Hello Bioinformatics lovers,

Whether you're just starting or deep into bioinformatics, one question always looms large:
​Which tools should you master first?

R vs Python: Which Should You Learn?

Many beginners ask me this, and honestly, the answer depends on your needs. Let me share my journey:

I started learning Unix commands over a decade ago, followed by Python through Python for Absolute Beginners. While Python taught me programming fundamentals (like print("hello world")), it wasn’t solving the real bioinformatics problems I faced.

Why?

At the time, Python lacked user-friendly libraries for working with data tables (pandas never ticked for me). My work required handling large spreadsheets, so I turned to R.

R's built-in support for dataframes made it perfect for bioinformatics tasks. And with the tidyverse, complex data wrangling became simple. Even today, ggplot2 remains one of the best tools for visualizations in bioinformatics.

R’s Bioconductor ecosystem is another game-changer. From RNA-seq (think DESeq2) to single-cell analysis, it offers countless well-tested packages tailored for biologists.

But R has limitations:

• It struggles with large datasets due to its in-memory nature.
• Handling millions of rows can be a challenge unless you use tools like DelayedArray.

On the other hand, Python is a general-purpose language. While it has fewer specialized bioinformatics tools, it excels in deep learning, algorithm development, and scalability.

So, R or Python?

Here’s my advice:

• If you're a biologist looking for quick analyses, start with R.
• If you want to dive into serious programming or deep learning, add Python to your toolkit.

Ultimately, both languages are valuable. Learn one well, and the other will come more easily!

Why Every Bioinformatician Needs Unix Skills

While R and Python are great for scripting and analysis, bioinformatics revolves around text files.

From FASTA to SAM files, everything is text-based, and Unix commands are essential for wrangling them efficiently.

Here are a few Unix tricks every bioinformatician should know:

1️⃣ Reverse complement a sequence in one line:

echo'ATTGCTATGCTNNNT' | rev | tr'ACTG''TGAC'

Output: ANNNAGCATAGCAAT

• rev: reverses the sequence
• tr: translates A ↔ T, C ↔ G to get the complement

2️⃣ Convert a comma-delimited file to tab-delimited:

cat file.csv | tr",""\t"

Why? Many bioinformatics tools expect tab-delimited input, like BED files. A simple Unix command can reformat your data without the need for Excel or fancy GUIs.

3️⃣ Split a column like chr1_1000_2000 into multiple columns:

cat file.txt | tr"_""\t"

Unix commands like rev, tr, cut, awk, sed, and grep will save you hours of repetitive work.

Bottom Line

Whether you're wrangling data with Unix, analyzing it in R, or scaling up with Python:
👉 Use the best tool for the task at hand.
👉 Master the basics before diving into advanced techniques.

Tip: Want to learn more Unix tricks? I’ll be sharing one every week—stay tuned!

Happy learning,

Tommy aka. Crazyhottommy

Other posts from the past week:

1. Want to assign unique IDs to combinations of multiple columns in an R dataframe? Here's a quick R trick. Why is it useful for bioinformatics?

2. Rstats trick: use case_when(). I use it almost every week. No more nested if else

3. A popular course on Bash scripting on Linux.

4. When should you use a package vs. solving a problem from scratch in bioinformatics? I get asked this a lot. Here’s my approach.​

5. What happens when biology and computer science collide? A conversation with a colleague revealed just how different these fields can be. Here's why understanding both is crucial for the future of research.

​

PS:

I made this youtube video on how to get the sum of exon length for each gene using Bioconductor

annotation packages.

PPS:

If you want to learn Bioinformatics, there are other ways that I can help:

1. My free YouTube Chatomics channel, make sure you subscribe to it.
2. I have many resources collected on my github here.
3. I have been writing blog posts for over 10 years https://divingintogeneticsandgenomics.com/​

​

Stay awesome!