A high-throughput screening platform in Docker

Motivation

Dealing with massive amounts of biological data is unthinkable without state-of-the-art tools. Over time, these applications have become increasingly complex and can often only be used when a long list of preconditions are met. There are serious issues with the installation and maintenance of tools due to version conflicts, outdated repositories and poor documentation. Moreover, complex tasks require integrating several tools into a workflow. Open platforms like Galaxy and Taverna have emerged to simplify building and operating such workflows. Nevertheless, ensuring the fulfillment of all preconditions remains a critical issue.

A solution to encapsulate a tool with its dependencies in container images is Docker. An extension, called Docker compose, further facilitates interaction of several containers in a coherent software configuration. Here, we demonstrate the power of this approach by creating and deploying an integrated high-throughput screening (HTS) platform through Docker compose, which comprises systems for laboratory information management, HTS sample and plate management, HTS data analysis, systems biology analysis, and reverse-phase-protein array data management/analysis, as well as service containers for relational database management, load balancing and for a key-value store.

Docker is a promising solution to fully address issues in deploying scientific software even in cases where several tools need to be integrated. In addition, Docker compose allows, for instance, to deploy a complex HTS platform in a single command. We expect that the use of Docker in cases like this will enable the more widespread use of scientific software and free up time spent on dealing with software dependencies.

A detailed description of this platform can be found here:

List, M. (2017).
Using Docker Compose for the Simple Deployment of an Integrated Drug Target Screening Platform.
Journal of Integrative Bioinformatics, 2017, doi: 10.1515/jib-2017-0016

The following tools are included in this platform:





Additional References

  1. List, M., Lemvig Pedersen, M., Schmidt, S., Christiansen, H., Tan, Q., Mollenhauer, J., Baumbach, J. (2016).
    Efficient Management of High-Throughput Screening Libraries with SAVANAH.
    SLAS Discovery (formerly Journal of Biomolecular Screening), 2016, doi: 10.1177/1087057116673607

  2. List, M., Schmidt, S., Christiansen, H., Rehmsmeier, M., Tan, Q., Mollenhauer, J., Baumbach, J. (2016).
    Comprehensive analysis of high-throughput screens with HiTSeekR.
    Nucleic Acids Research, 2016, doi: 10.1093/nar/gkw554
    Abstract | Web application link

  3. List, M., Alcaraz, N., Dissing-Hansen, M., Ditzel, H., Mollenhauer, J., Baumbach, J. (2016).
    KeyPathwayMinerWeb: online multi-omics network enrichment.
    Nucleic Acids Research, Web Server Issue 2016, doi: 10.1093/nar/gkw373
    Abstract | Web application link

  4. List, M., Franz, M., Tan, Q., Mollenhauer, J., Baumbach, J. (2015).
    OpenLabNotes - An Electronic Laboratory Notebook Extension for OpenLabFramework.
    Journal of Integrative Bioinformatics, doi:10.2390/biecoll-jib-2015-274
    Abstract | GitHub

  5. List, M., Block, I., Pedersen, M. L., Christiansen, H., Schmidt, S., Thomassen, M., Tan, Q., Baumbach, J., Mollenhauer, J. (2014).
    Microarray R-based analysis of complex lysate experiments with MIRACLE.
    Bioinformatics, 30(17), i631-i638. doi:10.1093/bioinformatics/btu473
    Abstract | GitHub

  6. List, M., Schmidt, S., Trojnar, J., Thomas, J., Thomassen, M., Kruse, T. A., Tan, Q., Baumbach, J., Mollenhauer, J. (2014).
    Efficient Sample Tracking With OpenLabFramework.
    Scientific Reports, 4. doi:10.1038/srep04278
    Abstract | GitHub