Facilitating Bioinformatics Reproducibility

作     者：Keefe, Christopher R. Dillon, Matthew R. Herman, Chloe Jewell, Mary Wood, Colin V. Bolyen, Evan Caporaso, J. Gregory 

作者机构：Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States Department of Epidemiology University of Washington SeattleWA United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Bioinformatics 

摘      要：Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving many different tools. Additionally many biologists aren t trained in how to effectively record their bioinformatics analysis steps to ensure reproducibility, so critical information is often missing. Software tools used in bioinformatics can automate provenance tracking of the results they generate, removing most barriers to bioinformatics reproducibility. Here we present an implementation of that idea, Provenance Replay, a tool for generating new executable code from results generated with the QIIME 2 bioinformatics platform, and discuss considerations for bioinformatics developers who wish to implement similar functionality in their software. © 2023, CC BY.