Bioinformatics career – Skills Required | Vast Applications | Best College | Training | Scope
Currently, one of the most lucrative career options in the field of life science is Bioinformatics because of its future-oriented problem-solving ability with higher accuracy, low manpower involvement, and high-speed data handling. As we all know, in the modern era every field of science is integrating itself with the use of high-performance computers. The same is applicable for life science too, and hence, the field of Bioinformatics is booming like no other in this particular field.
Before moving forward with the career options in Bioinformatics, we must know what bioinformatics is in the first place and why it has become the need of the hour. Bioinformatics comprises Bio-‘Biology’, infor-‘Information technology’, ma- ‘Maths’, ti-‘Statistics’, cs-‘Computer Science’. Although this is not the standard definition of Bioinformatics, it gives a better idea of what the field is capable of and the interdisciplinary knowledge you need to have in order to pursue this field. If we talk about Bioinformatics broadly, it requires the knowledge of both core biology and computer science.
So, first of all, let’s understand what the need is for using high-performance computers, programming languages, and databases in Life science or Biology.
| Need in Biology | Solutions provided by computers |
|---|---|
| Speed and accuracy | High-performance computers aid in drug candidate screening, reducing the time from years to days with better accuracy. Tools like Molecular Dynamics Simulation assess drug stability in an in-silico environment. |
| Handling large amounts of data | Computers process and analyze vast amounts of data from sequencing platforms, delivering results within days. |
| Remote access and handling | Access to high-performance clusters/supercomputers remotely or via Cloud computing enables researchers to work from anywhere, facilitating higher-level research. |
| Object-oriented and future-proof applications | Computing power evolution from desktops/laptops to HPCC, Cloud computing, supercomputers, and quantum computers enables more complex simulations and advancements in fields like Bioinformatics. |
| Assistance for wet lab biologists | Python and R libraries like ggplot and Matplotlib aid in visualizing data for publication standards. Bioinformatic pipelines assist in analyzing sequencing data, fulfilling the demands of hybrid research papers combining in-silico and wet lab experiments. |
- The first and foremost problem that computers solve in biology is that of speed and accuracy. If we take the example of drug candidate screening in the drug discovery process, the manual wet lab screenings involving thousands of druggable compounds used to take years of hard work. But now, with the involvement of high-performance computers in the drug discovery process (Computer-aided drug design), we can screen the same amount of druggable compounds in a few days, with better accuracy. Further, we can test the drug for its stability in an in-silico environment using tools like Molecular Dynamics Simulation.
- The second advantage of using high-performance computers is to handle a very large amount of data in one go, processing it, and getting the results within a few days, like handling and analyzing the data coming from sequencing platforms.
- The third most advantage, computer over the classic methods is remote access and handling. This might not sound very useful, but it is, as you can access any high-performance cluster/supercomputer remotely/Cloud computing network from anywhere in the world, which gives you the freedom of doing higher-level research from your comfort space.
Further, advantages involve the object-oriented and future-proof application because computing power is evolving day by day in a fastidious manner from home desktops/laptops to High-performance computing clusters (HPCC), Cloud computing, supercomputers to now quantum computers. As the computing power evolves so will the fields associated with it like Bioinformatics, for example in the late 90’s simulating a simple protein in water for 10 nanoseconds took months and now with the use of HPCC or supercomputers we can simulate more complex simulations of protein-ligand for 100 milliseconds in a few days or even hours.
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Bioinformatics Career – Role in Wet Lab
Bioinformatics is also very crucial for wet lab biologists in many aspects as more often they want to visualize any relevant data from their wet lab experiments in a presentable form and in accordance with the publication standards of reputed journals they can take the help of Python and R libraries like ggplot and Matplotlib.
Apart from that, one can perform the web lab sequencing technique and analyze the data themselves using bioinformatic pipelines to have a better understanding of the whole process. In the current scenario, most of the good journals are demanding hybrid research papers that involve both In-silico and wet lab experiments. The most famously used experimental pipeline nowadays is to do the experiment or screening in-silico as it saves a lot of time and validates the results of that experiment using a few wet-lab experiments. In this way, we can cut short the time and have more confidence in our results.
In order to access all of the things mentioned above, you need to have a core knowledge of Bioinformatics which can catapult your career in many high demanding fields of science practiced worldwide.
The scope of Bioinformatics is endless and forever growing but as it has many branches, it is highly advisable that you must have mastery over at least 3-4 branches to succeed.
Further in this article, we are going to discuss how to get started with Bioinformatics and how to choose the best Bioinformatics career options available worldwide.
BIOINFORMATICS AS A CAREER
When we talk about taking bioinformatics as a career or Career Transition In Bioinformatics, it becomes a different ball game altogether as compared to doing college projects and degrees because the most important thing that the Bioinformatics sector demands are skill sets, command over your skills, and to apply our rational mind in problem-solving. So, the gist of the story is that in the Bioinformatics field skill sets always count over degrees and reputation but that doesn’t mean you should not pursue higher studies like Ph.D. or Post-doc. in this field.
Higher studies like Ph.D. and Post-doc. give you a whole lot of experience and you get better with experience but as I mention in the previous lines also that just a degree without proper knowledge and hands-on experience is not worth anything as for all subjects and particularly in the objective-oriented subjects like Bioinformatics and computational biology.
One who is seeking to learn bioinformatics from scratch or wants to enroll for a degree in Bioinformatics should keep certain things in mind.
| Aspect | Description |
|---|---|
| Adaptability | Being from a biological background, one should be adaptive towards changes as bioinformatics encompasses various disciplines like physics, mathematics, computer science, and statistics. |
| Continuous Learning | In bioinformatics, continuous learning is crucial due to its rapid advancements. Staying updated with current trends is essential for professional growth in this field. |
| Emphasis on Practical Skills | Practical skills are paramount in bioinformatics, as they provide invaluable learning experiences. While theoretical knowledge is important, practical application often leads to deeper understanding. |
| Problem-solving Mindset | Bioinformatics requires a problem-solving mindset, especially when developing pipelines, workflows, and databases using programming languages like Python, R, Java, and PHP. |
| Object-oriented Approach | Having an object-oriented approach is vital in bioinformatics research and development, as it helps in setting clear goals and working efficiently towards achieving them. |
| Streamlined Working | Working in a streamlined manner is essential for efficiency and clarity, ensuring that your work is understandable and reproducible by others in the field. |
| Advanced Skill Sets | Apart from disciplinary aspects, advanced skill sets such as proficiency in specific programming languages, familiarity with bioinformatics tools, and experience with data analysis are advantageous. |
- Adaptability: Being from a biological background, one should be adaptive towards changes because in Bioinformatics you have to deal with sciences other than biology and chemistry like Physics, Mathematics, computer science, statistics and some interdisciplinary subjects.
- Continuous Learning- In all other fields also people do learn and repeat exercises but in Bioinformatics it is to be done in a more profound way as this field is related to computer science and biological research both it is growing in an accelerating manner and in order to keep yourself up to date with the current advancements you have keep your learning curve exponentially growing.
- Emphasis on practical skills- Practical skills or hands-on experiences proves to provide wonders in this field as you can learn more from your practical mistakes than from theoretical knowledge, having said that theoretical knowledge is not to be looked down upon instead it provides the base on which your practical knowledge stands.
- Problem-solving mindset- Bioinformatics is a field where you need to develop a lot of pipelines or workflows and databases etc. mostly with the help of programming languages like python, biopython, R, Java and PHP . So while developing new things, problems will come your way but you have to deal with them in a smart and rational way.
- Object-oriented and streamlined working– When you are in Research and Development it is important to have a goal in mind and work upon it (object-oriented) to achieve the goal or get near it, in this process you might discover something new as well and for the same your work should follow a streamlined pattern which is simple and understandable by others.
All the above-discussed are the disciplinary aspects of growth in the field of Bioinformatics but apart from all these you must need to have advanced skill sets that act as a cherry on the top.
Top Bioinformatics Career Skill Sets
The top skill sets required by a bioinformatician to thrive in this particular field worldwide are as follows:
| Skill Set | Description |
|---|---|
| Data analysis and interpretation | – Clean, process, and interpret complex data. – Customize analysis based on research questions or client requirements. – Utilize various tools and pipelines for data processing and presentation. |
| Programming languages | – Proficiency in Python and R, essential for data visualization, statistical analysis, and more. – Knowledge of additional languages like PHP, Java, and C beneficial for software development and database management. |
| Computer-Aided Drug Designing (CADD) | – Proficiency in tools for drug candidate selection, virtual screening, pharmacokinetic and pharmacodynamic study, protein-ligand docking, molecular dynamics simulations, and more. – Skill sets highly sought after in bio-pharmaceutical industries and research organizations focusing on drug discovery. |
| Genomic sequencing Data Analysis | – Expertise in analyzing genomic sequencing data, including DNA sequencing, RNA-Seq, and whole-genome sequencing data. – Familiarity with standard protocols and pipelines, as well as the ability to develop custom pipelines. – Mastery over tools like FastQC, trimmomatic, and HISAT2 for quality control, alignment, quantification, and analysis. |
| Artificial Intelligence and Machine Learning (AI/ML) | – Harness AI/ML techniques to enhance bioinformatics processes, including multi-omics analysis, drug discovery, and evolution biology. – Utilize algorithms such as Support Vector Machine, Convolutional Neural Network, and Random Forest for problem-solving. – Examples include AI-powered technologies like the SkinVision app for skin cancer detection and tumor identification from radiology images. – AI/ML proficiency opens career opportunities globally, with high demand in countries like the USA and Germany. |
- Data analysis and interpretation– Being a bioinformatician you need to deal with a lot of complex data and you will be expected to clean the raw data, process the data in a particular fashion and to present the results in a form that is understandable by others. The processing of data depends upon your research question or as per the requirements of your client. So, the analysis and interpretation totally depend upon what meaningful results you want to achieve or derive using that particular data. For instance, two bioinformaticians can be provided with the same data but the pipeline line they use and the result they achieve might be different but both are useful results with respect to their corresponding requirements.
- Programming languages– The use of programming languages in the Bioinformatics field has evolved over two decades, and now it has become a “must-need skill”. The most widely used programming languages are Python, python, and R. Python programming language is capable of many things like data visualization, classification of problems, training AI/ML models using different types of learnings, and RNA-Seq. data analysis, virtual screening of drug candidates and more. Apart from Python’s applications, it is also used worldwide because of its easy-to-understand syntax. R, on the other hand, is mostly used for statistical analysis and is a fantastic tool when it comes to data visualization and representation. For programming language knowledge in today’s scenario, we can use the phrase “the more the merrier” because it is good to know as many languages as possible. Like for example, if you want to develop software or a database to solve bioinformatics problems apart from Python, you would require knowledge of PHP, Java and C.
- Computer-Aided Drug Designing (CADD)– The drug biology industries are more prevalent and provide thousands of opportunities worldwide and in order to grab those opportunities you need to have drug designing and discovery skill sets which include handling tools for Drug candidate selection, virtual screening, pharmacokinetic and pharmacodynamic study of the druggable compounds like ADME/T and Lipinski’s rule of five analysis, cavitome and pocketome analysis of proteins, protein-ligand docking, multiple ligand docking, blind docking, Molecular dynamics simulations (MDS), Free energy perturbation and more.
All of these skill sets will help you get a job in the bio-pharmaceutical industries as well as in the esteemed research organizations worldwide that are dealing with data related to drug biology or focusing on novel drug discovery.
- Genomic sequencing Data Analysis– When we talk about genomic sequencing, the sequencing platforms generate a lot of data to deal with and for the same reason, we use computational power to analyze the data in a faster way while keeping accuracy in mind. The genomic sequencing data can be divided into multi-omics sequencing like DNA sequencing data, Transcriptomic or RNA-Seq. data and whole-genome sequencing data.
There are standard protocols and pipelines to carry out sequencing data analysis but you can also develop your pipeline to smoothen the process. The basic process followed in this starts with taking the raw data, Filtering and refining the raw data using quality control tools, trimming the data if required, reading alignment, quantification and differential analysis. The pipeline includes the use of tools like FastQC for FASTA quality control and refining the raw data, trimmomatic for trimming, HISAT2 for reading alignment and so on. The command over sequencing data analysis can prove wonders to your career.
In this particular area, you can start your career journey at a very young age because you can learn this specific skill set very fast, and most of the time, you don’t need a higher degree to begin your career as a Sequence Data analyst. As this particular domain of bioinformatics does not require very high computational power, you can get started with a laptop and pursue a career in freelancing. The client base in this particular domain is very strong, starting from big private biotechnology and genomic farms and Bio-MSME enterprises to government-based research laboratories.
Artificial Intelligence and Machine Learning (AI/ML) – When we talk about the current scenario, AI/ML has taken over every field, and so has bioinformatics, but only for the betterment of the field. Artificial Intelligence (AI) can be defined as a computer’s ability to mimic human cognitive abilities. AI is a bigger umbrella under which fall many tools to execute AI including Machine Learning (ML), Deep Learning (DL), Neural Network (NN), Computer Vision (CV) and Natural Language Processing (NLP) etc.
The AI/ML-based tools have revolutionized the process of multi-omics analysis, drug designing and discovery, evolution biology and many more. Using AI/ML expertise, you can catapult your career to new heights as it is the present and future. Machine Learning can be used to train a system either in a supervised, unsupervised or reinforced way, and the machine can later on use that learning to solve new problems.
The most widely used AI/ML algorithms in bioinformatics are Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Random Forest (RF). There are numerous examples of AI/ML-based technologies and pipelines used in different fields of life science, Bioinformatics and medicine, like the SkinVision app developed to detect skin cancer with an accuracy of 95%, Identification of malignant and benign tumors from the radiology images, etc. AI/ML skillset can not only place you in India but also abroad, specifically in the U.S.A. and Germany, as this field of science is in high demand worldwide.
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Now, let’s discuss some of the top Indian colleges and universities that provide degrees in bioinformatics so that if you are planning to build a career in bioinformatics at a young age, you can start learning early. The colleges and universities listed are as follows-
Top Indian colleges and universities that provide degrees in Bioinformatics
| Degree | Course Name | University/College |
|---|---|---|
| Bachelors | B.Sc. Bioinformatics | IBAB, Bengaluru |
| Amity University, Noida | ||
| Sathyabama University, Chennai | ||
| Masters | M.Sc. Bioinformatics | Pondicherry University, Puducherry |
| IBAB, Bengaluru | ||
| Sathyabama University, Chennai | ||
| Manipal University, Bengaluru | ||
| Stella Maris College, Chennai | ||
| M.Tech | M.Tech Bioinformatics | Delhi Technological University, Delhi |
| NSUT, Delhi | ||
| IIIT, Delhi (Computational Biology) | ||
| SRM, Chennai | ||
| VIT, Vellore | ||
| Ph.D. | Ph.D. in Bioinformatics | CSIR/ICMR/DBT labs |
| IISc, Bengaluru/TIFR, Mumbai | ||
| IITs/NITs/IISERs |
Growth Opportunities in Bioinformatics
In order to get a good placement in bioinformatics either in India or abroad with a handsome salary package, the command over the above-mentioned skill sets plays a pivotal role. Previously, in this article, we discussed how to get started in the field of Bioinformatics and how to pursue a career in terms of jobs. Now, let us discuss the growth opportunities that bioinformatics provides to the academic scholars like Ph.D., Post Doc., Researcher, Scientist, and likes. Bioinformatics is a field of analytics and analysis is always required so, it is never out of fashion be it in the job perspective or research perspective. The academic scholar can get the benefit of bioinformatics as being a wet lab biologist you must be generating a lot of data be it in cell culture, sequencing, microbiome, cancer, and other molecular biology techniques, All those data can be managed and analyzed in a better and faster manner using Bioinformatics.
Chronological Learning Order
Finally, we should know the standard chronological order of learning the things that will help you to crack a job in bioinformatics and to thrive in this field.
- The student who is in higher secondary should consider opting for a bachelor’s in bioinformatics, followed by a master’s degree. After that, he/she can decide either to go for a job in India/abroad or academics. For academics, it is recommended that you do a Ph.D. and become a research pioneer, be it a research scholar, associate professor, or scientist.
- Now, coming on to the most asked question, which is how to switch to a master’s in bioinformatics from other disciplines of biological science like biotechnology, botany, biochemistry, etc. So, the answer is very simple: one should opt for a master’s in bioinformatics by qualifying for the university-level exams, or if you want to pursue an M.Tech in Bioinformatics from top government colleges, you should qualify for a national-level exam like GATE (Graduate Aptitude Test in Engineering) where you will provide a decent fellowship alongside your degree. In GATE, you also have options to choose from, i.e., GATE BT (Biotechnology) and GATE XL (Life Sciences); most of the M.Tech Bioinformatics courses offering colleges and universities have eligibility for both GATE BT and XL.
Further Education and International Opportunities
Furthermore, if you are interested in higher studies like Ph.D., you can do it from private universities according to eligibility and for good government universities/research labs like IITs, NITs, IISERs, IISc, TIFR, CSIRs, ICMRs, and DBTs you have to qualify NET (National Eligibility Test) offered by UGC, CSIR, ICMR, and DBT. Once you qualify NET you can enroll in top government Colleges and research labs with your own fellowship (2 years as JRF and 3 years as SRF), apart from that you can also get enrolled in Ph.D. using your GATE scores in institutes like IIT, NIT, and IISER. In order to pursue your masters and Ph.D. from universities abroad India, you should go for exams like IELTS, TOEFL, GMAT, and GRE, depending upon the country you are aiming for.
Skill Enhancement without Higher Degrees
There is another way to learn the subject and to gain expertise over it without having a higher degree and that path is doing internships, hands-on workshops, live projects, etc. from the top training institutions like Biotecnika, UpGrad, Decode Life, NextGen Helper and likes. One advantage these institutes offer is convenience, as you can pursue the training from any part of the world as it is online and apart from that you just have to give 2-3 hours of your day for a few months and get your skills certified at the same time. These institutions teach you practical skill sets that make you industry-ready and you can get placement to better companies out there. One added advantage these institutes offer over others is that they have good connections in the field and can assist you in getting a core job easier than others.
Future Prospects and Challenges
To conclude, bioinformatics is a diverse field dealing with multiple disciplines at a time and the integration of biotechnology and bioinformatics can solve and address bigger problems in the field of Biological research. The future prospects of biotechnology must include bioinformatics and within bioinformatics as we move to more advanced technologies like the use of AI/ML, Deep learning, programming, use of supercomputers etc. we are progressing exponentially. In the future we can expect further advancements in the field including automated bio-bots, use of quantum computers, artificial super intelligence and much more. To achieve the above mentioned we as bioinformaticians have to work hard and try challenging ourselves to achieve new heights in the field as well as take the field of Bioinformatics to new highs.
About The Author:
Mr. Prodyot Banerjee is a seasoned professional in Computer-Aided Drug Designing, 
Bioinformatics Analysis, and Genomics, boasting rich experience from institutions like CSIR-IGIB, CSIR-CLRI, IIT Madras, and Delhi Technological University. With an M.Tech in Bioinformatics from Delhi Technological University, Prodyot has excelled in research and development roles, presenting his work at prestigious venues like IIT Kharagpur. His research is published in esteemed journals such as IEEE and Frontiers in Pharmacology, with more underway. Prodyot’s GATE 2019 qualification from IIT Madras underscores his dedication to both academic excellence and professional growth. With a proven track record and relentless pursuit of knowledge, he is a valuable asset in bioinformatics, genomics, and computer-aided drug design endeavors.
