Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis

Kylee Degatano
,
Aseel Awdeh
,
Wes Dingman
,
George Grant
,
Farzaneh Khajouei
,
Elizabeth Kiernan
,
Kishori Konwar
,
Kaylee Mathews
ORCID logo ,
Kevin Palis
,
Nikelle Petrillo
,
Geraldine Van der Auwera
,
Chengchen (Rex) Wang
,
Jessica Way
,
WARP Pipelines
*
Version 1 : Received: 26 January 2024 / Approved: 30 January 2024 / Online: 30 January 2024 (13:17:07 CET)

How to cite: Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.(.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. https://doi.org/10.20944/preprints202401.2131.v1 Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.(.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. https://doi.org/10.20944/preprints202401.2131.v1

Abstract

In the era of large data, the cloud is increasingly used as a computing environment, necessitating the development of cloud-compatible pipelines that can provide uniform analysis across disparate biological datasets. The WDL Analysis Research Pipelines (WARP) repository is a GitHub repository of open-source, cloud-optimized WDL workflows for biological data processing that are semantically versioned, tested, and documented. A companion repository, WARP-Tools, hosts Docker containers and custom tools used in WARP workflows.

Keywords

WARP, WDL, pipelines, FAIR pipelines, cloud pipelines, sequencing analysis

Subject

Biology and Life Sciences, Biology and Biotechnology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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