"Should I learn R or Bash first?" is one of the most common questions from biologists entering bioinformatics. The honest answer is that they do different jobs and work together: Bash moves, filters and runs tools on large files, while R analyzes, tests and visualizes results. This guide explains what each is for, how to learn them in a sensible order, the essential commands and packages, how to organize reproducible projects, the usual mistakes, and where Python, Git and workflow managers fit in.
Awkward for statistics and complex data structures
Slower and memory-bound for very large raw files
A usual project: Bash (or a workflow manager) goes from raw reads to count tables, then R goes from count tables to statistics and figures. Learn Bash basics first (a few days), then R (a few weeks to be productive), and add Python when you need it.
2. Why learn programming at all?
Reproducibility: a script can be rerun on new data, while point-and-click steps cannot be audited.
Scale: you cannot open a 50 GB FASTQ in Excel.
Flexibility: web tools cover standard cases; code covers your own question.
Collaboration and review: code can be shared, versioned and checked.
Career: reproducible computing is now expected in genomics and data-driven biology.
3. Part 1: Bash and the Linux command line
Why Bash?
Nearly all bioinformatics tools (BWA, samtools, STAR, bcftools, bedtools, IQ-TREE) are command-line programs, and servers and clusters run Linux. On Windows use WSL2 (Windows Subsystem for Linux); macOS and Linux have a terminal built in.
Essential commands
Task
Command
Example
Where am I, list files
pwd, ls -lh
ls -lh data/
Move around
cd
cd ~/project/data
Create, copy, move
mkdir -p, cp, mv
mkdir -p results/qc
Delete (careful, no trash)
rm, rm -r
rm tmp.txt
View files
head, tail, less, zcat
`zcat reads.fq.gz
head -n 8`
Count
wc -l
`zcat reads.fq.gz
wc -l`
Search text
grep
grep -c "^>" genome.fa
Columns of tables
cut, sort, uniq, awk
`cut -f1 file.tsv
sort
uniq -c`
Replace text
sed
sed 's/chr//' in.bed > out.bed
Combine commands
pipe `
, redirect > and >>`
`grep ">" x.fa
wc -l > n.txt`
Find files
find
find . -name "*.bam"
Disk space
du -sh, df -h
du -sh *
Permissions
chmod
chmod +x script.sh
Download
wget, curl
wget URL
Compress
gzip, tar
tar -czf out.tar.gz folder/
Check a process
top, htop, ps
htop
A first useful script
#!/usr/bin/env bash
set -euo pipefail # stop at the first error, undefined variable or failed pipe
mkdir -p results/fastqc
for fq in data/*.fastq.gz; do
sample=$(basename "$fq" .fastq.gz)
echo "Processing $sample"
fastqc -o results/fastqc "$fq"
done
Concepts shown here: variables, loops, command substitution $(...), quoting "$var", and safe-mode set -euo pipefail.
Bash tips that save you from disasters
Quote variables ("$file") so spaces do not break commands.
**Never run rm -rf with a variable** that could be empty.
Keep raw data read-only (chmod -w) and write results to separate folders.
Use **tmux or screen for long jobs so they survive a disconnection, and job schedulers** (SLURM) on clusters.
Log everything: command > out.log 2> err.log.
Use conda or mamba with bioconda to install tools in isolated environments, instead of installing system-wide.
R was built for statistics and has the richest ecosystem for omics: Bioconductor hosts thousands of packages for sequencing, microarray, proteomics and annotation, and ggplot2 produces publication-quality figures.
Getting set up
Install R and RStudio (or Positron, VS Code). Create an RStudio project per analysis, so paths are relative and the work is portable.
The core ideas
# Vectors and data frames
counts <- c(10, 20, 15)
df <- data.frame(sample = c("A", "B", "C"), group = c("ctrl", "ctrl", "treat"), value = counts)
# Read a table, inspect it
meta <- read.csv("metadata.csv")
str(meta); summary(meta); head(meta)
# Tidyverse workflow
library(tidyverse)
meta %>%
filter(group == "treat") %>%
group_by(batch) %>%
summarise(n = n(), mean_value = mean(value))
# A plot
ggplot(df, aes(group, value, fill = group)) +
geom_boxplot() + geom_jitter(width = 0.1) +
theme_classic()
Replicates and pseudoreplication: the unit of replication is the independent biological sample.
p-values and multiple testing: with 20,000 genes, use adjusted p-values (Benjamini-Hochberg FDR).
Effect size versus significance: report fold change and confidence interval, not only p.
Normalization and batch effects: raw counts or raw intensities are not comparable between samples.
Choose tests that match data distribution (counts need negative-binomial or similar models, not a t-test on raw counts).
Visualize before testing: boxplots, PCA, histograms.
5. Where Python, Git and workflow managers fit
Tool
Why it helps
Python (pandas, Biopython, scikit-learn, Scanpy)
General programming, machine learning, single-cell with Scanpy, scripting
Git and GitHub
Version control, collaboration, backup of code; never commit large raw data
Snakemake or Nextflow (nf-core pipelines)
Reproducible, scalable pipelines with automatic reruns and cluster support
Docker, Apptainer, conda
Reproducible software environments
Jupyter, Quarto, R Markdown
Documents combining code, results and text
SQL
Querying structured databases
6. Reproducible project structure
project/
README.md # what the project is and how to run it
data/
raw/ # untouched, read-only
processed/
metadata/ # sample sheets
scripts/ # numbered: 01_qc.sh, 02_align.sh, 03_deseq2.R
results/
tables/
figures/
envs/ # environment.yml, renv.lock
logs/
Rules that pay off:
Never edit raw data.
Everything from raw to figure should be scripted.
Record versions (conda export, sessionInfo() in R).
Use relative paths and avoid hard-coded locations.
Set random seeds (set.seed(1)) for anything stochastic.
Write a README a colleague (or you, in six months) can follow.
7. A realistic learning path
Stage
Goal
Typical time
1
Terminal basics: navigation, files, grep, pipes, small loops
1 to 2 weeks
2
Install tools with conda, run FastQC and an aligner on a test dataset
1 to 2 weeks
3
R basics and tidyverse, plotting with ggplot2
3 to 4 weeks
4
A full mini-project: public RNA-seq dataset, QC, counts, DESeq2, figures
4 weeks
5
Git, Snakemake or Nextflow, Quarto reports
ongoing
6
Python or advanced topics according to your field
as needed
Practice with public datasets (GEO and SRA) and with exercises from tutorials, and read other people's code (nf-core pipelines, Bioconductor vignettes).
A practical sequence for learning the tools of bioinformatics (schematic made for Bioeduc).
8. Common mistakes
Mistake
Consequence
Better practice
Modifying files by hand in Excel
Gene names turned into dates, no record of changes
Edit with code, export CSV or TSV, keep the raw file
Copy-pasting commands without understanding
Silent errors, wrong results
Read each command, test on a small subset
Not saving the commands used
Impossible to reproduce
Save scripts and logs
Mixing genome builds or annotation versions
Wrong coordinates and counts
Record versions and use one consistently
Ignoring warnings
Hidden problems
Read warnings and investigate
Running everything on the login node of a cluster
Account warnings, slow jobs
Submit jobs with the scheduler
Testing raw counts with a t-test
Invalid statistics
Use count-appropriate models (DESeq2, edgeR)
Using absolute paths
Code breaks on another computer
Use project-relative paths
Installing everything in the base environment
Version conflicts
One conda environment per project
Treating tools as black boxes
Misinterpreted outputs
Read the documentation and the paper
9. Troubleshooting for beginners
Message or symptom
Meaning
Fix
command not found
Program not installed or not in PATH
Activate the conda environment or install the tool
Permission denied
No right to run or write
chmod +x, or check folder ownership
No such file or directory
Wrong path or typo
Check with ls and pwd, use tab completion
Error in library(x): there is no package called x (R)
Package missing
install.packages() or BiocManager::install()
object 'x' not found (R)
Variable not created or typo
Run earlier lines, check names with ls()
Package installation fails (R)
Missing system libraries or old R
Update R, read the error for the missing library, try conda
Columns read as factors or characters
Wrong delimiter or encoding
Use read_tsv, read_csv, check str()
Script stops with Killed
Out of memory
Use more memory, subset data, or process in chunks
Differences between your result and a tutorial
Different software versions
Pin versions and compare with the tutorial's sessionInfo()
Windows line endings break scripts
CRLF characters
dos2unix script.sh
10. Best-practice checklist
Learn Bash basics first, then R, then add Python and Git as needed.
Use conda or mamba environments and record versions.
Keep raw data untouched and script every step.
Organize projects with the same folder structure every time.
Understand the statistics behind the tools you use.
Start from small test data before running full datasets.
Ask for help with a minimal reproducible example.
11. Frequently asked questions
Do I need to learn R and Python? Not at once. R is excellent for statistics and Bioconductor; Python is strong for general programming, machine learning and Scanpy. Pick the one your field uses and learn the second later.
Can I do bioinformatics without the command line? Galaxy and web servers allow it for standard analyses, but flexibility, scale and reproducibility are limited.
How long to become productive? Around two to three months of regular practice for basic analyses, with continuous learning after that.
Are AI assistants useful for coding? They can explain errors and draft code, but you must test and understand the output, especially statistics and file formats, and never share private or patient data.
Where do I ask for help? Biostars, Bioconductor support, the Stack Exchange network, tool GitHub issues and community forums. Include your code, error message and sessionInfo().
12. References and resources
Wilson G et al. (2017). Good enough practices in scientific computing. PLoS Computational Biology 13:e1005510.
Sandve GK et al. (2013). Ten simple rules for reproducible computational research. PLoS Computational Biology 9:e1003285.
Huber W et al. (2015). Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods 12:115-121.
Amezquita RA et al. (2020). Orchestrating single-cell analysis with Bioconductor. Nature Methods 17:137-145.
Wickham H et al. (2019). Welcome to the tidyverse. Journal of Open Source Software 4:1686.
Grüning B et al. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods 15:475-476.
Mölder F et al. (2021). Sustainable data analysis with Snakemake. F1000Research 10:33.
Di Tommaso P et al. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology 35:316-319.
Ewels PA et al. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology 38:276-278.
Bryan J (2018). Excuse me, do you have a moment to talk about version control? The American Statistician 72:20-27.
Zeeberg BR et al. (2004). Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics. BMC Bioinformatics 5:80.
Wickham H, Grolemund G. R for Data Science (free online book, r4ds.hadley.nz).
Shotts W. The Linux Command Line (free online book).
Software Carpentry and Data Carpentry lessons (swcarpentry.github.io, datacarpentry.org).
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