The Top Programming Languages to Learn in the Life Sciences

Courtesy Getty Images

As technology becomes more and more integrated with research and development, life science companies are increasingly seeking candidates who are well-versed in computer programming.

Not just any kind of programming experience is helpful, though. There are specific languages that a programmer should know to be successful in this industry.

To help, here are the best programming languages to learn for those entering or continuing their careers in the life sciences industry.

For decades, research and drug development depended on experiments that took place in wet labs, or labs that utilize physical samples, chemicals and liquids. Now, biopharmaceutical companies are filling seats in computational dry labs as well.

Dry labs utilize computer models and computer-assisted experiments to generate and analyze data. These dry labs can save companies a lot of money, manpower and time by testing their hypotheses on computer models before turning to traditional wet lab methodology.

Running an experiment in a dry lab requires knowledge of a computer programming language. These languages are typically text-based and are used to communicate with computers to run computational analyses.

Learning a programming language will help job applicants better position themselves for a spot in a biopharmaceutical dry lab. Additionally, learning to code can be useful for other applications in the life sciences industry, such as analyzing data from wet labs or contributing to data management.

Python is a widely used, high-level, general-purpose programming language that has an easy learning curve. It is popular within the scientific community, which offers a vast support network for learning that comes with coding libraries and tools.

This programming language emphasizes code readability by using English words that succinctly define the functions and purpose of the code. This makes it easy for those new to programming to understand how the code they are writing is interacting with the computer.

Python supports structured, object-oriented, and functional programming which primes it to be used in many different applications. Within the life sciences industry, Python is often used for genome sequencing, processing large-scale chemical libraries, machine learning purposes, or other biological computations.

There are many resources for learning Python, including Python for the Life Sciences - an introduction to coding in Python for those in the industry.

If you’re looking for a role in statistical computation or data analysis, you will want to know R. It’s considered one of the most popular languages for biostatisticians.

R is a programming language used primarily for data analytics. It’s used to import, clean and conduct statistical analyses of quantitative datasets.

Another major selling point of R is that it can produce data visualizations and static graphics.

Because R is free and open-source, companies may choose to utilize its data analysis capabilities to avoid paying expensive fees for statistical software. R is also useful for tackling the analysis of large-scale datasets, like those produced from proteomics.

Both Roche and Greentech have previously attested to using R for analyzing clinical trial data.

If you’re working with databases, there is a good chance you will be using SQL (sometimes pronounced “sequel”).

Structured Query Language, or SQL, is a programming language designed for managing and communicating with databases. It is useful for updating, retrieving and manipulating data from large databases.

SQL is an old programming language that has worldwide adoption in almost any industry that utilizes data in a relational database. In the life sciences industry, SQL could be used for laboratory information management and in other cases where large databases are involved.

While MATLAB is typically thought of as a language just for engineers, it does have its place in many other science roles. MATLAB is a programming language that allows for the implementation of algorithms, plotting of data and supports parallel computing.

Within the life sciences, MATLAB can be used to simulate pharmacokinetics and pharmacodynamics using its platform SimBiology. MATLAB is also useful in pharmaceutical manufacturing, where it can help to optimize yield during drug manufacturing.

MATLAB can also contribute to the analysis of data from the wet lab. The language can be used to analyze whole slide data and perform cell classification and radiomics analysis.

MathWorks, the corporation that owns MATLAB, provides an introductory book for those in the life sciences.

JavaScript is an object-oriented programming language that is widely used for webpages, but its ubiquity is ever-expanding. It is useful for controlling multimedia, animating images and creating interactive components on websites.

Although it isn’t as popular as some other programming languages within the industry, its popularity is on the rise. Some industry professionals are taking advantage of JavaScript’s usefulness by creating BioJS, an open-source JavaScript framework for biological data visualization.

JavaScript can also be useful to know if you are working with applications like Qualtrics, a survey building software. By using JavaScript, you can create dynamic, moving components to surveys.

While programming is popular, it is not the be all end all of analysis in the industry. Instead, companies might opt to use software that has streamlined the experience and is sometimes more user-friendly with a visual user interface.

Popular for data analysis and management is SAS, or Statistical Analysis Software, a suite of statistical software. SAS has many applications, including data management, predictive analytics and advanced analytics.

SAS advertises a Life Science Analytics Framework, a computing environment designed specifically for clinical research. The framework boasts faster time to market, built in regulatory compliance and controls and improved efficiency.

Not all programming languages are created equal, and you might be unsure of where to start. If you’re interested in learning programming in general or are a new learner, you should start with Python or JavaScript. These languages are used for many different applications and are easy to learn.

If you are interested in learning programming just for statistical analysis, you should start with R. Generally, if you’re interested in learning for a specific job, check out what languages the companies typically use in their scientific publications or presentations.

There are a lot of resources available to new learners. For example, you might consider using an IDE or integrated development environment. These software applications can take away the complexity of learning, debugging your code and building software. They are designed to provide users with a visual component to their coding process and built-in tools that make coding easier.

Additionally, many programs are free and open source. There are tutorials aplenty online, and if you’re stuck, you can utilize sites like StackOverflow where experienced programmers can provide answers and assistance to your programming troubles.

Learning a new language isn’t always easy, so try to begin with a skill or project in mind. Many beginner workbooks for programming provide easy to follow examples and step by step instructions. You might also find a coding buddy helpful to bounce ideas off of when you’re stuck on abstract concepts or syntax.