Top 10 Real Commands Every Bioinformatician Uses (and the One They Pretend They Understand)
Top 10 Real Commands Every Bioinformatician Uses (and the One They Pretend They Understand)
Computational biology is full of fancy algorithms and pipelines, but under the hood, bioinformatics work is still powered by a not massive set of terminal commands. These tools move files, slice FASTQs, clean GTFs, rescue broken VCFs, and occasionally save careers.
Below are the real-life workhorses every bioinformatician relies on – plus the one command most people pretend to master!
1. grep – Find Stuff Fast
Search for patterns in files. FASTQ header? Gene name? Weird alignment record? grep finds it.
Example
Why it matters
Debugging pipelines, scanning logs, verifying records. If you don’t know grep, you’re still in the lobby!
2. awk – When You Need to Look Smart
The Swiss army knife for text processing! Field extraction, math, filtering, on-the-fly logic. It could be considered as a programming language itself…
Example
Why it matters
Speed, precision, and zero dependencies. If bioinformatics had a bloodstream, awk would be in it.
3. sed – Text Surgery
Edit text streams: remove characters, replace patterns, reformat tables.
Example
Why it matters
Quick transformations without opening a file. Perfect for data wrangling before pipeline steps.
4. cut – Column Sniper
Extract columns from delimited files. Super useful for TSVs and CSVs.
Example
Why it matters
Fastest way to grab fields without loading R/Python.
5. sort & uniq – Count, Deduplicate, Breathe
Often used together: sorting lists and removing duplicates.
Example
Why it matters
Gene lists, sample IDs, BAM flags – everything ends up sorted eventually.
6. wc – Check Counter
Count lines, words, or characters. As an example, essential for checking FASTQ integrity.
Example
Why it matters
Verifying read counts beats guessing every time.
7. head & tail – Peak Inside Files
Preview large files without loading them.
Example
Why it matters
Quick QC, error hunting, file validation.
8. tar & gzip/bgzip – Compress
Bioinformatics produces huge files. Efficient compression is survival.
Example
Why it matters
Faster data transfer, smaller storage footprint. bgzip + tabix = indexing magic for VCFs & BEDs.
9. samtools – DNA Swiss Knife
View, sort, index, convert, filter BAM/CRAM.
Example
Why it matters
If you touch sequencing data, you use samtools – no exceptions.
10. rsync/scp – The Data Teleporter
Move data across servers safely and efficiently.
Example
Why it matters
When files are 50-500 GB, you don’t trust drag-and-drop.
Bonus: The Command Everyone Pretends They Understand
🥇 awk
Yes, it’s already on the list – but it deserves special mention. Everyone uses it. Few truly master it like me! A veteran can bend reality with awk one-liners; the rest of us google the syntax every time like me!!!
And that’s okay.
Final Thoughts
This toolkit is the backbone of modern bioinformatics. Sure, you’ll graduate to workflow engines, containers, cloud computing, and databases – but these commands stay with you.
Learn them. Practice them. And don’t be ashamed to look up awk syntax for the thousandth time. We all do.
References
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Blischak JD, Davenport ER, Wilson G. A quick introduction to version control with Git and GitHub. PLOS Comput Biol. 2016;12(1):e1004668.
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Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-2079.
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Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10(2):giab008.
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Mousel D, Schatz MC, Langmead B. Practical computing for biologists. Cold Spring Harbor Laboratory Press; 2011.
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Cock PJ, et al. Biopython: freely available Python tools for computational molecular biology. Bioinformatics. 2009;25(11):1422-1423.
