Where do biotech companies receive bioinformatics services from?

I recently directed this question to experimental scientists in biomedicine. The results for as follows:

Do it myself (45%):

Like any other software, bioinformatics software is becoming increasingly user-friendly with the introduction of tools featuring graphical user interfaces. These tools are less dependent on coding skills, enabling experimental scientists to perform certain bioinformatics tasks. For example, tools like Gene Set Enrichment Analysis (GSEA) and Metascape allow users to perform pathway analysis without any coding. This can be a plausible approach when the data size is relatively small and the analysis is well-standardized.

Some experimental scientists are also proficient in scripting and analytics and can perform fairly complex bioinformatics tasks. However, like all other dual-skill fields, gaining proficiency in both wet-lab and computational domains comes at a cost—particularly when troubleshooting complex analyses, which can be time-consuming and inefficient.

Somebody in my group (7%):

Some large biomedical research groups have a bioinformatician on their team who can conduct analyses for the group. This is especially common in academic environments, but it also applies in industry settings. This role may be filled by a dedicated bioinformatician or by an experimental biologist with some computational background.

The main advantage of this approach is that the bioinformatician works in close contact with team members and has a strong understanding of the group’s research needs. However, the departure of the bioinformatician may pose a risk, as the analyses depend heavily on that individual. Having a second analytical team member can help mitigate this risk to some extent.

Bioinformatics unit in my organization (34%):

This strategy is especially common in industry and is becoming more prevalent in academia. Here, an independent Bioinformatics or Computational Biology department assigns its personnel to support biomedical projects.

This structure has several advantages: bioinformaticians receive training, support, and mentoring from peers within their department. They can contribute to multiple groups and projects, increasing their efficiency and productivity. When a bioinformatician leaves, their responsibilities can be transferred more smoothly by the department, relieving the research teams of this burden.

The main drawback of this approach is the potential for limited familiarity between the bioinformaticians and the research teams. This can be addressed by assigning one or two bioinformaticians consistently to the same projects, reducing the need for additional communication and ramp-up time.

Outside of the organization (14%):

This model is gaining traction, particularly among small pharmaceutical companies or research groups that lack the resources to hire in-house bioinformaticians. Their analytical needs may also require specialized expertise that is not available internally. In an economic environment where skilled labor is increasingly expensive, relying on external experts can be a cost-effective solution.

This approach offers multiple advantages. It provides the institution with near-instant access to the required expertise, without the delays and costs associated with recruitment and training. It can save both time and money.

However, there is a significant risk: if cost-saving becomes the sole motivation for outsourcing, the organization may fall into the “cheap labor” trap—sacrificing data quality and turnaround time for lower upfront costs. The solution lies in changing this perception. Rather than viewing outsourcing as a source of cheap labor, it should be seen as a way to quickly access a specialized workforce capable of extracting greater value from data in less time. In other words, outsourcing should be a strategy to do more with the same resources. This allows organizations to fully utilize both their data and their budget.