For the Genomic Data Science beginners only.

If you love Biology and Data Science, you’ll love Bioinformatics.

Last updated on 15 Sep 2020.

I want to know what are the opportunities in Genomic Data Science. So I begin with a few questions. Here are my top five (4W1H).

1. What is Genomic Data Science (a.k.a. Bioinformatics)? And what are the differences between Biostatistics, Computational Biology, and Precision Medicine?
2. Why am I interested in exploring Genomic Data Science (a.k.a. Bioinformatics)?
3. How do I start exploring Genomic Data Science (a.k.a. Bioinformatics) coming from a Computer Science background?
4. Where is Genomic Data Science (a.k.a. Bioinformatics) going in the future?
5. When is Genomic Data Science (a.k.a. Bioinformatics) booming and busting?

1. What is Genomic Data Science (a.k.a. Bioinformatics)? And what are the differences between Biostatistics, Computational Biology, and Precision Medicine?

Coursera explains that Genomic Data Science is the field that applies statistics and data science to the genome. And from my findings, Genomic Data Science is better known as Bioinformatics. So throughout this article, we will use Bioinformatics and Genomic Data Science interchangeably.

Next, what are the differences between Bioinformatics and Biostatistics?

A school’s take on Biostatistics and Bioinformatics. Screenshot on 5 Sep 2020.

How about the differences between Bioinformatics and Computational Biology?

A school’s take on Computational Biology and Bioinformatics. Screenshot on 5 Sep 2020. https://www.duke-nus.edu.sg/education/our-programmes/phd/qbm-phd/specialty-tracks

Bioinformatics is grouped under Computational Biology as seen above. But what are their differences?

To find out more, I researched further.

An online school’s take on Computational Biology and Bioinformatics. Screenshot taken on 5 Sep 2020. https://online.lewisu.edu/msds/resources/computational-biology-and-bioinformatics-two-fields-changing-the-world

So now we know the subtle difference as seen above. But what does the Bioinformatics community think about computational biology and bioinformatics subtle differences? I asked on Reddit forum.

A reddit user explained the differences between Bioinformatics and Computational Biology. Screenshot on 5 Sep 2020. 0https://www.reddit.com/r/bioinformatics/comments/imfgrx/how_should_i_get_started_with_computational/g403sd9

Another reddit user explaining the differences between Bioinformatics and Computational Biology. Screenshot on 5 Sep 2020. https://www.reddit.com/r/bioinformatics/comments/imfgrx/how_should_i_get_started_with_computational/g42ogmi

Lastly, what about precision medicine?

All in all, Computer Science, Statistics, Medicine, and Biology are big fields altogether. I will simply use Duke-NUS programmes to illustrate the differences using a few diagrams.

Broad overview

Precision Medicine is the healthcare outcome from Computational Biology (and Bioinformatics)

The common outcome of Bioinformatics, Biostatistics, Computational Biology, and Precision Medicine are better healthcare for all.

2. Why am I interested in exploring Genomic Data Science (a.k.a. Bioinformatics)?

My primary STEM (Science, Technology, Engineering, and Mathematics) interests are Computer Science. While my secondary STEM interest is Statistics.

• Computer Science: I have formal training from my Bachelor of Science (Information Systems Management) and Master of Computing (Computer Science) studies. For computer science, I am deeply interested in Software Engineering, Data Management & Analysis, and Solutions Architecture.
• Statistics: I have some training from my Bachelor of Science (Information Systems Management) and Master of Computing (Computer Science) studies.

Knowing Data Science intrigues me to explore further. Thus, Bioinformatics (a.k.a.) Genomic Data Science. Image taken on 15 Sep 2020. Image courtesy: https://genomejigsaw.wordpress.com/2015/09/27/faq/

Besides my STEM interest, I have an interest in Medicine.

• Medicine: I have no formal training. I read about medicine from occasional population health articles and some research papers. For medicine, I am generally interested in Neurology, Dermatology, and Oncology. Especially interested in how Genomic Data Science can be used to help deliver better personalised healthcare.

3. How do I start exploring Genomic Data Science (a.k.a. Bioinformatics) coming from a Computer Science background?

Having a computer science background is a good start. There are many posts explaining this on Reddit and also suggestions on how to get started. In essence:

• Learn Bioinformatics from Coursera
• Check out teaching videos from Ben Langmead, Aaron Quinlan or Shirley Liu to learn more about Bioinformatics.
• Pick up computational biology/bioinformatics from Rosalind

In addition:

• Brush up on Statistics from Khan Academy
• Familiar with Statistical Inference from Coursera

Misc:

• Read this personal blog for cutting edge of Bioinformatics concepts
• Check out on Bioinformatics threads (similar to Hacker News)
• Join Reddit Bioinformatics community
• See what is new at preprint server of biology

Bioinformatics is a broad field. Here is the suggestion of a Bioinformatics expert on Reddit:

How to get started with Bioinformatics. Screenshot taken on 7 Sep 2020. https://www.reddit.com/r/bioinformatics/comments/imfgrx/how_should_i_get_started_with_computational/g483e1k

4. Where is Genomic Data Science (a.k.a. Bioinformatics) going in the future?

Does Bioinformatics’ future look promising?

In 2020, a few notable business leaders noted that MedTech is the space to watch out (if I remember correctly). In particular, the CEO of Blizzard noted the genomics space.

• Leadership Live With David Rubenstein: Former Google CEO Eric Schmidt
• Leadership Live With David Rubenstein: YouTube CEO Susan Wojcicki
• Leadership Live with David Rubenstein: Activision Blizzard CEO Bobby Kotick

So does Bioinformatics future look promising? I don’t know enough to form my judgement on this matter yet!

What are some technological advances that changed Bioinformatics?

Better computing power, easier access to compute power through cloud computing, and decrease in computing cost. All these contributed to performing next-generation sequencing faster, better, and cheaper.

The top 3 cloud computing vendors have genomic service for us to use:

• Google Cloud Platform: Cloud Life Sciences (at the time of writing, it is in beta)
• Amazon Web Services: Genomics in the Cloud
• Microsoft Azure: Microsoft Genomics

They are investing resources in Bioinformatics is a green flag.

5. When is Genomic Data Science (a.k.a. Bioinformatics) booming and busting?

Only the future will know. At the time of writing, according to a reddit user, Bioinformatics had gone through boom and bust in around 12 years ago. So now the question is will it rise again and when will it fall again? I don’t know.

In brief, it is undergoing continual change due to technological advancement. So watch this space carefully. Screenshot on 5 Sep 2020. https://www.reddit.com/r/bioinformatics/comments/7nh34o/is_bioinformatics_a_bubble_or_does_the_future/ds1s820

FAQ: Should I do a PhD in Bioinformatics?

The question should be academia or industry?

According to a Bioinformatics practitioner, now a Software Engineer with Google Health, who has a PhD in Bioinformatics:

If working in industry, you do NOT need a PhD in Bioinformatics to be doing work in Computational Biology or Bioinformatics.

If working in academia, you do NOT necessarily need a PhD in Bioinformatics. But, you are likely to hit a ceiling early without a PhD. So consider getting a PhD.

In brief, if going into bioinformatics research, then get a PhD. If going into industry, then NO need to get a PhD in Bioinformatics.

FAQ: Where can I work as a Bioinformatician?

In many reddit posts, the 3 common types of employers are (a) research institute i.e. university academia, (b) hospitals i.e. industry, and (c) biotech companies i.e. industry.

FAQ: What do Bioinformaticians do daily?

See this Reddit discussion.

FAQ: Is a Bioinformatics career path suitable for me?

Generally speaking, to know if a field is suitable, you have to understand your career aspiration, aware of your personalities (e.g. Myers–Briggs, DISC), and your core competencies / competitive advantage (i.e. be an expert at my craft — good at solving problem and creating added value).

Sharing a bit of my background. At time of writing, in 2020, I am in my 30s based in Singapore. I have been in the industry for all my career. I started out with engineering software, then managing data, and now engineering, marketing, and selling a Software-as-a-Service (i.e. software dev., consultative sales, client facing, and demo). I am committed to building my business persona.

My competitive advantages are Data Management, Software Engineering, Cloud Architecture, Sales and Marketing. With my competitive advantages / core competencies, my goals are to further strengthen my competencies. I do NOT want to shift away from my core competencies. Because my competencies are highly relevant to current market demands at the time of writing.

As for my personality, I draw energy from a balance of human interaction and computer interaction. In brief, I do not want to be 24/7 desk-bound to my computer interacting with data “pipelines” only.

I want to be actively involved in Business dealings, while putting my Computer Science (including Statistics) knowledge to good use. I can make an impact on my company’s bottom-line by pitching and selling solutions to customers — I love seeing happy customers.

In short, I want to commit my time to business dealings (i.e. marketing, pitching, sales, and management), while also developing my interest in Computer Science, Statistics, and Medicine.

By writing the above, I am clear on whether Bioinformatics is suitable for me and whether I need to pursue a PhD in Bioinformatics. For now, it is no.

Conclusion

There are plenty of resources online for having an executive overview of Genomic Data Science (a.k.a. Bioinformatics). Because of this vast amount of information, you can be easily inundated. So I suggest a methodical approach to understanding Bioinformatics.

My suggestion is to ask yourself up to five pressing questions. Answer those questions by researching it. After which, write down your thoughts, and read through your writing. I am certain you will have a crystal clear understanding of what Bioinformatics is to you. And lastly, get hands-on with Bioinformatics to briefly understand it!