Before you start
Learning objectives
By the end of this lesson you will be able to: define the core biology vocabulary a coder meets constantly, walk through the central dogma of DNA to RNA to protein, and read common biological terms without getting lost.
If you're comfortable with code but fuzzy on biology, good news: the central ideas map astonishingly well onto things you already understand. Let's build the whole picture from one analogy.
DNA is source code
Every cell carries a complete copy of an organism's instructions, written in a 4-letter alphabet: A, C, G, T. That's DNA - think of it as a giant read-only source file that every cell stores but only runs parts of.
The core vocabulary
| DNA | The instruction set, written in bases A/C/G/T. Like a program's source code - stored in every cell. |
| Base / nucleotide | A single letter (A, C, G, or T). The individual characters of the code. |
| Genome | The entire DNA of an organism - the whole codebase. The human genome is ~3 billion bases. |
| Chromosome | The genome split into big chunks for storage. Like separate files in a repo; humans have 23 pairs. |
| Gene | A meaningful segment of DNA with a specific job - most like a function in your codebase. Humans have ~20,000. |
| Protein | What a gene actually produces when "called" - the working machinery that does jobs in the cell. The function's output, and the thing that does real work. |
| RNA | A temporary working copy of a gene, made on demand. Like loading a function into memory to run it. |
| Mutation | A change in the DNA letters - a typo in the source. Some are harmless; some break things (cause disease). |
The "central dogma" - biology's main function call
There's one process so fundamental it has a grand name: the central dogma. It's just the pipeline by which a gene becomes a working protein:
In programmer terms: transcription is copying a function's code out of the source file into memory (DNA → RNA), and translation is executing it to produce a result (RNA → protein). When biologists say a gene is "expressed," they mean it's currently being run - actively copied into RNA and made into protein.
Why "gene expression" is the heart of so much bioinformatics
Every cell has the same DNA, but a brain cell and a muscle cell are wildly different - because they run different genes. Measuring which genes are expressed, and how much, tells you what a cell is doing. That measurement (RNA-seq) is one of the most common analyses in the field, and it's the flagship project on this site.
A few more terms you'll meet constantly
| Sequencing | Reading the order of bases in DNA or RNA - turning a physical molecule into text you can analyze. The machines output millions of short "reads." |
| Read | One short stretch of sequence from the machine (e.g. 150 letters). You reassemble or map these to study the whole. |
| Reference genome | A standard, agreed-upon copy of a species' genome you compare your data against - like a baseline branch you diff against. |
| Expression level | How much RNA a gene is producing - i.e., how "active" it is right now. The main quantity in transcriptomics. |
That's genuinely most of it
DNA is code, genes are functions, proteins are the output they produce, and "expression" is which functions are running. Sequencing turns molecules into text, and bioinformatics analyzes that text. With just this mental model, the rest of the roadmap will make sense as you go - and we'll define anything new the moment it appears. Next, the mirror image: the computing concepts a biologist needs.