RESCRIPt: Reproducible sequence taxonomy reference database management

TLDR

Nucleotide sequence and taxonomy reference databases are essential for microbiome, diet metabarcoding, and eDNA studies, yet their reproducible generation and management remain a bottleneck, and inconsistent composition hampers cross‑study comparisons. RESCRIPt was developed to provide a reproducible Python 3 and QIIME 2 tool for generating, managing, evaluating, and comparing reference sequence taxonomy databases. The package offers functions to build databases from popular sources such as SILVA, Greengenes, NCBI‑RefSeq, GTDB, BOLD, GenBank, and to evaluate, compare, and explore their qualitative and quantitative characteristics, demonstrated with examples including hepatitis genomes. Our analyses show that larger databases are not always better, standardized taxonomies and type‑strain‑focused databases offer quantitative advantages, most databases benefit from quality filtering, clustering degrades quality, and RESCRIPt enables reproducible, transparent reference database creation. RESCRIPt is released under a permissive BSD‑3 license at https://github.com/bokulich‑lab/RESCRIPt.

Abstract

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 .