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

Yu-Peng Hsieh; Yuan-Mao Hung; Mong-Hsun Tsai; Liang-Chuan Lai; Eric Y Chuang

doi:10.3389/fbinf.2022.905489

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

Front Bioinform. 2022 Aug 3:2:905489. doi: 10.3389/fbinf.2022.905489. eCollection 2022.

Authors

Yu-Peng Hsieh¹, Yuan-Mao Hung², Mong-Hsun Tsai^{3

4}, Liang-Chuan Lai^{5

4}, Eric Y Chuang^{1

2

6

4}

Affiliations

¹ Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
² Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
³ Institute of Biotechnology, National Taiwan University, Taipei, Taiwan.
⁴ Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan.
⁵ Graduate Institute of Physiology, College of Medicine, National Taiwan University, Taipei, Taiwan.
⁶ College of Biomedical Engineering, China Medical University, Taichung, Taiwan.

Abstract

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.

Keywords: 16S full length; 16S rRNA (16S rDNA); ITGDB; metagenomics 16S; sequence classification; taxonomy assignment; third-generation sequencing.