#DNA Day 2023: A Young Bioinformatician’s Reflection
On January 16th, 2023, I got a very excited newsletter from the National Human Genome Research Institute (NHGRI) about the 20th anniversary of the human genome project completion and the 70th anniversary of the DNA structure discovery to be celebrated on DNA Day, April 25th.
My first thought was, “I have to meet whoever is behind the content creation of this team” I mean, how do they come up with phrases like, “You bet your base pairs we are”? Get it?
My next thought was a “20th and 70th anniversary is a huge deal for many scientists, myself included.” As someone who draws on trending industry events as a writing prompt and inspiration, I decided I would write (and publish) a detailed article or story explaining the HGP on that day and relate it to something close to home as I did for CRISPR’s 10th anniversary with a sickle cell narrative in Africa.
However, as the date drew near and NHGRI’s LinkedIn posts kept pushing that reminder in my head, I chose to change my course of action. I would still write and publish (you’re reading it), but most importantly, I’d enjoy it. I’d talk about what those achievements in 1953 and 2003 meant to me as a young biomedical scientist building a career in bioinformatics and genomics. I’d write about how it opened a career path that makes me feel like I’m right where I need to be. So, here goes…
The Inspiration Behind the HGP and the Journey So Far
Did you know that cancer is why scientists sought to determine the order of the genome’s building block? I didn’t until I started writing this. Here’s an opinion piece about the HGP on Biomed Central if you’re interested. The quest to understand why cancer behaves the way it does was the driving force for the HGP. Here’s an industry secret: many scientists were against this initiative for many reasons. Today, like Francis Collins of the NHGRI said, I don’t think any young biological researcher can imagine a world of biological research without genomic data. I can’t.
This project is the reason biological research blends well with any other field. You name it, and I’ll tell you where biology fits in. Researchers have used statistics, mathematics, physics, and engineering to approach one or more biological and medical questions from different perspectives. Of all the combinations, bioinformatics is my favourite. It combines maths, statistics, and computer science with biology to explore relationships between diseased and healthy individuals at the genetic, proteomic, and metabolic levels.
The HGP also led to other projects like the human proteomic project, hapmap discovery for genetic variant documentation, 1000 genomes, functional elements, and gene enhancers documentation. The more initiatives it influences, the larger and more complex the data we generate.
How I Use the Discovery of the DNA Structure and HGP as a Bioinformatics Scientist
I fell in love with the DNA and genomic data in 2019. During my first internship, I had the opportunity to participate in the 2019 Introduction to Bioinformatics organised by H3ABioNet, an African bioinformatics network specifically created to develop bioinformatics capacity in Africa. Learning that people built their careers by answering biological questions using this available data and certain technical skills was fascinating.
The more I learnt about it, the more I realised that there was so much more to cover regarding understanding the human genome, especially in medicine. As we actively explored the data and information we have, we developed new tools and uncovered more questions, paving the way for novel research and discoveries. It’s like going down Alice’s rabbit hole, but you know that whatever you find has the potential to revolutionise the way we offer disease prognosis, diagnosis, and treatment.
The excitement from what I learnt drove me towards upskilling and taking courses on Python data analysis, R programming for biologists, and Bash for analysis. Like every normal biologist, I had an initial fear of the command line, but it’s been 4 years since and I’m proud to say troubleshooting is my favourite part of coding right now. Yes, it gets frustrating, but I never get over that feeling of triumph when the code finally does what you want.
As a PhD student and researcher, I will use human genome data for methylation and mutation analysis, transcriptomics (RNA sequence analysis), and genome assembly. Methylation and mutation analysis is a branch of genomics we call epigenomics — studying the chemical or environmental changes in the genome and their effects on gene expression. Transcriptomics directly explores gene expression, and genome assembly, in this case, intends to use outputs from high-tech sequencing machines (long reads) to reconstruct the human genome.
For instance, if a gene sequence in the genome has an additional or excess methyl group (organic chemistry, remember?), would it still behave normally or abnormally? Does it still form the protein it’s expected to create? Or will it be the foundation for something cancerous? If it’s cancerous, to what degree or on what body tissue would it manifest? The questions I’ll be answering will be specific to a particular disease, but this is a sneak peek into what goes on in the mind of a medical bioinformatician. I know the name keeps changing, but you can’t blame me cos I’m still trying to figure out what title to use.
All of these will be possible, thanks to the multitude of databases that exist for genetic elements, open-source bioinformatics packages on different GitHub profiles to manipulate said elements, and the knowledge of statistics and machine learning for exploration and interpretation.
Okay. That’s the last one 😁
In conclusion, the biggest achievement that came from the HGP is the unrestricted integration of computational power into medicine. It is simply incredible.
