In the modern era of biotechnology and medicine, just knowing biology is not good enough anymore. Biologists and biotechnologists need programming skills too. Programming is getting increasingly important in the field of Biotechnology and Medicine.
Just imagine – it took 2nd fastest vaccine (mumps vaccine) 5 years to develop. The COVID-19 vaccines took less than 1 year. It was possible due to AI, big data analytics, and bioinformatics.
Additionally, core biotechnology is a research-oriented career path. The biological lab skills might not work in the finance or the IT industry. It always helps if you diversify your skillset. Learning how to code and understand the basics of data science can help you to break into other industries as well. Read Want to Make It as a Biologist? Better Learn to Code!
Earlier, Ankita Murmu (B.Tech Biotechnology student back then) had shared her perspectives as a B.Tech Biotechnology student life in India and common problems faced by B.Tech Biotechnology students in India. In this post, Ankita Murmu shares her career journey as a Biotechnology graduate, especially the importance of programming in biotechnology and her learning roadmap.
Coding and R Programming for Biotechnology and Bioinformatics
By Ankita Murmu
What is Coding and Programming?
Coding is one of the important skills of the 21st century. In the current generation, nothing is possible without programming. Right from using the internet to using vending machines, everything works because of the codes that have been written by someone.
So, what exactly are programming and coding?
In simple terms, programming is developing software and coding is the language you require to develop the software. It has been a perception that programming is only for someone who is from Information Technology or Computer Science background. But, programming and coding are the foundations for anyone who develops software and works with data. In recent years, there has been a huge demand for programming and coding to work with biological datasets.
Why Should You Learn Programming as a Biotech or Life Science Student?
Gordon Webster, a computational biologist, quoted in one of his articles that “For the life scientist, learning to code is like learning to fly”. Incorporating computational skills in life science or biotech students and researchers can be considered as a prerequisite to cover the depth of computational thinking. Surprisingly, the field of Bioinformatics serves as an intersection in this aspect.
The Era of Computational Biology and Bioinformatics
Bioinformatics is an interdisciplinary field that includes different branches of science for analyzing and interpreting data with the application of computational tools.
The importance of programming comes into play because the amount of biological data produced from next-generation sequencing platforms is huge to analyze and interpret without software or tool. Also, for the ease of researchers, another interdisciplinary field, genomics, along with bioinformatics has been successful in analyzing and comparing massive genomic data.
The complementary skills of biology and computer science are required in all these cases. Learning bioinformatics like biology can be messy. But, coding opens up several possibilities to understand different organisms, different conditions, and different systems.
If you work with most of the bioinformatics tools, you will find most of them working from the command line interface which requires some amount of coding. Learning bioinformatics will not only help you construct algorithms and pipelines but will also help in understanding biological problems from a computational perspective.
Read how to improve employability as a B.Sc/B.Tech Biotechnology Student.
My Roadmap of Learning R Programming as a Biotechnology Graduate
To be effective in a programming language, it is not only important to grasp the basics of different programming languages but to also master at least one of them. Several programming languages have been used to date for working with data of humans, plants, and microorganisms. For large-scale data analysis, understanding statistics is a prerequisite. R programming is one of the most widely used programming languages in bioinformatics to perform statistics, visualizations, and data analyses.
Being a Biotechnology graduate, I was always inclined towards bioinformatics. I loved programming since my undergraduate days. When I had the chance to work on a bioinformatics project for my master’s thesis, I was overjoyed.
However, it was during those times that I heard about R programming. I was a newbie to this fascinating programming language but, unfortunately, R was not included in my curriculum. My thesis was for six months so it was quite a tight schedule for me to learn R and work on the project at the same time. However, I had the thirst to learn it.
Learning R Programming through Self-Paced Online Courses
Initially, I started learning R from YouTube thinking it would be easier for me to grasp the concepts but, it was quite confusing. Fortunately, I signed up for “R programming” coursework from Coursera offered by John Hopkins University.
This course has an excellent syllabus that provides an understanding of concepts, tricks for coding, and working examples for statistical data analysis. Assignments are challenging but satisfying to tackle. Also, the tutors are exceptional scientists who give a whole new experience than learning from a textbook or YouTube.
Advantages of Self-Paced Courses
One thing I like about the courses in Coursera is that it has a flexible timing for learning the topics and provides an option to attempt the assignments if you are unsuccessful at the first attempt. This helps the learners to rectify their mistakes and score better marks.
Surprisingly, you can even apply the concepts learned from R to many programming languages like Python. Soon after taking up the course, I joined an online international workshop “MultiOmics Box” by Decode Life to gain more hands-on experience with R.
My master’s thesis work provided me with a whole new experience to analyze data in the context of research. Since then my curiosity and enthusiasm in research involving bioinformatics applications increased.
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Advantages of R Programming
There are many good reasons why R is preferred over other languages for scientific computation. It is a continuously evolving language with upgrades and updates on the R packages frequently.
- Free to use: R is an open-source language which means no paid licenses are needed to use. It is accessible to everyone and anyone can contribute to the modifications of the R.
- Cross-platform programming language: R can run on all operating systems so the programmers only have to write one program to develop software compatible with all systems.
- Rich set of packages: R has more than 10,000 packages and the number keeps on increasing. These packages are useful in working with large sets of data and performing statistical analyses.
- Versatility: R is a versatile programming language that is helpful in smooth collaboration with other programming languages.
Applications of R Programming in Bioinformatics
R is used by many programmers and scientists for data analysis, machine learning, and statistical inference. As R provides a number of statistical packages and libraries, it is favorable for analysis in the field of bioinformatics and genomics.
Recently, R has been considered as one of the top programming languages widely used by data analysts and scientists across various fields. As R is used in an interdisciplinary field, a computer scientist might want to start with genome biology and a biologist with R programming.
In biology, experimental works provide the data. Due to a large number of variations, it is difficult to get exact measurements every time you measure something. These data can be represented in a lucid manner using graphs and plots made from R programming which is easy to comprehend.
Career Benefits of Learning R Programming for Biotechnology and Life Science Students
Throughout my academic journey, I had the opportunity to learn three programming languages- C, C++, and R. However, I only used R for research projects.
While studying the basic bioinformatics coursework during my under-graduation, I was not aware that programming languages like Python and R could be used in life science. The awareness of how programming can be useful for students with biotech or life science background is fuzzy.
Over the years, attending workshops, seminars, conferences, and training programs made me realize how programming can do wonders in your career. Majority of the scientists in academia or those working in a Biotech company use electronic calculators or spreadsheets to handle their data.
However, when you consider the computational resources available to a researcher in a laboratory, these fine tools do not seem akin. With the advent of emerging technologies, strong programming skills are needed to deal with a massive amount of biological data.
To learn programming as a life science or biotech student can be a complex task at the beginning. But, for a young biotechnologist or life scientist to work at cutting-edge research require rapidly evolving technologies and huge biological datasets for which solid programming and statistical skills are necessary to be productive.
Biggest Advantages if You Learn Coding & Programming as a Biotech Student
- By mastering the skills of computer science and biology, a life science or biotech graduate can expertise in bioinformatics. Bioinformatics is indispensable and is a promising path to professional success. The employability of a bioinformatician in the industry and academia has competitive salaries and is a long-lasting career.
- Programming languages like R has been used by data scientist as a go-to tool for most projects. A career as a data scientist in the life science or biotech industry is challenging but, has the potential to bring new tools to existing data through innovations.
- Researchers and medical scientists in academia who have extensive knowledge in quantitative fields like mathematics and statistics also learn and work with R to analyze genomic data. If you are aiming for a research career in academia, expertise in any one of the programming languages is valuable for your research work.
Read Career Transition from Biotechnology to Data Science – Experience Shared by VIT and UCL Alumnus.
Career Advice for High School Students
Learning R as the first programming language is not as weird as you think. Introducing R programming to high school students will teach them how to think about code and will directly impact their future careers.
An under-graduate student learning R might not apply their programming language for several months while a graduate student might need to apply immediately for their research projects. Similarly, a high school student learning R will have an ample amount of time to apply the code into a productive system. Learning R programming will open the door to opportunities or career paths that the high school students might want to take up.
Mathematics and statistics are popular among high school subjects. Learning the basics of R can help the students relate their knowledge to the codes they write and also compare the output of the code with the solutions of their calculations. Hence, investing time and effort early can provide surprising returns in terms of career prospects.
Editor’s Note
Ankita will be teaching R programming during the following two summer programs:
Bioinformatics and Biostatistics Summer Program for High School Students (Ideal for Grade 10 – 12 students)
AI and Data Science for Biology Summer Bootcamp (Ideal for High School and College Students)
About Ankita Murmu:
Ankita worked as a Data Curation Intern at NuGenomics. She completed her Bachelors & Masters in Biotechnology and interned at CSIR, Pine Biotech, and Guwahati Biotech Park.
Ankita comes from a land of the highest tea production in India and a place known for its red rivers and blue hills – Assam. Writing articles is her passion, traveling is her hobby and, she is a huge lover of food.
Featured Image Source: Genetics Digest