Purpose: The purpose of this dissertation is to investigate the feasibility of using tests of robustness in peer review. This study involved selecting three high-impact papers which featured open data and utilized bioinformatic analyses but provided no source code and refactoring these to allow external survey participants to swap tools, parameters, and data subsets to evaluate the robustness and underlying validity of these analyses. Technical advances that have taken place in recent years - scientific computing infrastructure has matured to support the distribution of reproducible computational analyses - enable this approach. These advances, along with cultural shifts encompassing open data and open code initiatives, promise to address technical stumbling blocks that have contributed to the "reproducibility crisis." To take full advantage of these developments toward improving scientific quality, authors, reviewers, and publishers must integrate reproducible analysis into the peer review process. Seven existing major case study types - reproduction, replication, refactor, robustness test, survey, census, and case narrative - have been invaluable toward establishing reproducibility as a serious and independent area of research. Of particular interest are refactors, in which an existing analysis with abstract methods is reimplemented by a third party, and robustness tests, which involve the manipulation of tools, parameters, and data to assess the scientific validity of an analysis. This thesis describes efforts to test the feasibility of robustness testing in the context of in silico peer review. The contributions described are complemented with extensive source code. Design and Methods: A multi-method approach was employed for this study consisting of user surveys and tests of robustness - hands-on, self-directed software development exercises. Three high-impact genomics publications with open data, but no source code, were selected, refactored, and distributed to active study participants who acted as quasi-external reviewers. The process of the refactor was used to evaluate the limitations of reproducibility using conventional tools and to study how best to present analyses for peer review, and the tests of robustness were employed under the hypothesis this practice would help to evaluate the underlying validity of an analysis. Three different approaches were taken in these tests of robustness - a faithful reproduction of the original manuscript into a framework that could be manipulated by participants, a workflow-library approach in which participants were encouraged to employ modern "off-the-shelf" pre-built pipelines to triangulate tests, and an advisor-led approach in which senior experts suggested alternate tools to be implemented and I generated a report for their evaluation. Findings: The refactors and tests of robustness produced numerous discoveries both in terms of the underlying scientific content and, more importantly, into the strengths and weakness of the three robustness approaches (faithful/workflow-library/advisor-led) and pain points in the analytic stack, which may be addressed with appropriate software and metadata. The principal findings are that the faithful approach may often discourage aggressive robustness testing because of the inertia imposed by the existing framework, the workflow-library approach is efficient but can prove inconclusive, and the advisor-led approach may be most practical for journals but requires a higher level of communication to be effective. The vast majority of time in all these refactors was spent on sample metadata management, particularly organizing sample groups of biological and technical replicates to produce the numerous and varied tool input manifests. Practical Implications: Reproducibility-enabled in silico peer review is substantially more time-consuming than traditional manuscript peer review and will require economic, cultural, and technical change to bring to reality. The work presented here could contribute to developing new models to minimize the increased effort of this type of peer review while incentivizing reproducibility. Value: This study provides practical guidance toward designing the future of reproducibility-enabled in silico peer review, which is a logical extension of the computational reproducibility afforded by technical advances in dependency management, containerization, pipeline frameworks, and notebooks.