To compare the performance of prokaryotic taxonomy classifiers using curated 16S full-length rRNA sequences

Hung, Yuan-Mao; Lyu, Wei-Ni; Tsai, Ming-Lin; Liu, Chiang-Lin; Lai, Liang‐Chuan; Tsai, Mong‐Hsun; Chuang, Eric Y.

doi:10.1016/j.compbiomed.2022.105416

Cited by 14 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Like our recent meta-analysis, 81 we used MetaPhlAn 3 33 and SPINGO 34 to annotate metagenomic and 16S species-level taxonomic profiling, respectively, because they provide highest accuracy at the (sub)species level. 82 , 83 MetaPhlAn, computationally efficient, has been applied to process human microbiome data and develop the ExperimentHub, 51 including over 20 thousands of annotated microbial metagenomes from human so far. Annotation from MetaPhlAn will facilitate the generalization of the impact and scientific relevance of research findings.…”

Section: Discussionmentioning

confidence: 99%

Robust cross-cohort gut microbiome associations with COVID-19 severity

Ghosh

McCann

et al. 2023

Gut Microbes

View full text Add to dashboard Cite

Although many recent studies have examined associations between the gut microbiome and COVID-19 disease severity in individual patient cohorts, questions remain on the robustness across international cohorts of the biomarkers they reported. Here, we performed a meta-analysis of eight shotgun metagenomic studies of COVID-19 patients (comprising 1,023 stool samples) and 23 > 16S rRNA gene amplicon sequencing (16S) cohorts (2,415 total stool samples). We found that disease severity (as defined by the WHO clinical progression scale) was associated with taxonomic and functional microbiome differences. This alteration in gut microbiome configuration peaks at days 7–30 post diagnosis, after which the gut microbiome returns to a configuration that becomes more similar to that of healthy controls over time. Furthermore, we identified a core set of species that were consistently associated with disease severity across shotgun metagenomic and 16S cohorts, and whose abundance can accurately predict disease severity category of SARS-CoV-2 infected subjects, with Actinomyces oris abundance predicting population-level mortality rate of COVID-19. Additionally, we used relational diet-microbiome databases constructed from cohort studies to predict microbiota-targeted diet patterns that would modulate gut microbiota composition toward that of healthy controls. Finally, we demonstrated the association of disease severity with the composition of intestinal archaeal, fungal, viral, and parasitic communities. Collectively, this study has identified robust COVID-19 microbiome biomarkers, established accurate predictive models as a basis for clinical prognostic tests for disease severity, and proposed biomarker-targeted diets for managing COVID-19 infection.

show abstract

Section: Discussionmentioning

confidence: 99%

Robust cross-cohort gut microbiome associations with COVID-19 severity

Ghosh

McCann

et al. 2023

Gut Microbes

View full text Add to dashboard Cite

show abstract

“…SINTAX was used for taxonomy assignment in the database comparison experiment because SINTAX showed good performance in sequence classification and provided comprehensive assignment results ( Hung et al, 2022 ). Each reference database, including RDP, SILVA, and Greengenes, was used as the SINTAX’s reference for taxonomic assignment tasks.…”

Section: Methodsmentioning

confidence: 99%

16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences

et al. 2022

Self Cite

View full text Add to dashboard Cite

Analyzing 16S ribosomal RNA (rRNA) sequences allows researchers to elucidate the prokaryotic composition of an environment. In recent years, third-generation sequencing technology has provided opportunities for researchers to perform full-length sequence analysis of bacterial 16S rRNA. RDP, SILVA, and Greengenes are the most widely used 16S rRNA databases. Many 16S rRNA classifiers have used these databases as a reference for taxonomic assignment tasks. However, some of the prokaryotic taxonomies only exist in one of the three databases. Furthermore, Greengenes and SILVA include a considerable number of taxonomies that do not have the resolution to the species level, which has limited the classifiers’ performance. In order to improve the accuracy of taxonomic assignment at the species level for full-length 16S rRNA sequences, we manually curated the three databases and removed the sequences that did not have a species name. We then established a taxonomy-based integrated database by considering both taxonomies and sequences from all three 16S rRNA databases and validated it by a mock community. Results showed that our taxonomy-based integrated database had improved taxonomic resolution to the species level. The integrated database and the related datasets are available at https://github.com/yphsieh/ItgDB.

show abstract

“…The RDP taxonomy is one of the few widely accepted prokaryotic taxonomies used for bacteria and archaea identification and has been suggested as the preferred training set for several rRNA-based taxonomic classifiers ( 1 ). This taxonomy, with a few exceptions, is based on validly named species and higher taxonomic ranks, using rRNA gene sequences from species type strains.…”

Section: Announcementmentioning

confidence: 99%