As I began exploring the intersection between biology and programming, I kept running into the same question:
How do I actually get started with bioinformatics coding?
This post is a summary of what I’ve been learning so far, written from the perspective of someone coming from outside the field, trying to understand how real-world genomic projects work.
🧰 The Core Trio: Bash, Python & R
1. Bash – Speaking to the system
Across forums and tutorials, one piece of advice keeps showing up: learning Bash and the Linux terminal is essential. It lets you filter, move, and process genomic files at scale.
A typical example:
zcat sample.fastq.gz | awk 'NR%4==0 && length($1) > 20' > cleaned.fastqThis script removes poor-quality reads from a FASTQ file. It’s not fancy, but it’s foundational for avoiding downstream errors.
Recommended resources:
2. Python – Data wrangling and automation
Python is versatile and great for:
- Reading
.fastaor.vcffiles - Automating steps in a workflow
- Using libraries like Biopython
- Connecting with other tools
If you already know pandas, you’ll feel at home. In bioinformatics, tools like Bio, pysam, or scikit-bio can help a lot.
Learn here:
3. R – Visualizing results clearly
R is still the go-to for clean statistical graphics and final figures:
ggplot2,DESeq2,edgeRfor expression analysisphyloseqfor microbiome studies- Bioconductor is a great ecosystem
Learn from:
- HarvardX Data Science with R
- R Graphics Cookbook by Winston Chang
🔎 How do I know which tools to use?
A very common recommendation:
Look at recent papers similar to your project, note which tools they used, and start there.
Sites like quay.io or Bioconda let you pull many of these tools in ready-to-run containers.
🧪 Mini-project ideas to practice
| Project | Tools | Goal |
|---|---|---|
| FASTQ filtering | Bash + awk | Clean noisy reads |
| FASTA parser | Python + Biopython | Extract sequences by ID |
| RNA-seq plots | R + DESeq2 | Visualize gene expression |
| BLAST automation | Python + subprocess | Search sequences against databases |
| Microbiome diversity | R + phyloseq | Plot alpha/beta diversity metrics |
🤯 Feeling overwhelmed?
That seems to be part of the learning curve.
Bioinformatics includes:
- Biological complexity
- Messy, large-scale data
- Dozens of tools for similar tasks
The best tip I found was:
Don’t learn everything. Pick one thing and learn it like you’ll teach it.
đź“š References I found helpful
- Nathan Frey – Getting into BioML
- Biostars – How to start in bioinformatics
- edX – Bioinformatics Courses
This is just the beginning. I’ll keep posting what works (and what doesn’t) as I go.
If you’re just starting too, I hope some of this helps you find your bearings.
🧬