Biologists encounter staggering amounts of data in their day-to-day work. Genomic data alone has grown faster than any other data type since 2015 and is expected to reach 40 exabytes per year by 2025. This rapid growth presents new challenges in acquisition, storage, distribution, and analysis for industry professionals.
While bioinformatics and computational biology may sound similar, they are distinct disciplines that scientists can use to help manage and understand all this data. Here are some of the main differences between computational biology and bioinformatics and when scientists should turn to each for their analyses.
What is Computational Biology?
“There are many aspects of biology that aren’t necessarily bioinformatics,” says Stefan Kaluziak, an associate professor of bioinformatics at Northeastern.
These areas are referred to as computational biology, which uses computer science, statistics, and mathematics to help solve problems. Computational biology can also include the development of algorithms, theoretical models, computational simulations, and mathematical models for statistical inference.
While computational biology relies on computers and technology, it typically does not imply the use of machine learning and other, more recent developments in computing.
“Computational biology concerns all the parts of biology that aren’t wrapped up in big data,” Kaluziak says.
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When to Use Computational Biology
Computational biology can be used in support of or instead of lab procedures, helping organizations save money and sometimes generate more accurate results. It is most effective when dealing with smaller, specific data sets, such as projects that involve conducting population genetics and protein analysis or understanding specific pathways within a larger genome.
It is also used to answer more general biological questions rather than pinpointing highly specific information.
“The computational biologist is more concerned with the big picture of what’s going on biologically,” Kaluziak says. As such, many take on more academic roles than laboratory- or field-based careers.
Scientists choosing to study significantly larger data sets or those that require multiple-server networks should instead turn to bioinformatics, which offers resources better suited to organizing and understanding big data.
What is Bioinformatics?
Bioinformatics is a multidisciplinary field that combines biological knowledge with computer programming and big data. It is particularly useful when dealing with large amounts of data, such as genome sequencing.
While Kaluziak notes that there is a great deal of overlap between computational biology and bioinformatics, the latter requires programming and technical knowledge that allows scientists to gather and interpret the results of more complex analyses.
“The goal in bioinformatics is teaching students how to best and most efficiently leverage all the different technologies available so they can accurately answer the questions that need answering,” Kaluziak says.
These technologies include advances in computing capacities, graphics cards, algorithmic analysis, machine learning, artificial intelligence, and others that can handle previously overwhelming amounts of data in less time.
When to Use Bioinformatics
Bioinformatics helps scientists analyze large amounts of data more quickly and accurately than ever before, sometimes allowing professionals to tackle data sets that were previously too challenging to work with because of their size.
“The future of biology is going to involve bioinformatics and big data,” Kaluziak says.
Leveraging machine learning, algorithms, visualization methods, and new software and database technology to tackle large data sets requires a strong understanding of bioinformatics. Given the sheer volume of biological data now available, it is quickly becoming a necessary skill for scientists to develop in order to close in on breakthroughs in biology.
Earning Your Master’s in Bioinformatics
The lines between computational biology and bioinformatics continue to blur, and most scientists use both at various points when dealing with biological data. As the amount of data available continues to grow, professionals with a strong background in bioinformatics will be in high demand. Earning your master’s in biotechnology is one way to become a more competitive applicant.
Northeastern’s program gives career changers and students with limited computer or biological knowledge the skills they need to take the next step in their careers, focusing heavily on critical thinking and problem-solving skills to prepare them for this fast-paced industry.
“We do a good job of exposing students to whatever gaps are missing in their knowledge,” Kaluziak says.
Students begin working with real-world data and developing their own programs right away, rather than working with polished data sets.
“We make a lot of mistakes in our programming, and the process of actually implementing that is probably the most effective way to learn it,” Kaluziak says.
Outside the classroom, students have access to numerous on-campus labs and professor-led projects in addition to the university’s signature co-op program. Those interested in undertaking a co-op can gain up to six months of full-time work experience before graduation with thousands of business partners all over the world, with many world-class co-ops available near Northeastern’s Boston and Seattle campuses.
Learn more about Northeastern’s master’s degree in bioinformatics here.