Do you develop bioinformatics tools for cancer researchers? Do you want to make sure they can use your tool? No-code solutions may be, well, the solution!

While there are pros and cons to this approach, in this blog, we explore the value of no-code solutions and basic tips you should keep in mind when developing your tools.

Why should I embrace no-code solutions for my bioinformatics tool?

There’s a lot of interdisciplinary collaboration within the field of bioinformatics, and what comes easily to some people may not be practical (or even possible) for others. Cancer researchers are often looking to quickly test and go through their ideas, models, and applications, but they may not have much (if any) knowledge of coding. If you want the most cancer researchers to get the most out of your tool, a no-code solution may be the right choice!

If you build a bioinformatics tool with no-code solutions, the graphical user interface (GUI) you create will feature a library of pre-built components, modules, or building blocks that researchers can combine and customize to create the application or workflow they want. But what specifically can make no-code solutions so helpful to the scientists who might use your tool?

Here are some benefits of developing a no-code bioinformatics tool:

• Rapid prototyping and iteration means researchers can do their work faster and more efficiently.
• User-friendly interfaces and pre-built tools for data analysis, visualization, and reporting allows researchers to quickly explore and get insight. Cancer data can be complex; make things simpler by removing the need-to-know coding!
• Researchers can easily combine and work with data from various sources without writing custom code for data integration. Whether it’s electronic health records, genomic databases, or clinical trial data, with a no-code tool researchers can use the pre-built components to combine their data.

What are quick tips for making a bioinformatics tool easier to use?

1. Focus on web-based tools as opposed to desktop tools that researchers must download. A good example of a user-friendly tool for cancer researchers is the Genomic Data Commons.
2. Regularly maintain your tool. Researchers may be frustrated by tools that have outdated information, have slower than expected processing speed, or lack security features. Cancer researchers are looking for tools that are not only easy to use, but also up to date.
3. Create an intuitive interface. We know we want functionality in a tool, but don’t forget to consider the ease with which a researcher can learn the basics (and beyond).
4. Implement drag-and-drop features, if possible. If people can simply upload and input their data files, choose their app setting, and run the task, then you’re well on your way to a bioinformatics tool that cancer researchers can actually use.
5. Make sure the first screen researchers will see is simple and informative. This means including initial instructions and making the page visually appealing.
6. Remember that interactive menus are common on websites these days, and including menus to help people navigate your tool is a great addition.
7. Build a documentation and community support resource within your tool where researchers can access training materials or reach out for help.
8. Consider if your tool makes collaboration easier! Are there forum pages for discussion? Is it simple for researchers to work together with colleagues within the tool? Can they quickly and easily share generated reports?

I want to take on this worthwhile challenge. So, what’s next?

You can start by exploring some of the existing GUI tools for bioinformatics! By taking a look at what’s out there, you can get an idea of what these tools commonly include and what features could be helpful to include in your own.

• With Galaxy, you’ll see an example of an open source, web-based platform that offers researchers thousands of tools, a tutorial, and no-code solutions. Galaxy receives site maintenance and development support from an NIH National Human Genome Research Institute award, among others.
• You can look at the NCI Informatics Technology for Cancer Research (ITCR)-funded Gene Set Enrichment Analysis tool for an example of extensive instructions on how to use the tool.
• There are also several ITCR-funded, low-code tools you can explore, including:
  • OpenCRAVAT,
  • UCSC Xena, and
  • WebMeV.

Let us know what else you’d like to hear about on the topic by leaving a comment below! And if you want to know about the latest in cancer data science, subscribe to weekly email updates.