Bioinformatics can be a bit of a mysterious field. I’ve collected a number of questions I’ve either been asked in real life about the area, or questions I see coming up a lot on the bioinformatics subreddit. This is my attempt to unshroud some of the mystery for those that aren’t bioinformaticians, but are considering getting into the discipline. These answers are my opinions only, and should certainly be taken with a grain of salt.

What the heck is “bioinformatics”?

Whenever I mention that I’m in bioinformatics, I either get a nod of recognition or a look of utter puzzlement, at the sheer number of syllables I’ve thrown their way. Some think I’m involved in engineering bio-mechanical devices (which is the completely different field of biomedical engineering), or that I am splicing genes to create some kind of mutant super-army. (This is actually my completely unrelated part-time hobby, but I digress…) I can say that it is an area of study incorporating the intersection between molecular biology, computer science and mathematics/statistics. It’s everything from the human genome project to studying how cancer evolves and mutates. It can be a difficult field to pin down with an exact definition, so I will link to some cool projects that are heavy on bioinformatics, to give you more of an idea:

ENCODE – how bioinformatics is helping us to understand the features of the DNA landscape.

Connectome project – how bioinformatics will transform our knowledge of the brain.

Discovering our inner neanderthal – what bioinformatics can tell us about our ancestry.

What’s it like being a bioinformatician?

This depends on what kind of bioinformatician you are. I like to think that there’s two main types of bioinformatician. The first are those who are concerned mostly with the biological questions, and computers + stats are just their tool of choice. They might be a wet-lab biologist who has picked up enough R, python or perl to be dangerous, or they’ve started out in the dry lab but are interested in solving chiefly biological problems. Such bioinformaticians may spend time generating data in the wet lab for their computational analyses, or they may never set foot in a wet lab at all. They will generally use existing methods and techniques to analyse their data when they’re in the dry lab. Generally, their focus is on the bio of bioinformatics.

The other category are bioinformaticians who work on developing new statistical or computational methods. This might be because they are attempting to answer a biological question and no appropriate method exists, or they are trying to fix artifacts or noisy signals that creep into the data. Many of these kinds of bioinformaticians will come from the more quantitative sciences – statistics or computer science for instance. They will generally spend more time writing code and experimenting with computational/statistical techniques to develop new models or approaches. Their focus tends to be more on the informatics part of bioinformatics.

You can see that it’s hard to pin down the typical bioinformatician! They could be a pure biologist who dabbles in the informatics side, or a theoretical statistician who’s work may have some biological application, or anywhere in between. Day to day, a typical bioinformatician may be developing software, parsing data (I think most would agree that we collectively spend too much time on this), reading scientific papers, scribbling statistical models on a white-board or stepping into the wet lab to prepare some biological data for sequencing.

How do I get into the field?

There are many answers to this question. Personally, I studied a Masters degree specifically in bioinformatics, after getting a computer science degree and working in IT. But you don’t have to study bioinformatics specifically; plenty of people get hired in post-doc positions as bioinformaticians who come from backgrounds as diverse as astrophysics or physics, pure mathematics, computer science or theoretical statistics – essentially if you can wrangle data, write code and think scientifically, you can get into the field.

It is actually the atypical case to come out of a structured bioinformatics-specific program, at least currently, although it is changing as new courses are being offered. If you study computer science, statistics or biology at a research-intensive level, such as honours or masters, it would be a good idea to look for a project that incorporates bioinformatics. Inevitably, this will introduce you to gaps in your knowledge, which you can fill from either self-directed units or by taking electives (if possible).

In general, research experience is the most valuable asset for getting into the field – especially research experience in quantitative projects. This usually means having an honours or masters degree. Only labs or companies working more in software-development or data mining, may hire coders straight from their undergrad studies or industry – generally there are few positions available to someone with solely a bachelors degree in bioinformatics. To get into academia, you will want an honours, masters or PhD degree that is bioinformatics related.

What’s the pay like?

This depends highly on where you live. In Australia, salaries for working bioinformaticians tend to be quite high. Lab-assistant roles may pay $50,000-80,000+ AUD depending on your lab and experience level. To progress further, a PhD is generally required – these are only paid if you are lucky enough to receive a scholarship, which pay roughly $25,000 p/a and are highly competitive to obtain. If you manage to get to the post-doctoral level, you could be earning a cool ~$78,000 AUD p/a.

Salaries tend to be be much lower in other parts of the world (especially for post-docs), so I would encourage you to research is thoroughly if it is a deciding factor. That said, bioinformatics is not something you do for the money. There are plenty of software-engineering or data-wrangling type positions that pay far more and have more job security. In bioinformatics, and academia in general for that matter, contracts for more than 1-2 years tend to be rare. Tenure-track or lab head positions are even rarer and have tons of competition, usually requiring an A-star publication record (think first author Nature and Cell papers). If you’re feeling brave, you could even calculate your odds of becoming a principal investigator.

Where should I look for a bioinformatics job?

It’s worth noting that most bioinformatics jobs won’t be advertised on your job search engine of choice. It’s currently a small field with a tight community (but growing fast), so your best bet is to get involved with your regional bioinformatics student group, or start going to conferences and engage with the community in your area.

What are the best things to learn if I want to get into the field?

This really depends on what specialty you want to be in or role that you want to work in. Learning these basics will never be a waste of time:

basic statistics, probability theory, set theory – get as solid grounding in these as possible!

basic molecular biology – get yourself a copy of Molecular Biology of the Cell.

R programming, and/or a scripting language – python is popular and easy to learn, Perl is another option but is less human-friendly.

UNIX/basic shell scripting – this becomes invaluable to quickly manipulate files and pipe programs.

These are the basic building blocks but only the first steps in a massive field. To be in bioinformatics, you have to constantly learn as the field moves rapidly.

As you get more experience, it’s worth thinking about your particular specialty that you bring to the field – are you good at working with algorithms? modelling complex biological systems? having an encyclopedic knowledge of the literature? Identify an area and build your skills – it will make you stand out. Also realise that you can’t know everything – true experts in the biological AND computational AND statistical ends of the spectrum are exceedingly rare – and it’s always better to know one area in depth, rather than several areas shallowly.

Should I get a PhD?

That’s a question only you can answer. If you desire to lead projects it would be difficult to progress far without a PhD in an academic or research-based setting. Keep in mind that as a post-doc, you will also have to spend a large amount of time hunting around for money to fund your projects – this means writing many-a-grant application – which may not be everyone’s cup of tea. If you are chiefly interested in the coding side, a PhD is not required for research assistant (RA) type positions, and a masters or honours will generally suffice. Being an RA usually involves more grunt work and having tasks delegated to you, but this might not be the case depending on the lab you work for. PhDs and higher-level positions tend to involve more freedom and ability to choose one’s direction, but are potentially more stressful and pay less. Some people also can’t stand reading papers or writing them – which will be a significant hurdle to overcome if you want to do a PhD. Salary-wise, a PhD likely won’t increase your pay-grade enough to counter-balance the lost income from years as a low-wage PhD candidate, so it’s likely not a choice one should make for monetary reasons.

Are there positions in industry?

It depends where you live. In Australia, there are few such positions, but they do exist (IBM for instance has a life sciences division). Once companies figure out business models that make bioinformatics viable from a profitability perspective, there’s a good chance many more bioinformatics start ups will emerge. Indeed, there are promising signs that genomics analysis is hitting the mainstream. Currently though, unless you are in the US or perhaps parts of Europe, bioinformatics jobs in industry are scarce. Over the next few years however, we will see new companies, maybe even new sectors created, which may employ people with job titles at the intersection of the life and quantitative sciences that don’t even exist yet.

Is bioinformatics a viable career path?

That depends on your perspective. Within academia, it can be a rewarding and viable career path; yet evidently, academia hasn’t done enough to create clear paths for early-career bioinformaticians. Sure, academia has problems, but in terms of the intellectual challenge, interesting projects and work that can ultimately make a difference, there’s few fields that can make such claims. As an extra perk, because the area is so inter-disciplinary, the skills you learn are largely transferable to data analysis/big data science and software-engineering/programming roles.

What is the future of bioinformatics?

If only I knew. Future predictions about the field are generally lofty and probably inaccurate. As mere mortals, we are generally really bad at predicting the future. In the near future however, I don’t see bioinformatics going away any time soon. I see it growing and integrating itself more strongly in areas outside of research, such as hospitals and general practice, and services direct to the public (think 23 & Me). The main short-term obstacles right now largely depend on political climate and government funding, which is eternally cyclical.