What is Bioinformatics

I was looking up this question because although I am interested in becoming a bioinformatician I had not attended a bioinformatics class while in school, or was taught what bioinformatics was. The most I had heard about bioinformatics was that it analyzed genetic information, specifically DNA. That didn’t satisfy me though, because I knew that biological data could be recovered in a plethora of ways that were not limited to genetic data and a lot of that data could not be analyzed by hand. The sheer amount of data that can now be recovered is massive and would take far too long to analyze by hand. I knew that bioinformaticians help analyze biological data by creating software applications to automate the analysis work, but I did not believe bioinformaticians would be limited to only analyzing genetic data. If they were indeed limited to analyzing genetic data why weren’t they called genetiformatics or something similar?

That is when I found this article published in PLOS Biology entitled Is “bioinformatics” dead? written by Philip E. Bourne (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7984605/#:~:text=Why%20would%20a%20computational%20biologist,field%20is%20very%20much%20alive). Initially, I was not persuaded to read this article because of the title. Bioinformatics being dead would be terrible for me as I am still trying to get into the field. I want to find a career where I can use computer programming to help biological research and I had thought bioinformatics was the solution. Luckily, I didn’t let that fear deter me, rather I let the curiosity about the contents of the article drive my decision to read it. It discussed the current understanding of bioinformatics as well as a desire to change the title of the field.

The article began by briefly discussing the human genome project which was declared complete in 2003. The human genome project was a massive research project adopted by the US government with the goal of sequencing the entire human genome, over 3 billion base pairs long. It explained that this project allowed the field of bioinformatics to gain the limelight. Bioinformatics was the synergistic combination of experimentation and computation. It allowed not only the ability to generate and maintain the volume of data needed to store the human genome but also to create software applications to assemble and make sense of the data.

Bioinformatics has emerged as a new discipline. Although it began in the 1960s, by the early 2000s it had taken the scientific world by storm. But just as the technology enabling the greater application of bioinformatics advanced, so did the data that biologists generated and stored. After the human genome project, scientists began collecting data on not only genetic information, but proteins, metabolites, or any other constituents of the cell. This led to the creation of branches of science known informally as omics: proteomics, genomics, metabolomics, and transcriptomics for example. Now there was so much different data out there that needed to be analyzed and understood. Bioinformatics had evolved, now it was not only needed for genetic data but to analyze any large biological datasets.

Finally, I found an article that agreed with what I had thought to be the case. Bioinformatics was not limited to genetic data. But the article continues from here. It discusses the fact that modern bioinformaticians need to expand their understanding past solely being data scientists toward an understanding of the data they analyze. Being able to communicate with all the stakeholders involved in the research. Bioinformaticians need to become more than what they were in the early 2000s, they need to become biomedical data scientists.

This means that the newly dubbed biomedical data scientists, formerly bioinformaticians, need to remove themselves from their academic silos. They need to not only focus on the computer science, and statistics knowledge they use on a daily basis but to expand to other related fields. Biomedical data scientists need to understand the biology related to the data they analyze, understand how to use artificial intelligence to help with the analysis and communicate their findings to scientists in diverse biological disciplines. Essentially a biomedical data scientist needs to be a data scientist, a machine learning engineer, a biologist, and a communications specialist. They must be interdisciplinary. This is the point of the article, that the education of bioinformatics must change.

I liked this idea. A bioinformatician or rather a biomedical data scientist must be interdisciplinary, mostly because my education has become interdisciplinary. Initially, I was a biochemist, then I became a molecular biologist, a neuroscientist, a virologist, an immunologist, and finally a data scientist and software engineer. These last two disciplines I did not go to school to earn but taught myself. Because I am interested in so many different disciplines, I call myself a scientist. The fact that the career I want to call my own is becoming interdisciplinary is inspiring.

I now want to expand on the challenges I have been facing trying to get into this field, but I have taken up enough of your time. I hope you have learned something and been entertained. Maybe inspired? Either way, I thank you for taking the time to read my latest blog post. I have not posted in a while and the reason for that is too complicated to expand upon now. Leave it to say that I plan to post more consistently. If you want to be contacted when my next article goes up you should subscribe :). If you enjoyed this article, please share it with someone else.

Have a fantastic day!