Essential Tech Skills for Industry Bioinformatics Roles
Modern bioinformatics roles require a broader technical toolkit than what most of us learn in graduate school. You’ll need to be part software engineer, part computational biologist, and part data engineer.
Here are key technical skills mentioned frequently in industry job ad’s, along with resources to learn more.
We’ve also created a job board focused on internships and temporary positions, which can be a great way to gain experience as a student!
Linux, SQL, and Version Control ¶
These fundamentals will serve you well in any bioinformatics role. Start with Software Carpentry’s free courses:
For deeper Linux knowledge, see Free Code Camp’s Linux resources.
SQL is vital for accessing and storing data in industry. Focus on the principles of relational databases and data normalization. I started with SQLite - it’s perfect for learning since you don’t need to deal with server setup and it is built into Python’s standard library! FreeCodeCamp’s SQLite3 tutorial is a great starting point.
Workflow Engines ¶
Remember that R script you wrote to analyze a few samples? How would you scale it up to process 100’s of gigabytes or terabytes of data? Could you run it on the cloud or a high performance computing environment? This is where workflow engines come in.
- Software Carpentries Nextflow
- Learning WDL
- Dave Tang’s WDL guide
- Johns Hopkins WDL course
- Nextflow Fundamentals
My take: WDL is easier to learn but Nextflow seems to be gaining more industry adoption.
Data Science and Data Management ¶
A lot of bioinformatics work involves moving and organizing data. These resources are a good jumping off point:
- OmicsTutorials
- Johns Hopkins Data Science Lab
- Bioinformatics Workbook
- Datascience workbook
- This classic paper on organizing computational biology projects
- The PLoS 10 Simple Rules Collection
- OSSU Bioinformatics Course
AI/ML Skills ¶
- Generative AI Handbook: A Roadmap for Learning Resources by William Brown is a great overview of key concepts and essential learning materials for the field with links to more resources.
Other Resources ¶
- Computational Genomics with R is a great resource for those just getting into the field. It covers genetics basics as well as programming in R
Share Your Experience ¶
What resources helped you transition to industry? Connect with me at [email protected] or on Bluesky @pharmapaywatch.com - I’d love to add your suggestions to this list.