Top N Reasons NOT to do a Ph.D. in Bioinformatics/Computational Biology

If you are looking for bioinformaticians’ salary, please check this post (What Is Really the Salary of a Bioinformatician/Computational Biologist?).

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We read a commentary in Casey Bergman’s blog titled “Top N Reasons To Do A Ph.D. or Post-Doc in Bioinformatics/Computational Biology”. It is a fantastic post, and everyone I talked to agreed with his points.

By our nature, we feel uncomfortable, when everyone agrees with a viewpoint. Especially, the people I questioned agreed about becoming internet programmers in 1998, buying houses in 2005 and working on alternate energy in 2008. Is there anything negative about doing a Ph.D. in Bioinformatics/Computational Biology?

Reason 1.

Bioinformatics has many layers, as discussed in the following articles.

A beginners guide to bioinformatics part I

A beginners guide to bioinformatics part II

Unless you can reach Layer 5, you remain a glorified technician in the bigger picture of things. Do you really think that is good enough reason to get a PhD?

To reach layer 5, you need to show some mathematical aptitude from much younger age. Changing course of the ship at postdoc stage is possibly too late.

Please note that we do not disagree with the nature of biology changing to include as much python scripting as lab work. The new students need to be inquisitive enough to learn the computing part, and use it for their research. However, using Twitter is one thing and running the company Twitter Inc. is another.

Reason 2.

CB: “5. You will publish more papers.”

CB: “6. You will have more flexibility in your research.”

CB: “7. You will have more flexibility in working practices. ….Seriously though, Computational Biology has many pluses when it come to balancing work and life, but still maintaining a high level of productivity.”

CB: “9. A successful scientist ends up in an office”

Do you see any inconsistency between 5-7 and 9?

If not, you will never go beyond layer 4.

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Edit.

Ok, we will add a bit more explanation than somewhat tongue-in-cheek remark above.

Lemma 1. (borrowed from CB): A successful scientist ends up in an office.

Lemma 2. (homolog.us observation) The only way to be successful in science is to be visible or to stand above the crowd.

Lemma 3. (borrowed from CB): As a computational biologist, you will publish more papers as well as have an easy life.

If you and your million competitors publish papers easily, while playing with their daughters (from CB), what chances are there for any of those papers to be highly visible?

]

Reason 3.

Biology is a physical science. Computer is a new tool to analyze the system just like molecular biology was in early 80s.

To find respect in layer 5 of computational biology, you will need to create new and effective algorithms, and that may make you master of the physical machine called computer, but it will take you away from living systems over time. To be a productive biologist, on the other hand, you will not be able to avoid the dirty and time-consuming work.

Let us go through the most important branch, from where biologists derive their largest funding from and explain why the physical science component will continue to remain the most important. Undoubtedly, it is the human health sector.

Many bioinformaticians seem to think that having access to genome sequences of all human beings will become the largest part of healthcare system, but that is far from true. Let us say that we run genome-wide association studies and find the most important genes for a rare disease. That gives a strong lead for finding cure, but we are far from curing the disease. Finding a drug that cures the disease will require very good knowledge of chemistry, proper understanding of the pathways affected by the drug and many, many months (years) of experimentation. It is true that the experimental part is time consuming, but society is willing to pay more for those curing diseases than those saying something about the diseases.

To be able to outshine the leaders in both places (computing and drug discovery) is extremely hard, because the computing world moves fast and so does the medical world. If you are really that good, you should seriously start thinking about not doing a PhD.

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Instead of being all negative, we also like to make some constructive suggestions to young students. If there is one thing homolog.us blog would recommend to young biology students, that is to learn Chinese and translate a biology paper to Chinese. It is far more important language to learn at this point of your career than C, C++ or Java.

We are convinced that the above suggestion will pay off better in the long run than becoming a bioinformatician, because none of our friends thoughts it is a good idea. Less competition in the long run?