The role of bioinformatics in the modern world of science and healthcare

In a general sense, the role of bioinformatics is mainly to analyze, organize, store, and visualize biological data in order to understand it better and utilize it for different purposes. In the advanced, modern way, bioinformatics heavily depends on cloud computing, machine learning, statistics, algorithms, and simulations.

The new approach to analyzing data revolutionized many industries and processes. For instance, most pharmaceutical companies completely changed the way of drug development, from trial and error to mostly in silico methods, often heavily aided by machine learning and AI.

Bioinformatics also plays a significant role in the groundbreaking industry of personalized medicine with its role in genomics, a study of the functions and structures of genes. Tools provided by bioinformatics enable the analysis of the genome in order to predict the effects of drug therapy better and identify potential side effects. They’re also utilized in the trailblazing field of gene therapy and preventive medicine.

What does it mean to develop bioinformatics software?

The life-science industry is already heavily dependent on bioinformatics. Corporations are in the game for a long time, but there’s also a massive potential for fresh ideas and emerging startups. The utilization of AI and machine learning for the processes opened the world of possibilities for cutting-edge projects. There’s also an instrumental aspect of better access to data and more capability to process it. All those advancements are fundamental for the future of life sciences in general.

The potential challenges include commercialization. Many trailblazing projects are developed by academic teams with limited financial resources and insufficient business knowledge. Cooperation between academics and software companies can significantly improve the workflow, but they require a mutual understanding, which brings us to the next point.

How different from everything else is bioinformatics development?

While it’s unnecessary for every member of a bioinformatics project to have a scientific background, having core members with academic knowledge is incredibly valuable. Understanding the domain of bio/cheminformatics enables smooth communication between all involved parties, which is crucial for an efficient development process. Moreover, for most modern bioinformatics applications, it’s necessary to understand concepts of computational chemistry and analysis and machine learning.

An ideal bioinformatics development team has top-notch programming skills and at least a few members with high expertise in the field of biology and chemistry. In our case, in addition to the experience in bioinformatics projects, we have several chemistry graduates, even on the Ph.D. level. It gives the whole team a better understanding of what the software is supposed to be and how it will be used. That allows them to not only execute the plan but gives them a chance to make the product even better.

Of course, if you already have a team of science-oriented developers, it’s possible to just look for any good team of developers. Still, from our experience, we can say it’s never an ideal option.

How can an experienced bioinformatics software agency improve the development process?

Knowing the most common challenges of the domain, experienced teams can influence the project in many ways, such as:

  • Delivering higher quality of code thanks to lab experience, knowing methods of research and users’ requirements
  • Saving time thanks to knowing all industry-specific algorithms and other tips and tricks (and how to use and implement them)
  • Saving money thanks to fast and efficient identification of bugs and other code shortcomings (bug fixing costs a lot, and as study shows, it’s possible to save even 100 times more money depending on how quickly bugs are detected)
  • Proposing better solutions for handling enormous amounts of data (one of the most challenging elements of bioinformatics)
  • Selecting the appropriate algorithm (machine learning and AI brings a lot of possibilities, but choosing the right one is a big challenge)

What are the biggest obstacles for bioinformatics startups, and how can we help to avoid them?

Aside from the technological aspects of bioinformatics development, the crucial challenge for startups is funding. To keep investors and clients interested and satisfied, it’s necessary to consistently improve and show the project’s value. That, of course, requires long-term planning and big picture thinking by product owners. But, unfortunately, that may all fail without a smooth and effective execution.

That just begs for a well-known quote. How about George S. Patton?

A good plan violently executed now is better than a perfect plan executed next week.

Yeah, it always works.

Deadlines matter, and it takes skill, experience, and efficient communication to deliver on time, especially with a limited budget. In this case, building a good MVP version is often a matter of life or death for the project. That’s where the value of an experienced bioinformatics team really shows. A team that’s well-adjusted in the industry is able to solve problems much quicker (and cheaper) because team members speak the same scientific language. They don’t need to spend time discussing the basics and focus on searching for the best possible solution without the constant need for consultation with the client, which comes especially handy in cooperation with teams working in distant time zones.

Additionally, the nature of science-related projects brings an omnipresent element of uncertainty. The result is never known right away. You have to analyze repeatedly before you come up with something rock solid, which can also be another unfavorable factor in terms of funding. That’s another issue that we’re able to assist with. As a team with expertise in both business and software development, we’re able to effectively communicate with stakeholders, explaining matters of scalability, security, and other aspects that are crucial from the investment standpoint. Last but not least, we’re always making sure to create the best user experience in every product we develop. The truth is that people buy with their eyes, so the often underestimated UX aspect may often be decisive.

The importance of security for bioinformatics projects

More often than not, building bioinformatics software means dealing with highly classified, sensitive data. Any risk of a breach is completely unacceptable, so agencies participating in bioinformatics projects must maintain every level of established security precautions. Signing NDAs or any other papers doesn’t cut it. In order to protect proprietary data, all members of the project must comply, no question.

Throughout many of our projects in recent years, we’ve seen many different techniques for maintaining security. Of course, some of them may be troublesome, but they’re perfectly understandable given the stakes. In our case, there are three levels of security.

Technical, which includes two-factor authentication for every account we use (no exceptions), encrypted Wi-Fi network, and even VPN and encrypted hard drives if necessary. On top of that, all documentation, source code, testing environments, and project management tools should be hosted and owned by you. We can also generate fake data to test the software or work on just a fraction of it to keep the crucial fraction completely safe.

There is also a physical security aspect. Our building is under 24h security, and it’s impossible to get in without a personalized card.

Last but not least, the legal aspect. We assure the highest security of all intellectual property with NDAs and additional documentation according to the project’s requirements.

What’s our experience with bioinformatics?

For the last five years, we have participated in several successful cheminformatics and bioinformatics projects. They involved subjects such as retrosynthesis, ELN, EM, and clinical trials. One of the model examples is Synthia for Merck corporation. For the project, we were responsible for full-stack development and optimization of the search algorithm in traversing dynamically built, quasi-infinite graph (yes, we can explain that). We are also developing an application for managing lab processes for L7 Informatics.

We have to admit, creating meaningful, valuable software is really addictive, so naturally, we’re looking for more. If you’re looking for an experienced team to help you with any bioinformatics project, we’ll be happy to discuss it with you.