Abstract

MotivationComputational biologists face many challenges related to data size, and they need to manage complicated analyses often including multiple stages and multiple tools, all of which must be deployed to modern infrastructures. To address these challenges and maintain reproducibility of results, researchers need (i) a reliable way to run processing stages in any computational environment, (ii) a well-defined way to orchestrate those processing stages and (iii) a data management layer that tracks data as it moves through the processing pipeline.ResultsPachyderm is an open-source workflow system and data management framework that fulfils these needs by creating a data pipelining and data versioning layer on top of projects from the container ecosystem, having Kubernetes as the backbone for container orchestration. We adapted Pachyderm and demonstrated its attractive properties in bioinformatics. A Helm Chart was created so that researchers can use Pachyderm in multiple scenarios. The Pachyderm File System was extended to support block storage. A wrapper for initiating Pachyderm on cloud-agnostic virtual infrastructures was created. The benefits of Pachyderm are illustrated via a large metabolomics workflow, demonstrating that Pachyderm enables efficient and sustainable data science workflows while maintaining reproducibility and scalability.Availability and implementationPachyderm is available from https://github.com/pachyderm/pachyderm. The Pachyderm Helm Chart is available from https://github.com/kubernetes/charts/tree/master/stable/pachyderm. Pachyderm is available out-of-the-box from the PhenoMeNal VRE (https://github.com/phnmnl/KubeNow-plugin) and general Kubernetes environments instantiated via KubeNow. The code of the workflow used for the analysis is available on GitHub (https://github.com/pharmbio/LC-MS-Pachyderm).Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

  • The relevance of big data in biomedicine is evident

  • We demonstrate by means of a metabolomics case study how Pachyderm can enable scalable and sustainable workflows

  • The goal of this study was to demonstrate Pachyderm as a bioinformatics workflow system based on software containers

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Summary

Introduction

The relevance of big data in biomedicine is evident. Technological advances in fields such as massively parallel sequencing (Shendure and Lieberman Aiden, 2012), mass spectrometry (Nilsson et al, 2010) and high-throughput screening (Macarron et al, 2011) are examples of how biology has shifted towards a data intensive field (Marx, 2013). The rapid increase in the number of data points and the size of the observations in those fields pose many difficulties, but this is definitely not the only obstacle. Apart from the need to process large amounts of data, computational biologists must manage analyses that.

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