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Homepage / 10 skills every bioinformatician should master

10 skills every bioinformatician should master


Ewa Marek Applied Bioinformatics MSc

1. Programming – the informatics part

As a proper bioinformatician you should be skilled at least at one programming language like R, Python, Perl, Java or C. Better if you know more than one of them as some things are easier to do in R whereas other will work much faster when implemented in Java or C. If you’re looking into pursuing a bioinformatics degree, you need to find a course that covers at least one scripting and one object oriented programming language. With such skills you’ll be able to create stand-alone software, tools to visualize large datasets, pipelines (which are commonly used in biological data analysis) that are efficient, reliable and easy to modify. This will also expand the skills-section in your CV, which definitely will increase your chances of finding a dream job. Anyway, programming is a fundamental skill for every bioinformatician, so ensure that you’re familiar with loops, if statements and functions before going any further.

2. Molecular biology knowledge – the bio part         

The basic understanding of molecular biology and processes that occurs in cells is key for a bioinformatician. You need to have a good grasp of biology knowledge on genomic, proteomic and metabolomics level. For example, advanced sequencing techniques are becoming quite popular, so there are a plethora of applications, including whole genome sequencing, RNA-Seq or Chip-Seq. It is impossible to efficiently analyse data from these experiments without knowing anything about such processes like transcription, translation and factors that affect them.

As soon as you begin your job or PhD hunting, you will see that the ability of analysing data from Next Generation Sequencing (NGS) data is one of the most common requirements (if you don’t believe me, just type “bioinformatics” in LinkedIn jobs search and have a look at what comes back). Although there are many online courses and YouTube videos which can cover this, such courses only offer a series of introductory sessions to such techniques and might not be sufficient to perform your own large-scale analysis. Thus, it seems reasonable to acquire such skills through formal training and/or credited courses. So if you’re looking for an MSc course for example, you need to make sure that this course actually provides hands-on step-by-step training using “real” NGS data (not just lectures and show cases of the analysis output). Additionally, before joining a course, you need to ask about the hardware infrastructure used during the training (I’d say a dedicated High performance computing facility – HPC is a must).

Trust me, when you do your first ever NGS analysis you will really appreciate the advantage of using a powerful super-computer; it’s quite common to be waiting for an hour or so for your results, only to realize that you made a mistake in one of the previous step! I did my first analysis using Cranfield University’s HPC facility, which allowed me to get results back quickly and learn from my own mistakes (seems as the most efficient way to learn anything though someone malicious may say it’s better to learn on other people’s mistakes!)

To sum up, the biology knowledge is one of the things that distinguishes a bioinformatician from a typical IT person, so if you don’t get the difference between gene and transcript you really need to study more of the foundation of molecular biology before calling yourself a bioinformatician!

3. Statistics – what is the data whispering about?

Ok, so the experiment is done and you have your data. A lot of numbers, what to do with them? It could be hard to draw any meaningful conclusions from them without using some statistics. And no, I don’t mean using some fancy formulas just to impress your boss and colleagues. It’s more about the way of thinking – at least equations could be found quite easily in the internet. They are only tools, it’s you who should know when to use them. You won’t try to mend your clothes with a hammer, will you? So don’t use the t-test when the sample distribution is not normal! As a bioinformatician, you need to make yourself quite familiar with machine learning and pattern recognition algorithms… in fact you can call yourself a biology data scientist!

4. Database skills – keep your data tidy

As a lot of data is produced it must be stored somewhere. This also applies to biological data, which are included in the definition of a popular nowadays term big data. You will often need to connect to databases (or even design your own database) in your programs, which means that some knowledge about SQL query statements could be useful. Extra points for knowing what NoSQL is. I’m pretty sure that your future employer will not complain if you are able to create and manage a database as well. And of course it’s a shame for a bioinformatician to not know what NCBI is.

5. Algorithms – what turns bioinformatician into magician

Imagine that you would like to prepare a tea. At first you put a bag of tea in a cup, then you boil water and pour it into the cup. After few minutes you remove a teabag from the cup and the tea is ready. What if you would like to prepare a coffee? Would you be successful following the same steps as you were preparing tea? Not really, coffee usually isn’t in a bag and there wasn’t a step of adding milk (every proper coffee is served with milk). So you have a sequence of instructions (algorithm) that allows you to prepare a tea and you can modify it to prepare a coffee. That’s why bioinformaticians should learn and understand algorithms – to be able to modify them for their own needs. Moreover, developing your own algorithms is the last level of initiation in bioinformatics career.

6. Linux – bioinformatician’s secret

I can program in two programming languages, come on, isn’t it enough? It could be but if you’re interested in analysing data from next generation sequencing, basic knowledge of Unix commands will make you a powerful bioinformatician. Furthermore, as you will be analysing a lot of data, you could get from your employer an access to external servers, which with high probability will work under this operating system.

7. Teamwork makes the dream work

Any bigger idea or a project cannot be accomplished without cooperation of at least few people. Be aware of the fact that whether you want or not you will be a part of a team and prepare yourself. What will make you a good team-worker? Don’t be selfish, listen to others and take their point of view into consideration but also defend your statements. It seems that good communication and the ability to compromise are the keys. That’s easier said than done, so practice whenever you have an occasion – involve yourself in activities where you’ll need to work as a part of a group for example enroll yourself in a student club. It’s also a good idea to pay an attention if the MSc course you are going to apply for will have a group work component. Working on a group project during your course is a double win – you learn things within the area of your interest and practice soft skills at the same time!

8. Time-management – don’t let your ideas die

Good time-management skills are vital for bioinformaticians as they often have to organize their work on their own. Have you got one month for doing a project? That’s great, don’t leave it for the last moment. It seems that at the beginning of each project people usually are excited and come up with many great ideas but then a problem appears with their realization. Manage your time, plan smartly and be careful to not fall in a trap of taking too much on your shoulders. It’s really easy to involve yourself into many projects, it’s harder to make all of them done (trust me). And at the end do not forget about yourself. Put in your plan time for some entertainment, because an unhappy bioinformatician is a bad bioinformatician.

9. Self-study – never ending story

The world is constantly developing, a bioinformatician should as well. You should be aware of the fact that molecular techniques are extremely dynamic, and almost every six months, a new sequencing platform becomes available. So, it’s impossible to learn everything you will need in your future job at the university. And that’s great – you will never be bored! Thus, you should be able to learn on your own and be skilled in looking for worthy information. Being a bioinformatician is an adventure – you never know with what problems you will have to deal with, so improvise, adapt, overcome.

10. Patience – may the force be with you

Hands up all who managed to write a longer program that worked perfectly at the first run! No hands? Yeah, developing an efficient program requires testing and rewriting it few times. Sometimes it could be frustrating but never give up. You can also meet with a problem with online resources, which could be temporarily unavailable due to some breakdown or have a problem with your program after update of some packages that it depends on. If you ever have a situation like these, keep calm, be patient and get yourself through that crisis. At least you’re doing something you love, aren’t you?

The development of above skills is important from a bioinformatics student perspective, which I am, in order to become a successful bioinformatician. Currently, I’m doing the Applied Bioinformatics MSc course at Cranfield University, so if you have any questions about this course do not hesitate to contact me on LinkedIn.


Ewa Marek

Written By: Cranfield University

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