RESCRIPt: Reproducible sequence taxonomy reference database management.
| Autor: | Robeson MS 2nd; University of Arkansas for Medical Sciences, Department of Biomedical Informatics, Little Rock, Arkansas, United States of America., O'Rourke DR; Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America., Kaehler BD; School of Science, University of New South Wales, Canberra, Australia., Ziemski M; Laboratory of Food Systems Biotechnology, Institute of Food, Nutrition, and Health, ETH Zürich, Switzerland., Dillon MR; Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America., Foster JT; Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America., Bokulich NA; Laboratory of Food Systems Biotechnology, Institute of Food, Nutrition, and Health, ETH Zürich, Switzerland. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | PLoS computational biology [PLoS Comput Biol] 2021 Nov 08; Vol. 17 (11), pp. e1009581. Date of Electronic Publication: 2021 Nov 08 (Print Publication: 2021). |
| DOI: | 10.1371/journal.pcbi.1009581 |
| Abstrakt: | Nucleotide sequence and taxonomy reference databases are critical resources for widespread applications including marker-gene and metagenome sequencing for microbiome analysis, diet metabarcoding, and environmental DNA (eDNA) surveys. Reproducibly generating, managing, using, and evaluating nucleotide sequence and taxonomy reference databases creates a significant bottleneck for researchers aiming to generate custom sequence databases. Furthermore, database composition drastically influences results, and lack of standardization limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a Python 3 software package and QIIME 2 plugin for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases. To highlight the breadth and capabilities of RESCRIPt, we provide several examples for working with popular databases for microbiome profiling (SILVA, Greengenes, NCBI-RefSeq, GTDB), eDNA and diet metabarcoding surveys (BOLD, GenBank), as well as for genome comparison. We show that bigger is not always better, and reference databases with standardized taxonomies and those that focus on type strains have quantitative advantages, though may not be appropriate for all use cases. Most databases appear to benefit from some curation (quality filtering), though sequence clustering appears detrimental to database quality. Finally, we demonstrate the breadth and extensibility of RESCRIPt for reproducible workflows with a comparison of global hepatitis genomes. RESCRIPt provides tools to democratize the process of reference database acquisition and management, enabling researchers to reproducibly and transparently create reference materials for diverse research applications. RESCRIPt is released under a permissive BSD-3 license at https://github.com/bokulich-lab/RESCRIPt. Competing Interests: The authors declare that they have no competing interests. |
| Databáze: | MEDLINE |
| Externí odkaz: |
|