Bioinformatics is just an umbrella name for all disciplines that use computational tools and methods to analyze biological data.
Its areas of applications span: genomics, drug discovery, transcriptomics, epigenomics, metagenomics amongst many others.
Bioinformatics is divided into 3 central themes:
- Data science
As a life scientist, the biology part is pretty much solved. A thorough review of any existing paper will get you started on even the most complex subject. You will need to learn the computing and data science part.
For starters, computing means programming aka coding. Computing in bioinformatics involves learning a scripting language (BASH) and a programming language (Python or R).
Why do you need these two?
Different bioinformaticians code with different programming languages. To make it usable and understandable to everyone, an interpreter is needed to translate the codes.
This’s where Bash comes in, this interpreter translates the codes in the language to commands. With commands, it becomes easy to use software the way you like
After using the software from the bioinformatician, you will most likely get raw results that need further processing. Basically, you still need to extract more information from the raw results, this is where the programming language and data science comes in.
With a programming language, you can write special functions to perform special calculations.
These days, programming libraries have libraries that contain some set of pre-written functions to solve related tasks, so you don’t have to write them again.
At the intersection of data science and computing, is statistics and data visualization.
Statistics is the scarecrow of most wet lab life scientists. Irrespective of your fears, stats give meaning to your data.
Statistics helps with experimental design, data processing, estimating significance, filtering, modeling and integration with future data.
On data visualization
Visualizing your results is as important as interpreting your results. This is why most of the top journals lay strong emphasis on graphs and illustrations.
With good visualization, you can easily communicate the relevance of your results.
List of skills needed as a bioinformatician
Therefore, to get started in the field of bioinformatics from a life science background, you need to learn the following skills:
- Programming skills: R & python
- Statistical skills
- Visualization skills
- General biology, genomics and genetics knowledge
- Knowledge of database
There are so many resources online where you can learn these skills but it can somehow be confusing. You can check some courses on Coursera or YouTube.
But if you want a structured and straight to the point course, a good way to start is this really good workshop by HackBio.
Here, you will learn premium bioinformatics skills, network with people from different parts of the world, access mentors and do a capstone project.