GUIdock: Using Docker Containers with a Common Graphics User Interface to Address the Reproducibility of Research

Reproducibility is vital in science. For complex computational methods, it is often necessary, not just to recreate the code, but also the software and hardware environment to reproduce results. Virtual machines, and container software such as Docker, make it possible to reproduce the exact environment regardless of the underlying hardware and operating system. However, workflows that use Graphical User Interfaces (GUIs) remain difficult to replicate on different host systems as there is no high level graphical software layer common to all platforms. GUIdock allows for the facile distribution of a systems biology application along with its graphics environment. Complex graphics based workflows, ubiquitous in systems biology, can now be easily exported and reproduced on many different platforms. GUIdock uses Docker, an open source project that provides a container with only the absolutely necessary software dependencies and configures a common X Windows (X11) graphic interface on Linux, Macintosh and Windows platforms. As proof of concept, we present a Docker package that contains a Bioconductor application written in R and C++ called networkBMA for gene network inference. Our package also includes Cytoscape, a java-based platform with a graphical user interface for visualizing and analyzing gene networks, and the CyNetworkBMA app, a Cytoscape app that allows the use of networkBMA via the user-friendly Cytoscape interface.

[1]  Adrian E. Raftery,et al.  Fast Bayesian inference for gene regulatory networks using ScanBMA , 2014, BMC Systems Biology.

[2]  Adrian E. Raftery,et al.  CyNetworkBMA: a Cytoscape app for inferring gene regulatory networks , 2015, Source Code for Biology and Medicine.

[3]  S. O’Brien,et al.  SmileFinder: a resampling-based approach to evaluate signatures of selection from genome-wide sets of matching allele frequency data in two or more diploid populations , 2015, GigaScience.

[4]  Mikel Egaña Aranguren,et al.  Enhanced reproducibility of SADI web service workflows with Galaxy and Docker , 2015, GigaScience.

[5]  Benno Schwikowski,et al.  The Cytoscape app article collection , 2014, F1000Research.

[6]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[7]  Adrian E. Raftery,et al.  Bayesian Model Averaging methods and R package for gene network construction , 2016 .

[8]  W. Huber,et al.  Differential expression analysis for sequence count data , 2010 .

[9]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[10]  T. Ideker,et al.  A decade of systems biology. , 2010, Annual review of cell and developmental biology.

[11]  A. Nekrutenko,et al.  Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences , 2010, Genome Biology.

[12]  Fang-Xiang Wu,et al.  A group LASSO-based method for robustly inferring gene regulatory networks from multiple time-course datasets , 2014, BMC Systems Biology.

[13]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[14]  Adrian E. Raftery,et al.  Bayesian Model Averaging: A Tutorial , 2016 .

[15]  Paul Shannon,et al.  CyREST: Turbocharging Cytoscape Access for External Tools via a RESTful API , 2015, F1000Research.

[16]  Carl Boettiger,et al.  An introduction to Docker for reproducible research , 2014, OPSR.

[17]  Galina V. Glazko,et al.  Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data , 2012, Front. Gene..

[18]  Roger E Bumgarner,et al.  Construction of regulatory networks using expression time-series data of a genotyped population , 2011, Proceedings of the National Academy of Sciences.

[19]  A. Raftery Bayesian Model Selection in Social Research , 1995 .

[20]  Daniele Santoni,et al.  GIANT: A Cytoscape Plugin for Modular Networks , 2014, PloS one.

[21]  Dianjing Guo,et al.  A new multiple regression approach for the construction of genetic regulatory networks , 2010, Artif. Intell. Medicine.

[22]  Chris T. A. Evelo,et al.  CyTargetLinker: A Cytoscape App to Integrate Regulatory Interactions in Network Analysis , 2013, PloS one.

[23]  Olivier Sallou,et al.  BioShaDock: a community driven bioinformatics shared Docker-based tools registry , 2015, F1000Research.

[24]  N LeNovère Quantitative and logic modelling of molecular and gene networks. , 2015 .

[25]  Brian A. Nosek,et al.  Promoting an open research culture , 2015, Science.

[26]  Carl Boettiger,et al.  An introduction to Docker for reproducible research, with examples from the R environment , 2014, ArXiv.

[27]  Thomas D. Wu,et al.  A comprehensive transcriptional portrait of human cancer cell lines , 2014, Nature Biotechnology.

[28]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[29]  Christophe Ambroise,et al.  Statistical Applications in Genetics and Molecular Biology Weighted-LASSO for Structured Network Inference from Time Course Data , 2011 .

[30]  Dario Floreano,et al.  Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods , 2009, J. Comput. Biol..

[31]  Jocelyn Kaiser,et al.  The cancer test. , 2015, Science.

[32]  Simon Urbanek,et al.  Rserve A fast way to provide R functionality to applications , 2003 .

[33]  Adrian E. Raftery,et al.  Integrating external biological knowledge in the construction of regulatory networks from time-series expression data , 2012, BMC Systems Biology.

[34]  Michael L. Creech,et al.  Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.