Nextpie: a web-based reporting tool and database for reproducible nextflow pipelines

  • Bishwa Ghimire*
  • , Nicholas Booth
  • , Tapio Lönnberg
  • , Tero Aittokallio
  • *Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Motivation High-throughput genomic data analysis consists of the inexorably intertwined inputs and outputs of a vast array of bioinformatic analysis tools. To guarantee streamlined and reproducible analyses, the often complex data analysis pipelines need to be run using workflow management tools. Nextflow is one popular tool commonly used to automate such pipelines. Nextflow records key pipeline data, such as the submission time, start time, completion time, CPU usage, memory usage, and disk usage for each task run. These data are stored in log files, often scattered across a file system. Therefore, aggregating information about resource usage critical for the optimization of Nextflow pipelines and improving reproducibility, as well as parsing and managing such log data, can quickly become cumbersome. Results Here, we present a web-based tool, Nextpie, which provides both a database and a reporting tool for Nextflow pipelines. Nextpie stores comprehensive resource usage information in a relational database, thus facilitating and accelerating the performance of a variety of data analyses and interactive visualizations, providing an easily comprehensible overview of a pipeline’s resource usage. Availability and implementation The Nextpie source code, user documentation, an SQLite database with test data, and a Nextflow example pipeline are available at GitHub (https://github.com/bishwaG/Nextpie).

Original languageEnglish
Article numbervbaf252
JournalBioinformatics Advances
Volume5
Issue number1
DOIs
Publication statusPublished - 10 Oct 2025
MoE publication typeA1 Journal article-refereed

Funding

This work was supported by the funds from Research Council of Finland [340141, 344698, and 34580 to T.A., 311081, 314557 and 335977 to T.L.]; Norwegian Health Authority South-East [2020026, 2023105 to T.A.]; and Norwegian Cancer Society [216104 and 273810 to T.A.]. This work was supported by the funds from Research Council of Finland [340141, 344698, and 34580 to T.A., 311081, 314557 and 335977 to T.L.]; Norwegian Health Authority South-East [2020026, 2023105 to T.A.]; and Norwegian Cancer Society [216104 and 273810 to T.A.]. We thank the National Natural Science Foundation of China(Grant No. 42371038), the Basic Research Innovation GroupProject of Gansu Province (No. 22JR5RA129), and the 2025Northwest Normal University Graduate Research GrantProgram (No. KYZZS2025177). We also thank the anonymous referees for their helpful comments on the manuscript.

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