Beyond NGS data sharing and towards open science

TL;DR

This paper introduces NGSPipes, a framework that enables decentralized, platform-independent execution of bioinformatics pipelines, promoting open science and improving reproducibility in biosciences.

Contribution

The paper presents NGSPipes, a novel framework that simplifies building and executing bioinformatics pipelines without centralized servers, enhancing collaboration and reproducibility.

Findings

NGSPipes enables decentralized pipeline execution.

Framework supports platform independence.

Facilitates long-term pipeline archiving and reuse.

Abstract

Biosciences have been revolutionized by next generation sequencing (NGS) technologies in last years, leading to new perspectives in medical, industrial and environmental applications. And although our motivation comes from biosciences, the following is true for many areas of science: published results are usually hard to reproduce either because data is not available or tools are not readily available, which delays the adoption of new methodologies and hinders innovation. Our focus is on tool readiness and pipelines availability. Even though most tools are freely available, pipelines for data analysis are in general barely described and their configuration is far from trivial, with many parameters to be tuned. In this paper we discuss how to effectively build and use pipelines, relying on state of the art computing technologies to execute them without users need to configure, install and manage tools, servers and complex workflow management systems. We perform an in depth comparative analysis of state of the art frameworks and systems. The NGSPipes framework is proposed showing that we can have public pipelines ready to process and analyse experimental data, produced for instance by high-throughput technologies, but without relying on centralized servers or Web services. The NGSPipes framework and underlying architecture provides a major step towards open science and true collaboration in what concerns tools and pipelines among computational biology researchers and practitioners. We show that it is possible to execute data analysis pipelines in a decentralized and platform independent way. Approaches like the one proposed are crucial for archiving and reusing data analysis pipelines at medium/long-term. NGSPipes framework is freely available at http://ngspipes.github.io/.