The prevalence of open-source bioinformatic tools in the genetic research space is enormous. According to The North Shore LIJ Research Institute, there are over 500 genetic analysis software packages – the great majority of which are free – as of August 2010.

Open-source tools are incredibly important in genetics. They allow new methodologies to be created and expanded. They tie a community of researchers together across countries, universities, and subject matter. They create the opportunity for genomic research to expand exponentially as there is unlimited access to analysis tools.

However, many researchers never stop to consider the hidden costs of open-source software.

Recently, we took at look at what some of these “hidden costs” may be and published a white paper on what we found: specifically, time and resources, potential damage to one’s reputation, and distraction from scientific discovery.

Let us know what you think in the comment section below! Have you ever experienced any of these costs? Are there any costs we’ve missed? Are there any costs of proprietary/commercial tools beyond the price that you consider when buying? Does this white paper shed some light on the struggles you face as a researcher with open-source tools?

Is Free Software Really Free?
Examining the hidden costs of open-source bioinformatic software tools

Executive Summary
When considering conducting genetic analysis, researchers often begin by looking at open-source tools or consider building a program themselves. In some instances, these are the only options available. While open-source tools are a vital and necessary component of any bioinformatics toolbox, many researchers never consider the hidden costs of open-source software when a proprietary option is available.

In this white paper, the true cost of “free” software is explored, including time allocation, potential damage to one’s reputation, and distraction from scientific discovery. Finally, five questions to consider in purchasing commercial software are discussed, as researchers weigh benefits and costs.

Background
There are many free things out there, and most people will ask, “Why should I pay for something that I can get for free?” This is a good question. The open-source movement has gained a lot of momentum in the last decade. In higher education, open-source programs such as Moodle (course management system), Kuali (financial system for academics), and Sakai (another course management system) have gained popularity. The genetic research field is no different with open-source tools such as Bioconductor and PLINK.

There are many great benefits to using open-source software, and many genetic research analysis projects would not have been completed without them. Burton Group, now a part of the Gartner research firm, describes the tendency to go open-source (OSS): “Cost is often cited as the primary motivator for using OSS. After all, if the software is free, doesn’t that mean that [the user] saves money?”^[i]

With scientific research at universities dependent almost entirely on grants, a culture of frugality and resourcefulness perpetuates. In recent history, even fewer projects are being funded given the National Institute of Health budget cuts. Thus, genetic researchers live in an environment where budgets are tight, grants are limited, and competition is fierce.

And when a grant is obtained, there is a tendency to spend the money on generating the data as explained by Mark Gerstein, a Professor of Biomedical Informatics at Yale University: “Historically, analysis has always been underfunded relative to data production… Previously [researchers] saw the data as the valuable thing and the analysis was an afterthought and easy to do.”^[ii] When new sequencing machines have price tags in the millions, it’s easy to see why.

With no money and such an “easy” task, a common belief runs rampant: that some graduate or post-doctoral student must be available to handle the bioinformatics (and they are cheap, right?). So Principal Investigators often rely on students to figure out the downstream analysis, with which they have little training or experience.

In an academic culture of self-reliance and required innovation, the prevalence of relying on open-source code or programming a one-off bioinformatics project from scratch seems logical.

But is “free” really free?

Jonathan Schwartz, former President of Sun Microsystems (acquired by Oracle in 2010) characterized open-source as a “free puppy”,^[iii] i.e. the puppy may be free, but the food, veterinary bills, and toys are costs that would not have incurred otherwise.

Open-source programs carry “hidden” costs that many researchers never consider: time and resources, reputation, and purpose. Let’s examine each.