Basic Linux for Bioinformatics: Commands You’ll Use Daily

A Beginner-Friendly Guide with Practical Examples and Cheat Sheet

Introduction

In the world of bioinformatics, Linux is more than just an operating system — it’s the foundation of your research workspace. Whether you’re analyzing sequencing data, parsing large FASTA files, or automating complex workflows, Linux and the command line are at the heart of it all.

You might be wondering: "Why can’t I just use Windows or macOS?"

Well, while those systems are great for general use, most bioinformatics tools — especially the powerful, open-source ones — are designed to run on Linux or Unix-like systems. Not to mention, Linux is highly efficient when dealing with huge datasets, which is common in genomics and computational biology.

For beginners, the Linux terminal might feel intimidating at first. But the good news? You don’t need to learn everything. Just a handful of basic commands will empower you to move around files, extract useful information from large datasets, and start running bioinformatics programs confidently.

In this blog, you'll learn:

• What Linux commands are essential for bioinformatics

• How to use them with real-world examples

• Simple one-liners that can save you hours of manual work

• Why learning the command line is crucial for scripting and pipeline automation

No fluff. Just practical, daily-use commands explained in a way that makes sense. By the end of this post, you'll be able to say:

“I can handle my own data on the Linux command line.”

Let’s turn that black screen with green text into your superpower.

Why Learn Linux for Bioinformatics?

If you're diving into bioinformatics, one of the first skills you must develop is working with Linux. You might think it’s just another operating system, but in the world of biological data analysis, Linux is your most powerful ally. Here’s why:

1. Most Bioinformatics Tools Are Built for Unix/Linux Systems

The vast majority of open-source bioinformatics software — including tools like BWA, HISAT2, SAMtools, and BEDTools — are developed and optimized for Linux or Unix-like environments. These tools are typically run from the command line, and many don’t even have a graphical interface.

Learning Linux ensures that you can install, run, and troubleshoot these tools easily. Windows support is rare, and even macOS (which is Unix-based) doesn’t always play well with everything. On Linux, these tools are native.

2. High-Performance Computing (HPC) Environments Run on Linux

When your datasets get too large for your laptop (think whole-genome sequences or metagenomics projects), you'll need to shift your analysis to servers, cloud platforms, or HPC clusters. These environments almost always run on Linux.

Being comfortable with Linux means you can:

• Navigate remote servers via SSH

• Submit and manage jobs with tools like SLURM

• Access storage drives, transfer files, and automate scripts efficiently

Without Linux knowledge, you're locked out of scalable computing.

3. Command Line Is Fast, Scriptable, and Reproducible

The command line might look old-school, but it’s the most precise, efficient, and reproducible way to handle tasks.

For example:

• Instead of manually opening and filtering a file in Excel, you can use grep or awk to extract what you need in seconds.

• You can write shell scripts to automate entire workflows and share them with collaborators — enabling reproducibility and transparency.

• Tools like bash, zsh, or tmux let you run long processes, even over days, with logging and scheduling.

This power is essential in research, where reproducibility and efficiency matter.

4. Graphical Interfaces Often Rely on Linux Behind the Scenes

Even if you're using web-based platforms like Galaxy, GenePattern, or BaseSpace, those interfaces are just frontends. Behind the scenes, the actual analysis is being run by Linux-based systems.

Understanding what’s happening under the hood helps you:

• Troubleshoot when jobs fail

• Understand the output directories

• Customize tools and workflows

In short, you’re not just clicking buttons — you’re making informed decisions.

5. Linux Gives You Full Control Over Your Data

Unlike GUI-based software, the Linux command line allows you to precisely control how your data is handled:

• You can combine commands (pipes) to do complex tasks in a single line

• You can track every step with logging

• You can work with files that are too large to open in Excel or R

Bioinformatics datasets are often huge, and traditional tools just can’t handle them efficiently.

6. Linux Is Free, Open-Source, and Community-Driven

Linux isn’t just powerful — it’s free. You don’t need a license, and you can install it on virtually any computer. The bioinformatics and Linux communities are massive and highly active. That means:

• Tons of free tutorials and forums (like Stack Overflow, SEQanswers, Biostars)

• Open-source contributions keep tools updated

• You’re never really stuck — someone else has probably solved your issue before

Plus, platforms like WSL (Windows Subsystem for Linux) even let you use Linux inside Windows — so you don’t need to dual-boot or buy a new system to get started.

Learning Linux isn’t optional for bioinformatics — it’s essential. It’s your gateway to using powerful tools, automating complex analyses, and scaling your research efficiently. The sooner you start using Linux, the faster you’ll grow as a confident bioinformatics researcher.

Prerequisites: What You Need to Get Started with Linux for Bioinformatics

Before you dive into using Linux commands for bioinformatics, here are a few things you should have or know to make your learning smooth and effective:

1. Access to a Linux Terminal

Most bioinformatics tools are designed to run on Unix-like systems. So, having a terminal to practice is essential. You can use:

• Ubuntu/Linux OS: If you have a Linux machine, you're already set.

• Windows Subsystem for Linux (WSL): If you're on Windows, you can install Ubuntu through WSL to access a Linux environment without dual-booting.

• macOS Terminal: macOS is Unix-based, so you can use its Terminal app directly.

• Cloud-based Linux: Platforms like Google Colab (for Python) or Replit also support Linux commands to some extent.

2. Basic Understanding of Files and Directories

Linux is all about working with files and directories through the command line. You don’t need to be an expert — just understand:

• What is a file?

• What is a folder (directory)?

• What is a file path?

• What are file extensions (.txt, .fasta, .csv, etc.)?

This helps when navigating your project folders or writing automation scripts.

3. A Curious Mind and Patience

Don’t worry if it looks intimidating at first — Linux has a learning curve, but it’s highly logical. You'll build confidence as you practice. Curiosity and a bit of patience go a long way in learning Linux.

4. A Text Editor (Optional, but Useful)

Having a lightweight text editor like nano, vim, or even using Visual Studio Code (VS Code) can help when you're editing scripts or configuration files directly on your system.

5. Interest in Reproducible Science

Bioinformatics is about processing large amounts of data reproducibly. Learning Linux gives you the power to automate tasks, build reproducible pipelines, and handle big datasets efficiently — all of which are crucial in scientific computing.

6. A Willingness to Google Errors

One of the best skills you can build alongside learning Linux is how to search for solutions. When you encounter errors, knowing how to read them and look them up is a major part of working like a bioinformatician.

Linux Command Cheat Sheet with Real Bioinformatics Tasks 

1. pwd — Print Working Directory

• What it does: Displays the full path of your current location in the file system.

• Why it's useful: When working with deep directory structures (e.g., /home/user/bioinformatics/project1/data/), you often need to confirm where you are before running scripts or accessing files.

• Bioinfo Example: You're inside a project folder and want to make sure you're in the /data/reads/ directory before processing .fastq files.

2. ls — List Files and Directories

• What it does: Lists all files and folders in the current directory.

• Why it's useful: Lets you check whether a file (like genome.fasta, sample.fastq, or results.txt) exists before processing.

• Bioinfo Example: Use ls *.fastq to see all sequencing reads available for QC.

3. cd — Change Directory

• What it does: Moves you into another directory.

• Why it's useful: You’ll constantly navigate through folders during analysis.

• Bioinfo Example: cd ~/projects/genomics/assemblies/ to enter your genome assembly directory.

4. mkdir — Make Directory

• What it does: Creates a new folder.

• Why it's useful: Helps organize your workflow outputs (e.g., keeping alignments, annotations, and plots in separate folders).

• Bioinfo Example: mkdir qc_reports/ to save FastQC output files.

5. cp — Copy Files

• What it does: Copies files or folders from one place to another.

• Why it's useful: Create backups of raw data or scripts before making changes.

• Bioinfo Example: cp sample1.fastq raw_data/ to make a backup copy before trimming.

6. mv — Move or Rename

• What it does: Moves files or renames them.

• Why it's useful: Useful for organizing files or correcting filenames.

• Bioinfo Example: mv results.txt variant_results.txt to make filenames more descriptive.

7. rm — Remove Files

• What it does: Deletes files or folders.

• Why it's useful: Helps clear temp files and reduce clutter.

• Bioinfo Example: rm temp_output.sam to delete intermediate files after converting to BAM.

Caution: rm is irreversible unless you use a trash manager or alias with -i for confirmation.

8. head / tail — View Top/Bottom of Files

• What it does: Shows the first or last few lines of a file.

• Why it's useful: Quickly check if data looks correct or check file format without opening the whole file.

• Bioinfo Example: head reads.fastq to verify the quality score format of your sequencing data.

9. cat — Concatenate and View File

• What it does: Displays the full content of a file.

• Why it's useful: Simple way to read small files or concatenate multiple files.

• Bioinfo Example: cat genome.fasta to read the full DNA sequence (for short files).

10. less — View File with Scrolling

• What it does: Opens large files one page at a time, with scroll capability.

• Why it's useful: Perfect for viewing annotation files, FASTA, or log files.

• Bioinfo Example: less annotation.gff lets you scroll through genome features.

11. grep — Search Text

• What it does: Searches for a string/pattern in files.

• Why it's useful: Extract specific sequences, genes, or error messages.

• Bioinfo Example: grep "ATG" genome.fasta to find start codons in a genome.

You can combine with -n to show line numbers or -v to invert the match.

12. wc — Word Count

• What it does: Counts lines, words, and characters in a file.

• Why it's useful: Useful to count reads, genes, or any entries in a file.

• Bioinfo Example: wc -l reads.fastq gives total number of lines (can be divided by 4 to count reads).

13. awk — Pattern Scanning and Processing

• What it does: Processes and extracts columns or patterns from text files.

• Why it's useful: Handles structured text (like tab-delimited gene tables).

• Bioinfo Example: awk '{print $1}' genes.tsv prints the first column (e.g., gene IDs).

You can also filter: awk '$3 == "CDS" {print $0}' annotation.gff to get all coding sequences.

14. sed — Stream Editor

• What it does: Performs text transformations like search and replace.

• Why it's useful: Batch editing headers, modifying data formats.

• Bioinfo Example: sed '/^>/d' genome.fasta removes FASTA headers (for just sequence content).

15. chmod — Change Permissions

• What it does: Modifies file permissions (like read, write, execute).

• Why it's useful: Essential for making scripts executable.

• Bioinfo Example: chmod +x run_pipeline.sh allows your bash script to be run like a program.

16. top — Monitor System Usage

• What it does: Displays real-time system resource usage.

• Why it's useful: Helps you check CPU/RAM usage during heavy analysis.

• Bioinfo Example: Run top while executing a genome assembly or large alignment.

17. history — Command History

• What it does: Shows your previously run commands.

• Why it's useful: Allows you to repeat or document your workflow.

• Bioinfo Example: You ran a long command yesterday and forgot the exact syntax — history | grep fastqc retrieves it.

Real-World Bioinformatics Use Cases (with Explanations)

1. Counting the number of reads in a FASTQ file

bash
wc -l sample.fastq

• Explanation: wc -l counts the number of lines in a file. A FASTQ file has 4 lines per read (header, sequence, plus line, and quality score).

• So: If wc -l returns 40,000 lines, it means you have 40,000 / 4 = 10,000 reads.

2. Finding all genes in a GFF (General Feature Format) file

bash
grep -i "gene" annotation.gff

• Explanation: grep searches for lines containing a pattern.

• -i makes it case-insensitive (so it finds "Gene", "gene", or "GENE").

• Use Case: GFF files store gene and genome feature annotations. This command finds all lines where genes are annotated.

3. Extracting FASTA headers

bash
grep ">" genome.fasta

• Explanation: In FASTA files, header lines start with >. This command pulls out just those headers (like sequence IDs).

• Use Case: Helpful to list sequence names before processing them further.

4. Remove all files except FASTA files

bash
rm !(*.fasta)

• Explanation: This removes all files except those ending in .fasta.

• ⚠️ Note: This only works if extended globbing is enabled. To enable:

  bash
  shopt -s extglob
  

• Use Case: Useful when cleaning up directories to keep only relevant sequence files.

5. Get the number of sequences in a FASTA file

bash
grep -c "^>" genome.fasta

• -c: Count number of matching lines

• ^>: Ensures it only matches lines starting with >, which are FASTA headers

• Use Case: Counts the number of sequences in the FASTA file.

6. Preview the first 5 reads in a FASTQ file

bash
head -n 20 reads.fastq

• Explanation: Since each read = 4 lines, 5 reads = 20 lines.

• Use Case: Quick sanity check on the format and quality of reads.

7. Move all FASTQ files into a reads/ folder

bash
mv *.fastq reads/

• *.fastq: Matches all files ending in .fastq

• reads/: Destination directory

• Use Case: Organizing sequencing data files before analysis.

8. Replace "chr" with "chromosome" in a file

bash
sed 's/chr/chromosome/g' genome.gff > new_genome.gff

• sed: Stream editor for search/replace

• s/chr/chromosome/g: Substitutes every instance of "chr" with "chromosome"

• > new_genome.gff: Saves output to a new file

• Use Case: Normalize chromosome naming conventions.

9. Extract only column 2 from a tab-separated annotation file

bash
awk '{print $2}' annotations.tsv

• Explanation: awk breaks each line into fields. $2 prints the second column.

• Use Case: For example, extract gene names from a column.

10. Monitor system resource usage during a large BLAST run

bash
top

• Explanation: Launches a dynamic view of CPU/memory usage. Useful to monitor your BLAST or InterProScan jobs on Linux.

• Press q to exit.

11. Check the size of a genome FASTA file

bash
ls -lh genome.fasta

• -l: Long listing

• -h: Human-readable format (KB, MB)

• Use Case: Estimate data size before running heavy analyses.

12. Show your past commands for reuse

bash
history | grep fastqc

• Explanation: Shows previous commands used, useful for reproducibility.

• grep fastqc: Filters history for lines where you used FastQC.

Summary

Using Linux in bioinformatics isn’t just for power users — it’s a core skill. These commands help you:

• Navigate data directories

• Automate tasks like parsing and cleanup

• Perform quick data exploration

• Save time and reduce manual error

If you learn just a handful of these and use them regularly, you'll become way more efficient as a bioinformatics researcher.

 How to Practice Linux Commands for Bioinformatics

Learning Linux can seem intimidating at first, but with the right setup and resources, you'll get comfortable quickly. Here's a breakdown of the most practical ways to learn and practice essential Linux commands, specifically tailored to bioinformatics.

1. Use WSL (Windows Subsystem for Linux)

What it is:

WSL allows Windows users to run a full Linux environment (like Ubuntu) directly from Windows without needing a virtual machine or dual boot.

Why it’s great for beginners:

You can open a terminal and start using Linux commands without leaving Windows. Most bioinformatics tools that work on Linux can be installed on WSL too.

Bioinfo Practice Tip:

Install tools like samtools, bedtools, or blast+ and use small datasets to practice analyzing sequencing files.

How to get started:

Search "Install WSL Ubuntu" on the Microsoft Docs site or click here.

2. Install Ubuntu (or other Linux distro) on Your Computer

What it is:

Ubuntu is a beginner-friendly Linux operating system. You can install it alongside Windows (dual boot), or on an old laptop.

Why it’s great for bioinformatics:

Most real-world bioinformatics work is done on Linux servers. Practicing on Ubuntu gives you the closest experience to working on research clusters or cloud environments.

Bioinfo Practice Tip:

Try parsing FASTA files using grep, awk, and sed, or automate folder creation and data movement with scripts.

3. Use Online Terminals like Google Cloud Shell

What it is:

Google Cloud Shell is a free browser-based Linux terminal offered by Google Cloud Platform.

Why it's useful:

You don’t need to install anything. You get a persistent shell with 5 GB storage and access to many pre-installed tools.

Bioinfo Practice Tip:

Clone a public GitHub repository containing sample genome data and use ls, cat, grep, etc., to explore it.

https://shell.cloud.google.com

4. Try Jupyter Notebooks with Shell Support

What it is:

Jupyter notebooks can also run shell commands using !command syntax in code cells.

Why it's good for learners:

You can mix Markdown explanations, code, and Linux commands in one interface.

Bioinfo Practice Tip:

Create notebooks where you document your use of commands like grep, awk, and head while analyzing example FASTQ files.

5. Play with Command-Line Challenges on Rosalind.info

What it is:

Rosalind offers problems based on computational biology. Some require Linux-like logic or command-line thinking.

Why it's fun:

It feels like solving puzzles with biology. You can simulate real bioinformatics tasks (e.g., string search, sequence parsing).

Bioinfo Practice Tip:

Solve problems like DNA string matching using shell commands or combine with Python/R when needed.

Use Bioinformatics Datasets from NCBI, EMBL-EBI, or Ensembl

What it is:

These are public biological databases that offer free access to sequence data, annotations, and more.

Why it matters:

Practicing on real datasets helps you understand the size, structure, and complexity of bioinfo data.

Bioinfo Practice Tip:

Download a small .fasta genome or .gff annotation file, and run commands like:

bash
grep -c ">" genome.fasta      # Count number of sequences
awk '$3=="gene"' genome.gff   # Extract only gene annotations

7. Use Public Docker Images (Optional Advanced)

What it is:

Docker allows you to run pre-configured environments. You can pull containers with all bioinformatics tools pre-installed.

Why it’s useful:

Good for intermediate users who want to test pipelines or tools like Galaxy, Nextflow, etc., in isolated environments.

Bioinfo Practice Tip:

Pull a container like biocontainers/blast and try running BLAST locally on a small test dataset.

8. Join Online Bioinformatics Platforms or Courses

🎓 Examples:

• Bioinformatics Workbook

• Coursera Linux for Bioinformatics

• edX Genomic Data Science

📘 Why it helps:

You get guided, hands-on instruction and exercises focused on real bioinformatics workflows.

Conclusion

Linux isn’t just an operating system — it's the backbone of most modern bioinformatics research. From downloading genome assemblies to running complex analytical pipelines, command-line tools empower you to handle large datasets quickly, reproducibly, and efficiently. By learning even the basic Linux commands, you take your first step toward becoming an independent and capable bioinformatician.

Understanding commands like grep, awk, and sed allows you to manipulate files and data on the fly — a must-have skill when working with massive genomic files. The command-line interface (CLI) also enhances transparency and automation, making your research more robust and reproducible.

Whether you're accessing your university cluster, configuring a pipeline on Galaxy, or building your own tool with Bash scripting, Linux is where it all begins. The more familiar you get with navigating files, parsing data, and chaining commands together, the more efficient and confident you’ll become in your bioinformatics journey.

So don't be intimidated — start small, play around with real datasets, make mistakes, and learn by doing. Every command you master brings you closer to becoming a Linux-savvy bioinformatician.

💬 Let’s Discuss

What’s one Linux command you wish you learned earlier in your bioinformatics journey — and how did it change the way you work? OR 

Do you prefer GUI tools or command-line workflows in bioinformatics? Why?

Drop your thoughts, questions, or favorite tips in the comments below!