Abstract
The ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil
Highlights
Advances in next-generation sequencing have supercharged biochemical assays into ‘big data’ genomic resources [1–3]
Efficient and scalable data visualization of analysis is a critical bottleneck in biological discovery within life sciences
We developed the STENCIL web platform to incorporate principles of project management with a strong emphasis on data reproducibility and FAIR data practices
Summary
Advances in next-generation sequencing have supercharged biochemical assays into ‘big data’ genomic resources [1–3]. This explosion in data has been paralleled by the development of multiple quality control and data analysis tools [4–8]. The unique analysis requirements from each distinct genomic assay complicates the already diverse ecosystem of bioinformatic tools by necessitating the creation of a novel tools and algorithms to maximize biological interpretation [9–12]. Many of these tools are equipped with quantitative and qualitative metrics designed to analyze user-supplied data, generating insights into different aspects of the experiment. It is often not possible to programmatically access the curated data and visualized results that retain their original relationship to the source data
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