What is bioinformatics?
Perhaps the most common parallel between all modern cutting-edge software technologies is the issue of data. In the last decade, advancements in computer technology allowed us to gather incredible amounts of information. The point is how to understand it, analyze it better, and use it efficiently. Modern bioinformatics takes on the challenge of harnessing massive amounts of biological data from many different sources and truly realizing its potential.
The term “bioinformatics”
Broadly speaking, bioinformatics means using computer science for analyzing sequences of biological molecules in order to find patterns and better understand evolutionary relationships between organisms. Most usually, it refers to DNA, RNA, or protein. In its most modern version, it heavily utilizes cloud computing, machine learning, statistics, algorithms, and simulations. And, of course, terabytes of data.
The term itself, however, initially was used to refer to the study of information and processes in biotic systems. Back then, it was more related to biochemistry. It was coined by biologists, Paulien Hogewag and Ben Hesper.
What is the goal of bioinformatics?
There are a few critical roles of modern bioinformatics:
- enabling easy access to various types of data
- assuring fast and efficient data management
- development of new algorithms and statistical measures to understand relationships between large sets of data
- providing software tools to improve workflow, performance, and capacity potential in all biological types of research and development
What are the applications of bioinformatics?
Nowadays, bioinformatics is considered a backbone of many industries. It evolved from being a field reserved mostly for trailblazing science projects to widespread scientific and commercial usage. What kind of industries and types of projects exactly utilize and develop bioinformatics software?
Drug research and development in the current era are only partially based on trial and error. Most pharmaceutical companies’ processes are predominantly in silico, which means they’re computer-aided. It includes methods and technologies such as structure-based drug design, machine learning, and artificial intelligence.
Bioinformatics is heavily utilized in genomics, which is a study of the functions and structures of genes. The software-aided ability to analyze the genome can help make better treatment decisions and identify potential risks of side effects. It’s primarily used in cancer and HIV therapy. It also can help with searching for possible diseases in the genomic data.
Another use in genomics is emerging gene therapy which means changing defective genes with functional ones. The method is completely trailblazing and still requires a lot of research.
Agriculture and animal husbandry
There’s a vast potential for bioinformatics usage in both fields. The most widespread is in crop improvement. With a better understanding of the genome and biological processes, scientists are able to make more resistant, stronger crops, which then improves the disease resistance of livestock.
Being able to harness genomic data plays a significant role in evolutionary studies. Thanks to bioinformatics tools and methods, scientists can analyze and compare genomes of different species and better understand connections between them, their functions and characteristics.
Other examples include environmental protection, biofuels, alternative energy sources, veterinary science, and much more.
What types of companies and institutions are currently developing bioinformatics software?
Bioinformatics software worldwide nowadays is being developed by enterprises, startups, different types of research institutions, and academic teams. While obviously, pharmaceutical corporations have the most funding, there are plenty of fascinating projects done by university teams and startups.
The biggest struggle for many groundbreaking bioinformatics projects is the issue of marketability. Many products and ideas in the field have incredible scientific potential but lack the larger business perspective, resulting in funding issues. It’s a well-known case for many emerging projects. One of the ways to solve the problem is the collaboration between smaller companies or academic teams and corporations.
How to develop bioinformatics software?
The development process in bioinformatics isn’t fundamentally different from other innovative types of software. Like many other cutting-edge types of projects, it’s often heavily based on technologies like machine learning and AI, but the key factor is the necessary scientific background. In order to understand the language of bioinformatics and have a broader user perspective on the project, it’s mandatory for at least lead devs on the project to understand the scientific aspect of the software. That’s why it’s common for bioinformatics developers to be chemistry or biology graduates (like in our team, for example). It allows them to communicate efficiently and use their experience to make the product better for the user.
Other challenges of bioinformatics software development include dealing with massive amounts of data. In some cases, the rate of data generation may be even higher than the computational ability to process it. The problem is even more complex in terms of data integration between different resources. Most existing tools are not prepared to handle such enormous amounts. There are also problems with certain pieces of data not being digitized and the tricky question of what algorithm to choose for a particular use case.
You can read more about the subject in our article “What have we learned from working with bioinformatics software companies?”.
Bioinformatics-specific tools and languages
Plenty of specific bioinformatics software packages are available, such as Biopython, BioJava, BioJS, BioRuby, and much more. However, the most often utilized programming language for many science projects is, most likely, Python. We explain why it is so popular among scientists in the article “Python – why it's the best language for cheminformatics.”
Therefore, if you’re looking for a software house for a bioinformatics project, it’s best to search for a team with experience in those languages.
The future of bioinformatics
It’s safe to say that the future of life science is heavily dependant on software development. The most recent developments in AI and machine learning helped move forward a lot of ideas and projects. Another critical reason is the broader (and still growing) availability of public databases to allow multidimensional analysis. Whether it’s drug development, gene therapy, or environmental projects, the role of bioinformatics will only grow. We can also safely predict the growing number of cooperations between software and business companies and science institutions. From our experience, we can say for sure that this combination leads to excellent results.
As a software development agency with business experience and a bioinformatics specialization, we are proud to develop products and solutions to help scientists worldwide. So far, we were responsible for projects such as Syntha for Merck corporation, which is a tool for retrosynthesis. We’ve also participated in developing a flag product for L7 Informatics, an application for designing and controlling laboratory processes. Of course, we would love to work on more fascinating projects, so if you’re looking for a life science-oriented software development team, let us know.