Docker virtualization allows for software tools to be executed in an isolated and controlled environment referred to as a container. In Docker containers, dependencies are provided exactly as intended by the developer and, consequently, they simplify the distribution of scientific software and foster reproducible research. The Docker paradigm is that each container encapsulates one particular software tool. However, to analyze complex biomedical data sets, it is often necessary to combine several software tools into elaborate workflows. To address this challenge, several Docker containers need to be instantiated and properly integrated, which complicates the software deployment process unnecessarily. Here, we demonstrate how an extension to Docker, Docker compose, can be used to mitigate these problems by providing a unified setup routine that deploys several tools in an integrated fashion. We demonstrate the power of this approach by example of a Docker compose setup for a drug target screening platform consisting of five integrated web applications and shared infrastructure, deployable in just two lines of codes.
Electronic laboratory notebooks (ELNs) are more accessible and reliable than their paper based alternatives and thus find widespread adoption. While a large number of commercial products is available, small- to mid-sized laboratories can often not afford the costs or are concerned about the longevity of the providers. Turning towards free alternatives, however, raises questions about data protection, which are not sufficiently addressed by available solutions. To serve as legal documents, ELNs must prevent scientific fraud through technical means such as digital signatures. It would also be advantageous if an ELN was integrated with a laboratory information management system to allow for a comprehensive documentation of experimental work including the location of samples that were used in a particular experiment. Here, we present OpenLabNotes, which adds state-of-the-art ELN capabilities to OpenLabFramework, a powerful and flexible laboratory information management system. In contrast to comparable solutions, it allows to protect the intellectual property of its users by offering data protection with digital signatures. OpenLabNotes effectively closes the gap between research documentation and sample management, thus making Open- LabFramework more attractive for laboratories that seek to increase productivity through electronic data management.
Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.