Getting started in bioinformatics can feel overwhelming — there are too many resources, too many directions, and no obvious entry point. This guide compiles recommendations for students who are just starting out, organized by skill area.
Foundational Skill Areas
Programming and the Unix Environment
Before diving into bioinformatics-specific tools, getting comfortable with coding is essential. Most bioinformatics workflows run on Unix/Linux systems, so familiarity with the command line (shell, Ubuntu, remote servers) is a prerequisite for almost everything else.
If your background is in life sciences, start here:
- Learn basic Python or R — whichever your lab uses
- Get comfortable with the terminal: file navigation, text processing, running scripts
- Use Git for version control and GitHub to share your work — even small personal projects are good practice
Once you have the basics, explore workflow systems like Nextflow or Snakemake to avoid reinventing the wheel for common analysis pipelines.
Statistics and Probability
A solid understanding of statistics is needed at every level of bioinformatics. If you studied life sciences, you likely have some foundation — the goal is to build on it.
Josh (StatQuest) Starmer’s YouTube channel is one of the best free resources available: it covers everything from basic statistics to machine learning, explained clearly and from scratch.
Video Resources and Community Pages
These YouTube channels are useful regardless of whether you’re a complete beginner or already doing advanced analyses:
- chatomics — practical tutorials by Tommy
- Bioinformagician — guided walkthroughs
- SIB Swiss Institute of Bioinformatics — structured courses and seminars
The Harvard Chan Bioinformatics Core (hbctraining) GitHub pages are also excellent — they include full training materials from their courses.
Self-Directed Curriculum
For a more structured approach, OSSU Bioinformatics offers a self-paced open curriculum covering the full range of skills. Most of the linked resources are free.
What Kind of Bioinformatician Do You Want to Be?
The field is broad. Here is a rough map of the main roles:
- Algorithm developers — Combine advanced mathematics and statistics with code. Usually Computer Science graduates with a strong theoretical background.
- Tool developers — More software-engineering focused. They build user-friendly tools on top of existing algorithms and expand their reach.
- Applied analysts — Strong biology background, use and adapt existing tools to drive life sciences research. This is where most biologists entering the field end up.
- Wet lab + tools — Experimental researchers who use specific online or offline tools for data analysis without going deep into the code.
There is no sharp line between these groups, and most people move between them over time. Knowing where you want to start helps you choose what to learn first.
Communities and Staying Motivated
Bioinformatics has a high learning curve. Working on real projects — especially with a mentor — accelerates progress more than working through tutorials alone.
Being part of a community helps with motivation. RSG Turkiye organizes free training events and connects students across Turkey working in bioinformatics and computational biology. Check our GitHub for upcoming events and past materials.
For questions and troubleshooting, Biostars and Stack Overflow remain the most reliable community resources. AI assistants (e.g. ChatGPT) can also be useful for quick explanations and debugging — but verify their output, especially for specialized tools.
Most skill sets in this field mature with practice. Pick a small project, start running analyses, make mistakes, and fix them. That is the fastest path forward.