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Chapter 4: Introduction to the Command Line for Biologists

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4.1 Breaking the GUI Habit

Illustration of CLI vs GUI

4.3 Practical CLI Best Practices

A bioinformatics environment is built on reliable command-line workflows. Key recommendations:

  • Environments: Use conda/mamba to manage packages and isolate environments.
  • Containers: Ship reproducible analysis with Docker or Singularity/Apptainer to freeze software stacks.
  • Workflow managers: Combine CLI tools with Snakemake or Nextflow for reproducibility and scalability.
  • Useful utilities: jq for JSON, csvkit for CSV, htop for processes, and tmux for session management.

Example: quickly view a BAM header and index it:

# View header
samtools view -H sample.bam

# Sort and index (recommended post-alignment)
samtools sort -o sample.sorted.bam sample.bam
samtools index sample.sorted.bam

Use containers when distributing pipelines; include Dockerfile or Singularity recipes in the repo.

Up until now, you have likely interacted with computers using a Graphical User Interface (GUI)—clicking icons, dragging folders, and using menus. While intuitive, GUIs have limits. They are hard to automate, struggle with massive files (try opening a 50GB genome file in Excel!), and are often unavailable on the powerful remote servers where actual bioinformatics work happens.

Enter the Command Line Interface (CLI), also known as the terminal or shell. It might look intimidating—a black screen with blinking text—but it is the most powerful tool in your arsenal.

4.2 Navigation: Finding Your Way

When you open a terminal, you are "standing" in a specific folder on your computer.

Where am I? (pwd)

pwd stands for Print Working Directory. 

$ pwd
/Users/biologist/data

What is here? (ls)

ls Lists the files in your current directory. 

$ ls
genome.fasta  notes.txt  raw_data/

Go somewhere else (cd)

cd stands for Change Directory. 

$ cd raw_data
$ pwd
/Users/biologist/data/raw_data
Tip: cd .. moves you "up" one folder.

4.3 Handling Files: The Basics

Bioinformatics involves moving, renaming, and organizing thousands of files.

  • mkdir analysis: Make a directory (folder) named "analysis".
  • cp gene.txt gene_backup.txt: Copy a file.
  • mv gene.txt analysis/: Move a file into a folder (also used to rename files).
  • rm junk.txt: Remove (delete) a file. Warning: There is no Trash Can in the terminal. Deleted files are gone forever.

4.4 Inspecting Biological Data

Biological data files (like FASTA or FASTQ) are often massive text files. You don't want to open them in a text editor; it will crash your computer. Instead, we peek at them.

head and tail

View the first or last 10 lines of a file. 

$ head genome.fasta
>chr1
ATGCGTAC...

less

Allows you to scroll through a huge file page by page without loading the whole thing into memory. Press q to exit.

wc

Word Count. Counts lines, words, and characters. 

$ wc -l genome.fasta
50000 genome.fasta
(This tells us the file has 50,000 lines).

4.5 The Power Tools: grep and Pipes

This is where the magic happens.

grep searches for a specific pattern in a file. Imagine you want to find a specific gene ID in a massive annotation file. 

$ grep "TP53" annotations.gff

The Pipe (|)

The pipe takes the output of one command and passes it as input to the next. It allows you to chain tools together.

Scenario: How many sequences are in my FASTA file? In a FASTA file, every sequence header starts with a >. We can find all headers with grep, and then count them with wc.

$ grep ">" sequences.fasta | wc -l
450
We just counted the number of sequences in a file without ever opening it!