What is Bioinformatics? A Complete Beginner’s Guide to Bioinformatics and Computational Biology

Chapter 1 of 5: What is bioinformatics and computational biology?

Biology today produces more data than any human can read. One run of a modern DNA sequencer can create hundreds of gigabytes of text made of four letters: A, T, G and C. Hospitals, farms, drug companies and research labs all hold this kind of data. Someone has to turn it into answers. That someone is a bioinformatician.

This what is bioinformatics guide is the starting point of the Bio Help Learning tutorial series. It is written for absolute beginners: biology students who have never written code, and computer students who have never studied DNA. You will learn what bioinformatics and computational biology are, where they are used, where the field is going, what jobs and salaries look like in different countries, how long it takes to learn, and how this free tutorial series will take you from zero to your first real analysis.

What you will learn in this tutorial

• What bioinformatics and computational biology mean, and how they differ.
• Why the field exists: the data explosion and the falling cost of DNA sequencing.
• Where bioinformatics is used: medicine, agriculture, drug discovery, microbiology, forensics and more.
• The future of bioinformatics: AI models, long-read sequencing, single-cell and spatial data.
• Job roles, indicative salaries in India, the USA, the UK, Germany and Canada, and the companies that hire.
• A realistic month-by-month roadmap to become a bioinformatician from scratch.
• How the Bio Help Learning tutorial series builds your skills, and how we keep it up to date.
• The story of how Bio Help Learning started, and why learning here stays free.

What is bioinformatics?

Bioinformatics is the use of computers, software and statistics to store, organise and analyse biological data. The data can be DNA sequences, RNA expression values, protein structures, microscope images or patient records. The goal is always the same: to answer a biological question.

Think of a library with three billion letters and no index. The human genome is roughly that size. A biologist can read a few genes by hand. A bioinformatician writes the index, the search tool and the reading machine, so that millions of letters can be compared in seconds.

In daily work, a bioinformatician does things like:

• checking the quality of sequencing data (our FastQC tutorial teaches this);
• aligning millions of short DNA reads to a reference genome;
• finding mutations (variants) that may cause a disease;
• measuring which genes are switched on or off in a tumour (RNA-seq);
• identifying bacteria in a soil or gut sample (metagenomics);
• building automated pipelines so the same analysis runs the same way every time.

What is computational biology?

Computational biology uses mathematics, models and simulations to understand how living systems work. Instead of only analysing data that already exists, a computational biologist builds a model of a process, such as how a protein folds, how a virus spreads, or how a gene network switches a cell from healthy to cancerous, and then tests the model against real data.

A simple way to remember it: bioinformatics builds and uses the tools; computational biology uses tools and models to answer “how” and “why” questions. In real jobs the two overlap so much that many job posts list both names.

Why bioinformatics matters: the data explosion

The first human genome took 13 years (1990–2003) and billions of dollars of total project cost. Today a human genome can be sequenced in a day or two. The US National Human Genome Research Institute (NHGRI) tracked the cost: about US$95 million per genome in 2001 and about US$500 by 2022, a drop of roughly 190,000 times.

When data becomes cheap, analysis becomes the bottleneck. A lab can sequence 100 samples in a week, but it needs people who can check, clean, align, compare and interpret those samples. This is why bioinformatics skills are in demand in research, hospitals and industry.

The data is also growing in India. The Genome India Project, funded by the Department of Biotechnology, sequenced the whole genomes of 10,000 Indians from 83 population groups. The data was released in January 2025 through the Indian Biological Data Centre (IBDC) in Faridabad for approved researchers. Projects like this create work for Indian bioinformaticians for years.

Your first taste: a tiny bioinformatics task

Here is the kind of thing bioinformaticians do every day, shrunk to one line of DNA. The GC content of a sequence is the percentage of letters that are G or C. It matters because GC-rich DNA is more stable, sequences differently on some machines, and varies between species.

The formula is simple:

GC% = (number of G + number of C) / (total number of bases) × 100

For the sequence ATGCGTACGTTAGCGC there are 16 bases. Count the G and C letters: there are 9. So GC% = 9 / 16 × 100 = 56.25%.

Now the same task in Python. You don’t need to understand every word yet; the Python tutorials in this series will cover it.

seq = "ATGCGTACGTTAGCGC"
gc = seq.count("G") + seq.count("C")
print("Length:", len(seq))
print("GC count:", gc)
print("GC %:", round(100 * gc / len(seq), 2))

And the same count in one line on the Linux command line, which our Linux tutorial teaches step by step:

echo "ATGCGTACGTTAGCGC" | grep -o "[GC]" | wc -l

This is the whole spirit of bioinformatics: a biology question (how GC-rich is this DNA?), a precise definition, and a few lines of code that work equally well on 16 letters or 3 billion.

# Pseudo-code: GC content of a very large FASTA file
gc = 0 ; total = 0
for each line in file:
    if line starts with ">": skip        # header line
    for each base in line:
        if base in {G, C}: gc = gc + 1
        if base in {A, C, G, T}: total = total + 1   # ignore N
report 100 * gc / total