Modern biology runs on data, and “bioinformatics” has become an unavoidable skill for most life scientists. The good news: you can do meaningful analysis with free, well-documented tools — once you know which ones to start with.
The core skills
- Command line basics: Linux/Mac terminal, navigating directories, file manipulation, piping
- Programming: Python or R for data analysis. Both are essential at intermediate level
- Version control: Git and GitHub for tracking your code
- Workflow management: Snakemake or Nextflow for reproducible pipelines
- Data visualization: ggplot2 (R), seaborn or matplotlib (Python)
Sequence analysis
Alignment
- BLAST: Quick sequence similarity search against databases
- BWA / Bowtie 2: Read alignment to reference genomes
- STAR / HISAT2: RNA-seq read alignment with splice-awareness
- Minimap2: Long-read alignment (PacBio, ONT)
Variant calling
- GATK: Germline and somatic variant calling — the standard
- DeepVariant: Deep learning–based variant caller
- Strelka2: Fast germline and somatic calling
- VEP, ANNOVAR, snpEff: Variant annotation
RNA-seq analysis
- Salmon / kallisto: Pseudo-alignment for fast transcript quantification
- featureCounts / HTSeq: Count reads per gene
- DESeq2 / edgeR (R/Bioconductor): Differential expression analysis
- GSEA / clusterProfiler: Pathway analysis
Single-cell analysis
- Cell Ranger: 10x Genomics processing pipeline
- Seurat (R): Most widely used scRNA-seq analysis
- Scanpy (Python): Python-based equivalent, scales to very large datasets
- Harmony / scVI: Batch correction and integration
Visualization
- IGV: Genome browser for inspecting alignments and variants
- UCSC Genome Browser: Web-based genome browser with rich annotation
- Cytoscape: Network visualization for interactions, pathways
- ggplot2 / matplotlib / seaborn: Programmatic plot creation
- EnhancedVolcano, ComplexHeatmap: Specialized R packages for common figures
Public databases
| Database | Use |
|---|---|
| NCBI | Sequences, genes, literature |
| Ensembl | Genome annotation |
| UniProt | Protein sequences and annotation |
| GTEx | Tissue gene expression |
| TCGA | Cancer genomics |
| GEO / SRA | Public sequencing data |
| ChEMBL / DrugBank | Bioactive compounds |
| STRING | Protein-protein interactions |
| KEGG / Reactome | Pathways |
Recommended learning path
- Learn command-line basics (an afternoon with a Linux primer)
- Pick R or Python — most biology-focused beginners start with R via Posit (RStudio)
- Work through one Bioconductor or Scanpy tutorial end-to-end on real data
- Learn Git for code version control
- Take on a small project: replicate the analysis from a published paper using public data
- Move toward workflow management once you’re managing several pipelines
Free learning resources
- Bioinformatics specializations on Coursera: Johns Hopkins Genomic Data Science series
- Harvard Chan Bioinformatics Core training
- Bioconductor course materials
- Single-Cell Best Practices online book
- Software Carpentry / Data Carpentry workshops
- Galaxy: Web-based bioinformatics for those who don’t want to use the command line
Common beginner pitfalls
- Trying to learn everything before doing anything — start with a real project
- Underestimating the importance of QC at every step
- Running tools blind without understanding their assumptions
- Not version-controlling code from day one
- Hardcoding paths and parameters instead of using configuration
The bioinformatics learning curve is real, but it flattens quickly once you’ve built a working pipeline end-to-end on real data. Pick a project, pick a starter stack, and learn by doing.
