In-house or out-house: Is it time to outsource bioinformatics?

Recently, we came across two insightful perspectives on the state of the bioinformatics industry. Philipp Zenter’s post examines why the bioinformatics field may benefit from increased privatization, while Tatsiana Aneichyk explores the commercialization of bioinformatics research. Both authors highlight key challenges currently facing the field and discuss how the private sector could play a pivotal role in addressing these issues.

Bioinformatics is still the preserve of academia (although the situation is changing at least in the area of next generation sequencing), and as such subject to its rules. Two aspects of academic life have in particular a profound impact: firstly, the short-term nature of positions at the most junior level (PhD students and Postdocs), where most of the actual software development and bioinformatic analysis takes and secondly, the fact that publication of novel algorithms and tools is rewarded, but not their maintenance. 

As a consequence, established software quickly becomes obsolete and new tools and standards keep arising, forcing researchers to constantly adapt their pipelines accordingly and slowing down analysis. These challenges extend to biotech and pharmaceutical companies, which often face difficult decisions about whether to develop in-house solutions or rely on external providers for omics data analysis and exploration.

The collaborative nature of academia, which is essential to both push research further but also needed by researchers to apply for grants to continue their work, also brings its own complications. The increasing popularity of genome and transcriptome sequencing, for example, means the bioinformatics units have to spend more of their time analysing data from such collaborative projects, rather than developing new tools and algorithms. And since their services are essentially free to academic collaborators, demand is continuously increasing and often offloaded to already overworked and inexperienced PhD students and young postdocs. Analysis quality can suffer as a consequence, a fact that has not passed unnoticed in academia itself. This is also consistent with the recent reports on unreliable data analysis in scientific articles (1-2).

It would make sense for academic teams to outsource their analysis to  bioinformaticians offering their paid services, either as freelance actors or as part of a for-profit company. They provide both expertise and also have a strong incentive to maintain and update their pipelines over the long term, resulting in more efficient and quicker analysis.

This is even more crucial as bioinformatics is becoming more complex and specialised. It is increasingly hard for a bioinformatician (let alone a student) to become an expert in all the areas and subareas of bioinformatic analysis. And yet, in academia bioinformaticians are often expected to deal with vastly different data types analysis (e.g. protein structure, phylogenetic tree analysis and epigenomics) at a deep level. 

Thus, outsourcing tasks for which there is fewer competence in a group saves time and also ensures the analysis is performed by a bioinformatician who is highly familiar with the data and the methods of analysis.

This paradigm change in bioinformatics will bring with it new challenges and opportunities, both within academia, with a renewed focus on creative research and for start-ups focusing on data analysis such as BigOmics.