Abstract

Computationally intensive disciplines such as computational biology often require use of a variety of tools implemented in different scripting languages and analysis of large data sets using high-performance computing systems. Although scientific workflow systems can powerfully organize and execute large-scale data-analysis processes, creating and maintaining such workflows usually comes with nontrivial learning curves and engineering overhead, making them cumbersome to use for everyday data exploration and prototyping. To bridge the gap between interactive analysis and workflow systems, we developed Script of Scripts (SoS), an interactive data-analysis platform and workflow system with a strong emphasis on readability, practicality, and reproducibility in daily computational research. For exploratory analysis, SoS has a multilanguage scripting format that centralizes otherwise-scattered scripts and creates dynamic reports for publication and sharing. As a workflow engine, SoS provides an intuitive syntax for creating workflows in process-oriented, outcome-oriented, and mixed styles, as well as a unified interface for executing and managing tasks on a variety of computing platforms with automatic synchronization of files among isolated file systems. As illustrated herein by real-world examples, SoS is both an interactive analysis tool and pipeline platform suitable for different stages of method development and data-analysis projects. In particular, SoS can be easily adopted in existing data analysis routines to substantially improve organization, readability, and cross-platform computation management of research projects.

Highlights

  • Computational biologists typically spend a significant portion of their time developing and using scripts written in general purpose scripting languages, such as shell, Python, R, and Ruby [1,2,3,4]

  • Motivated by the limitations of current systems for ad hoc data exploration, we developed Script of Scripts (SoS), a cross-platform, multilanguage scripting and workflow system designed for daily computational research

  • Development of SoS was driven by the need for a workflow system that can be used for daily computational research

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Summary

Introduction

Computational biologists typically spend a significant portion of their time developing and using scripts written in general purpose scripting languages, such as shell, Python, R, and Ruby [1,2,3,4].

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