What this subject asks you to show
Bioinformatics and computational biology sit where biological questions meet computation, statistics and data. A statement for this area is weakest when it reads as a biology statement with a coding paragraph attached, or a computing statement with the word genome added. The useful evidence shows you using one side to think about the other: a biological question that needed data to answer, or a computational idea you understood better because you saw it applied to living systems.
The two course labels overlap heavily, but the emphasis can differ.
- Bioinformatics leans towards biological data itself: sequences, genomes, protein structures and gene expression, and the tools, databases and pipelines used to store, compare and analyse them.
- Computational biology leans towards modelling and theory: simulating populations, cell signalling, epidemics or evolution, and using mathematics and statistics to test hypotheses about how systems behave.
Check how your chosen courses describe themselves and weight your evidence accordingly. An applicant whose strongest interest is sequence alignment and one whose strongest interest is differential equation models of disease spread can both fit, but they should not write the same statement.
Interests worth writing about
Name a specific problem rather than a field. These are examples of the level of detail that works, not a list to copy.
- Sequence comparison: why aligning DNA or protein sequences is computationally difficult, and how scoring choices change what counts as similar.
- Protein structure prediction: what it means to predict shape from sequence, and what such predictions can and cannot tell you about function.
- Genomic variation and disease: how a variant is linked to a trait, and why correlation in large datasets is not the same as a demonstrated mechanism.
- Phylogenetics: building evolutionary trees from molecular data, and how assumptions about mutation rates affect the result.
- Epidemiological or population models: how a simple model of infection or predator–prey dynamics behaves, and where its assumptions break down.
- Microbiomes and metagenomics: identifying organisms in a mixed sample from sequence fragments.
- Gene regulation and networks: representing interactions between genes or proteins as networks and asking what that representation hides.
For whichever you choose, say what drew you to it, what you did to understand it, and one thing you now recognise as a limitation or open question. That last part is what separates engagement from interest in a headline.
Showing both halves without inflating either
Biological understanding
Use your biology or chemistry coursework concretely. Transcription and translation, enzyme specificity, inheritance, natural selection and the genetic code are all directly relevant. A sentence explaining how a topic like codon redundancy or homologous chromosomes made you think about information or data is more useful than a statement that you enjoy biology.
Computational and quantitative thinking
If you have programmed, say what you built and what was hard about it. If you have not, mathematics and statistics still count: probability, handling uncertainty, interpreting graphs, recognising when a sample is too small. Many applicants underplay statistics, yet much of this subject depends on judging whether a pattern in data is real.
Do not claim fluency you lack. Completing an introductory coding course shows you can learn syntax and solve small problems; it does not show you can write analysis software. Say what you can actually do.
Accessible preparation
None of these is required. They are options if you want something concrete to reflect on.
- Use a public biological database or tool. Look up a gene you studied in class, compare a human protein sequence with the same protein in another species, and note what the similarities and differences suggest. The value lies in what you noticed and questioned, not in having used a famous resource.
- Write a small program on biological data. Counting base frequencies, finding the reverse complement of a sequence, translating DNA into amino acids or calculating GC content are realistic beginner projects. Reflect on a bug, a design decision or an edge case, such as handling incomplete reading frames.
- Build a simple model. A spreadsheet or short script simulating population growth, drug clearance or infection spread lets you see how changing one parameter alters the outcome. Comment on what the model leaves out.
- Read with a specific aim. A popular science book or article on genomics is a starting point; explaining one method it describes, and a question it left you with, is more useful than listing titles.
- An extended project or independent investigation on a computational biology question can carry a lot of weight in a statement because it lets you describe a method, a result and its limits in your own terms.
Using experience that is not directly relevant
Most applicants have no laboratory or research placement. Ordinary experience can still connect, provided you state the specific link and do not overstate it.
- Practical science lessons: repeating measurements, spotting anomalous results and estimating error relate directly to working with noisy biological data. They do not show experience with large datasets.
- Computing or mathematics coursework: sorting algorithms, searching, recursion or statistical tests are the building blocks of sequence analysis. Link a concept you learned to a biological use, such as searching for a pattern in a long string. This shows foundations, not applied bioinformatics.
- Part-time jobs involving records or stock: noticing inconsistent data entry or reconciling records relates to data quality, a real practical concern when combining biological datasets. Keep the claim modest: it shows care with data, not scientific analysis.
- Caring responsibilities: supporting a relative through a genetic or chronic condition may be why you became interested in how disease is studied. This is legitimate motivation. It does not give you clinical or scientific expertise, and you need not share more personal detail than you are comfortable with. Move quickly from motivation to what you did to understand the science.
- Gaming, modding or hobby programming: these can show persistence with debugging and logic. Make the connection to a specific technique; game interest alone is not evidence for this subject.
- Nature recording or citizen science: logging species sightings involves standardised data collection and can connect to ecological modelling or biodiversity data. It is evidence of data collection rather than computational analysis.
What useful reflection looks like
Reflection in this subject usually means explaining a decision or a limitation. Compare these two approaches.
- Weak: I used a sequence alignment tool and found it fascinating how technology can help biology.
- Stronger: Comparing a haemoglobin sequence across species showed regions that were almost identical and others that varied widely. Our biology lessons on protein structure suggested the conserved parts might be functionally essential, but I realised that similarity alone could not show this without experimental evidence.
The stronger version names the data, links it to prior learning and recognises what the analysis cannot prove. Useful prompts include: what assumption did the method make, what would have changed the result, and what would you need to test the idea properly.
Pitfalls specific to this subject
- Treating it as a route into a single job. Studying bioinformatics is not the same as being a data scientist in a pharmaceutical company or a genetic counsellor. If you mention a career, keep it brief and tie it to the study you want to do.
- Buzzword stacking. Mentioning artificial intelligence, big data and precision medicine in one sentence shows awareness of headlines, not understanding. If you discuss machine learning, explain one concrete use and one weakness, such as bias in training data.
- Listing languages and tools. A string of programming language names is not evidence. One project explained well is worth more.
- Ignoring the biology. Courses in this area are biological sciences; show you care about the living systems behind the data.
- Ignoring statistics. Treating computational output as automatically correct is a warning sign. Show you understand that results need interpreting.
- Drifting into a neighbouring subject. If your statement is mostly about laboratory genetics, cell biology or ecology fieldwork with no computational thread, consider whether a course in genetics, biochemistry or ecology fits you better, or rebalance your evidence.
Postgraduate applicants
If you are applying for a master’s, the balance shifts to what you have already done. Describe a dissertation, analysis or project with enough technical detail to show your role: the data, the methods, the decisions you made and what you would do differently. If you come from pure biology, show the computational and statistical study you have done to prepare; if you come from computer science or mathematics, show genuine engagement with biological questions rather than treating biology as a source of datasets. Be precise about collaborative work and credit others where the work was shared.
For general advice on planning, structure and editing, read our personal statement writing guide.
Bioinformatics and computational biology personal statement examples