and Udemy: Offer a variety of bioinformatics courses, often taught by university professors.
    • Coursera licenses available to NIH researchers
  • Dataquest or Datacamp: acquire data science and programming skills
    • Dataquest licenses available to NCI-CCR researchers
  • Rosalind: for learning centered around problem solving.
  • Sandbox.bio: for self-contained bioinformatics learning, only requiring a browser.
  • Glitrr: Git repositories with bioinformatics training materials

• In-person and Online Courses and Tutorials from NIH:
  • BTEP calendar with course offerings: includes NIH wide events related to bioinformatics including lessons and seminars on bioinformatics methods, concepts, skills
  • BTEP video archive: for recordings of previous BTEP trainings
  • NIAID Bioinformatics and Computational Biosciences Branch: for upcoming bioinformatics training events and online tutorials
  • NIH Library: hosts data science and bioinformatics training events
  • NCBI: includes courses, workshops, webinars, training materials and documentation.
  • CBIIT training: includes data science training materials for cancer research

Regardless of the steps taken to learn bioinformatics, it is incredibly important that any educational experience is supplemented by practical, “real-world” experience. Real-world experience can be obtained by undertaking bioinformatics projects with non-perfect data and results. The lessons learned from hands-on experience can be more advantageous than those learned from any formal education. Many bioinformaticians are “self-made”, having learned skills out of necessity, for example, by having data that needed to be analyzed and no one to analyze it. If you do not currently have data in need of analysis, speak with your PI or reach out to other labs. Alternatively, think of a problem in biology that interests you and work with data from public databases (e.g., GEO, SRA) to address it. You can also contribute to bioinformatics projects via DREAM Challenges or hackathons (and codeathons). Consider putting your code and projects on a repository such as GitHub. You will need to build a portfolio of bioinformatics projects to be taken seriously by any future employer.

Ultimately, to successfully transition your career you will need self-motivation, persistence, patience, and a strong interest in biology and computer science.

– Alex Emmons (BTEP)