SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis
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Author / Producer
Date
2016-06
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
Background
Next generation sequencing (NGS) produces massive datasets consisting of billions of reads and up to thousands of samples. Subsequent bioinformatic analysis is typically done with the help of open source tools, where each application performs a single step towards the final result. This situation leaves the bioinformaticians with the tasks to combine the tools, manage the data files and meta-information, document the analysis, and ensure reproducibility.
Results
We present SUSHI, an agile data analysis framework that relieves bioinformaticians from the administrative challenges of their data analysis. SUSHI lets users build reproducible data analysis workflows from individual applications and manages the input data, the parameters, meta-information with user-driven semantics, and the job scripts. As distinguishing features, SUSHI provides an expert command line interface as well as a convenient web interface to run bioinformatics tools. SUSHI datasets are self-contained and self-documented on the file system. This makes them fully reproducible and ready to be shared. With the associated meta-information being formatted as plain text tables, the datasets can be readily further analyzed and interpreted outside SUSHI.
Conclusion
SUSHI provides an exquisite recipe for analysing NGS data. By following the SUSHI recipe, SUSHI makes data analysis straightforward and takes care of documentation and administration tasks. Thus, the user can fully dedicate his time to the analysis itself. SUSHI is suitable for use by bioinformaticians as well as life science researchers. It is targeted for, but by no means constrained to, NGS data analysis. Our SUSHI instance is in productive use and has served as data analysis interface for more than 1000 data analysis projects. SUSHI source code as well as a demo server are freely available.
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Publication status
published
External links
Editor
Book title
Journal / series
Volume
17
Pages / Article No.
228
Publisher
BioMed Central
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Data analysis framework; Reproducible research; Meta-level system design
Organisational unit
02207 - Functional Genomics Center Zurich / Functional Genomics Center Zurich
08828 - Schlapbach, Ralph (Tit.-Prof.)