Pipeliner: A nextflow-based framework for the definition of sequencing data processing pipelines

Anthony Federico, Tanya Karagiannis, Kritika Karri, DIleep Kishore, Yusuke Koga, Joshua D. Campbell, Stefano Monti

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The advent of high-throughput sequencing technologies has led to the need for flexible and user-friendly data preprocessing platforms. The Pipeliner framework provides an out-of-the-box solution for processing various types of sequencing data. It combines the Nextflow scripting language and Anaconda package manager to generate modular computational workflows. We have used Pipeliner to create several pipelines for sequencing data processing including bulk RNA-sequencing (RNA-seq), single-cell RNA-seq, as well as digital gene expression data. This report highlights the design methodology behind Pipeliner that enables the development of highly flexible and reproducible pipelines that are easy to extend and maintain on multiple computing environments. We also provide a quick start user guide demonstrating how to setup and execute available pipelines with toy datasets.

Original languageEnglish
Article number614
JournalFrontiers in Genetics
Volume10
Issue numberJUN
DOIs
StatePublished - 2019
Externally publishedYes

Funding

This work was supported by a Superfund Research Program grant P42ES007381 (SM) and the LUNGevity Career Development Award (JC).

FundersFunder number
Superfund Research ProgramP42ES007381

    Keywords

    • Nextflow
    • Pipeline development
    • RNA-seq pipeline
    • ScRNA-seq pipeline
    • Sequencing workflows

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