What is the main goal of doing bioinformatics?

Tons of machine learning models, algorithms and complicated methods are being developed in academic level of bioinformatics. Every phd student working in bioinformatics is expected to develop a novel method to do some task. Some prefer using SVM, some prefer Bayesian networks and some neural network. Some try to develop new kernels that could have various applications in biology, and so on. They try to prove higher and higher accuracy of their models over others. I want to know how many of these tons and million models and methods developed are being used by other people in bioinformatics community? Are they simple enough to be easily understood and communicated by biologists? And top of it no research team want to use what already have been developed. They prefer to hire their own local programmers to build their in-house tools and methods doing the same thing. And here is one big reason why many bioinformatics companies are not able to do good business in selling their software and tools.

What is important is first to understand “What is bioinformatics?”and “What is the main goal of doing bioinformatics?”. I believe that the study and approach in bioinformatics is “Problem-driven” instead of “Solution-driven”. One should try to prove the quality and novelty of findings instead of the efficiency or accuracy of method applied. To be good bioinformatician, I would first start with understanding the biological question, problem and its hypothesis and then look for methods which can be used. This approach definitely needs interdisciplinary training to bioinformatics scientist. On the contrary, the interdisciplinary trained scientist are good for solving problems, but they might not be very good at implementing solutions which require more than interdisciplinary approach or some particular expertise skill.

Indeed multidisciplinary team has become a trend over interdisciplinary integration. Most of the pharmaceutical and bioinformatics companies are recruiting computer scientist and biologist rather than recruiting interdisciplinary bioinformaticians. I suspect this to be the approach where communication gap or linguistic difference arises within two groups trying to solve the same problem. Although, we have already seen experts in both fields to turn into interdisciplinary to take this field further so far, I still believe there is some gap where interdisciplinary experts and training like mine can prove a big asset.

How should a bioinformatician be trained?

This is one question which is troubling me since last 3 years. I am asking people in different seminars, reading blogs, articles and what not but still not clear. The question is, how should be a bioinformatician trained. How should be an ideal bioinformatics program be designed? Whether he/she be trained in the basic biology first or can a computer science expert having command on developing any software tool or performing computation-intensive work should specialize in life-science research. In present scenario, majority of leading bioinformatics experts are trained computer scientist and learn biology in due course of their research. And since bioinformatics is an inter/multidisciplinary field, some would say a bioinformatics training should have both components in its program together or one after other in any order. But the question is can one single person be trained in multiple disciplines and how well and quick can they learn.

I have raised this concern because it is personally related to me. I got exposed to bioinformatics, when an institute in India (my home country) came up with bioinformatics degree and they proposed that they will provide courses both in computer science and basic to advanced biology. This was very attractive to me. A new concept of learning, a new kind of specialization and something different from everyone else in India where everyone wanted to become either a doctor or an engineer. I took the challenge or I can also confess that I did not have guts or passion to wait another year or two to try getting into pure medicine plus I didn’t want to get in using sources. Well, I was in first batch we didn’t have any list of books assigned, no curriculum but just current topics of bioinformatics in syllabus. However, we had proper basic biology courses from Cell biology, Molecular biology, Genetics upto Virology and Immunology on biology side and basic programming courses in computation side with weaker computational aspect. Program was not very bad, however till the time I graduated after 4 years I was not at all clear what Bioinformatics means. That time approximately 8-10 years from now, some said it is database management to manage biological data while some said it is studying or modeling genes, proteins and cells using computer programs.

Finally, I decided to come to United States to explore more and got admitted to masters in computational biology. There, I started doing “mathematical modeling of malarial transmission dynamics of drug sensitive and resistant strains of malarial pathogen population under different treatment regimes”. Then I had to learn some of the mathematics and statistics background knowledge along with population genetics study which I didn’t know in my bachelors. This was interesting but then sometime in my second year, I saw a fasta file on my colleagues desktop and I got an urge to work on real genomic sequences and not modeling or simulation studies. For this I needed more skills so I joined PhD program in University of Missouri, Columbia. Here, I clearly stated that I wanted to work primarily on sequence data but nothing else. Now after starting PhD, I realized was lacking computer science expertise like machine learning methods, algorithm development, etc. and started learning those concepts to enhance my research here. So, again and again I am encountering the same question whether my training was accurate for bioinformatics work. May be I should have started with pure computer science training and gained expertise and then understand biology to apply that expertise to solve biological questions or problems or vice versa. Or, may be what I have is unique and try to be distinct!