The Truth Behind Free Software

Open-source tools and software are becoming a rising norm among researchers who want to conduct genetic analysis. Programs like Moodle (open-source learning platform), Kauli (financial system for academics) and even open-source Lab Information Management Systems like CloudLIMS have made quite an entrance in the market.

Open-source tools can be quite a catch within the bioinformatics industry, but often there are hidden costs that are not taken into consideration when making a choice between a premium option and free software. The true cost of “free” software is discussed in a white paper by Golden Helix where they explore the risks of time allocation, damage to reputation and distraction from scientific discovery.

This uptake of open-source software in understandable. Most research projects are depending on governmental grants and funding. In such cases, cutting costs wherever possible is the main priority among researchers, in order to fund other human resources costs. However, researchers often fail to foresee prospective hidden costs that are discovered through the course of the project.

We have extracted some hidden costs based on past literature and real-life experience as a bioinformatics company, some of them are listed below:

1. Open-source tools require programming and code-writing skills in order to operate and customize them based on your research needs. This leads to increase in time and money required to train/educate the personnel working with the software.
2. There is no stringent technical support policy with open-source software. They usually rely on community forums to answer customer queries.
3. Lack of program interoperability leads to results documented in different, incompatible data formats. This leads to further data conversion costs as well as delay in collaborative research.
4. Software viability can be a concern if the author loses interest in maintenance and sustainability of the software, leading to a sudden system crash or loss of data.
5. Unreliable and inconsistent open-source tools can lead to discrepancy in genetic analysis results, leading to a damaged reputation for a potential scientist.
6. Learning a new coding language to operate a free software leads to deviation from core scientific discovery goals for the researcher.

If you are looking to opt for free software, take into consideration the above factors. If the software company is able to provide quality customer support, ensures software viability and maintenance and offers free training programs, then free software can be the right choice. In any case, be sure to check for references and credibility of the software programmer.