As a wet-lab scientist, generating vast amounts of experimental data is a fundamental part of your work. Yet, the reliance on bioinformaticians and data specialists for crucial analysis often introduces its own set of challenges. While collaboration with these experts can be valuable, it frequently leads to delays and coordination issues. There are times when you might find yourself with ample data but no expert available for prompt analysis. Moreover, a lack of understanding of the analysis process can lead to misinterpretations when deriving conclusions from the results.

This article is dedicated to researchers who aspire to more than just a temporary solution for data analysis. It's for those who are committed to developing a deeper understanding and proficiency in bioinformatics. Now, to be clear, this isn't a guide on how to break into the bioinformatics profession, although following these steps could certainly help with that. Essentially, it’s about how traditional wet-lab scientists can become self-sufficient in the data analysis aspects of their work. 

The good news is you likely don't need an entire degree to become competent in the key areas of bioinformatics that matter most for your work. In the spirit of Pareto, let's focus on the 20% of actions that can give you 80% of the skills and knowledge you need. What are the most effective and efficient ways to get a solid handle on bioinformatics? 

I've outlined a process based on what worked for me (originally a molecular biologist who started out in bioinformatics through self-learning) and what I've seen work for others. The journey isn't easy, but it's definitely achievable for anyone who's willing to try and persist through tough times.

Learn to code

The first step in your bioinformatics journey is learning to code. This doesn't mean becoming an expert programmer; rather, it's about grasping the basics of a programming language and learning to manipulate data. There are so many programming concepts out there, most of which won't be relevant. Your aim here is to get comfortable with coding fundamentals and interacting effectively with data. 

What programming language should you learn?

When choosing what language to learn there are a few things you should consider:

Common languages in bioinformatics: Python and R are widely used in bioinformatics. They have large communities and their syntax is somewhat similar to the English language, making them relatively easier to learn. They are also great for data manipulation. 

Collaboration and community: Consider the programming languages used by your peers and collaborators. For instance, if your lab has an extensive code base in Perl, it might be more practical to stick with that. This ensures consistency and makes it easier to get help when needed. 

A Suggested Roadmap

A simple roadmap I would suggest is to start by learning your chosen language through courses available on YouTube or an online learning platform. Once you grasp the basics, apply your knowledge by working on some hobby projects. This obviously depends on the time you have available, but working on hobby projects is a fun step that helps in solidifying your understanding. These projects don't necessarily have to be related to bioinformatics; they can be anything that interests you. During this initial learning phase, make sure you are also covering subjects such as reading data from files and data manipulation, as these are very common tasks in bioinformatics. 

Dive into your bioinformatics analysis

Feeling a bit lost with coding initially is normal. The best way to improve is to dive in and learn as you go. This part of the journey will greatly vary depending on the analysis you need to perform, but here are three general steps you can follow:

Research your analysis: This involves reading relevant papers, exploring the methods sections of published studies, consulting knowledgeable colleagues, participating in forums like BioStars, and conducting other general research.

Find an existing workflow: Check with your colleagues or collaborators to see if they have preferred methods for analysis. If not, search online for workflows. For common bioinformatics analyses, you will often find well-documented code for a given analysis that you can follow. 

Work through the analysis: Do your best to work through the analysis, applying it to your own data. Again, use the internet to ask questions and get help when you need it.

Example

Let's say you've learned R and you want to analyze your own single-cell RNAseq data. The first step would involve researching scRNAseq analysis in R. You'll find numerous papers and online discussions about the topic. You'll likely read about Seurat, an R package for scRNAseq analysis. 

The second step involves finding an existing workflow to follow. For common analyses like scRNAseq, this isn't difficult. A simple search for ‘scRNAseq analysis in R’ will yield many results with detailed steps on how to perform the analysis, for example singlecellcourse.org.

You will want to ensure the workflow comes from a reputable and trusted source. You can then follow the workflow using your own data. I recommend writing notes in the comments of your code to remind yourself of what you are doing at each step. This may take longer, but it's really helpful for understanding what you are doing and why. After you have gone through this process a few times, you will become familiar with common steps and standard practices in such analyses.

You are not an imposter

Venturing into the field of bioinformatics can often bring about feelings of imposter syndrome. This is a normal experience, especially given the field's complexity and breadth. The best approach is to persist, embracing a student mindset and progressing step by step. This self-doubt gradually diminishes with time. Focus on continuously learning and applying your knowledge. You’ll find that with practice, you can iterate through these steps more quickly. Also, engaging in conversations with peers who are navigating the same path can be incredibly supportive. 

In my experience, motivation grows as you start seeing results. Overcoming initial hurdles and acknowledging your early successes will boost your confidence and inspire you to carry on your journey in bioinformatics. Best of luck on your bioinformatics adventure!

Images by Oggy Informatics