RESCRIPt: Reproducible sequence taxonomy reference database management for the masses

Abstract

BackgroundNucleotide 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 standardizations limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a software package 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.ResultsTo 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.ConclusionsRESCRIPt 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.

Beating Naive Bayes at Taxonomic Classification of 16S rRNA Gene Sequences

“…Hence, mature, existing methods for classification (NBC and some alignment-based classifiers) have already neared the upper limits of classification accuracy. The relationship between read length, primer selection, marker-gene target, sequence entropy, and taxonomic resolution has been well documented for 16S rRNA genes and other common targets, and even with long sequence reads (e.g., full-length 16S rRNA genes) species-level resolution can be challenging for many clades (Wang et al, 2007;Liu et al, 2008;Bokulich et al, 2018b;Johnson et al, 2019;Robeson et al, 2020). This is in part complicated by muddled microbial taxonomies (Oren and Garrity, 2014;Yarza et al, 2014) and misannotations and other issues with reference databases used for taxonomic classification (Kozlov et al, 2016).…”

“…3. Improvement of reference sequence and taxonomy databases (Parks et al, 2018;Robeson et al, 2020). 4.…”

“…Where they do share the same sequence, one matching classification is chosen at random as the label for that sequence. The performance of such a classifier represents the upper limit of possible classification accuracy (Busia et al, 2019;Robeson et al, 2020).…”

“…A critical step in any microbial census study is the taxonomic classification of observed DNA sequences, to infer the relative abundance of different taxonomic groups. This is performed by comparison of observed sequences to a reference database of sequences from known taxa, using an appropriate taxonomic classifier (Robeson et al, 2020). A large number of taxonomic classification methods have been developed and benchmarked for classification of marker gene sequences (Bokulich et al, 2018b;Gardner et al, 2019), but among the most successful and ubiquitous in microbiome studies have been naive Bayes classifiers (NBC).…”

Glycomic profiling of the gut microbiota by Glycan-seq

“…Amplicon sequence variants (ASVs) were generated by DADA2 analysis, which were then classified to family and genus levels using the q2-feature-classifier [20], a Naïve Bayes machine learning classifier plugin in the QIIME2. Operational taxonomic units (OTUs) were generated by the RESCRIPt QIIME2 plugin running a feature classifier trained on the V3–V4 region of the 16S rRNA gene using a preformatted SILVA 138 reference database [21, 22]. An equal sampling depth of 10,000 was selected for every sample for assessing the diversities.…”