Data-Driven Modeling for Species-Level Taxonomic Assignment From 16S rRNA: Application to Human Microbiomes

Abstract

With the emergence of next-generation sequencing (NGS) technology, there have been a large number of metagenomic studies that estimated the bacterial composition via 16S ribosomal RNA (16S rRNA) amplicon sequencing. In particular, subsets of the hypervariable regions in 16S rRNA, such as V1–V2 and V3–V4, are targeted using high-throughput sequencing. The sequences from different taxa are assigned to a specific taxon based on the sequence homology. Since such sequences are highly homologous or identical between species in the same genus, it is challenging to determine the exact species using 16S rRNA sequences only. Therefore, in this study, homologous species groups were defined to obtain maximum resolution related with species using 16S rRNA. For the taxonomic assignment using 16S rRNA, three major 16S rRNA databases are independently used since the lineage of certain bacteria is not consistent among these databases. On the basis of the NCBI taxonomy classification, we re-annotated inconsistent lineage information in three major 16S rRNA databases. For each species, we constructed a consensus sequence model for each hypervariable region and determined homologous species groups that consist of indistinguishable species in terms of sequence homology. Using a k -nearest neighbor method and the species consensus sequence models, the species-level taxonomy was determined. If the species determined is a member of homologous species groups , the species group is assigned instead of a specific species. Notably, the results of the evaluation on our method using simulated and mock datasets showed a high correlation with the real bacterial composition. Furthermore, in the analysis of real microbiome samples, such as salivary and gut microbiome samples, our method successfully performed species-level profiling and identified differences in the bacterial composition between different phenotypic groups.

Are Faecal Microbiota Analyses on Species-Level Suitable Clinical Biomarkers? A Pilot Study in Subjects with Morbid Obesity

“…This gene contains nine hypervariable regions (V1–V9) of varying conservation flanked by highly conserved regions. Sequences are highly homologous between species within the same genus, and it is therefore challenging to obtain species resolution by 16S rRNA sequencing [ 12 , 13 ].…”

Species-Level Resolution of Female Bladder Microbiota from 16S rRNA Amplicon Sequencing

“…In our study, we compared several combinations of classification scheme components in the context of the female bladder microbiome to identify bacterial species. This builds on several prior studies comparing databases and classifiers ( 47 – 49 ), identifiers and databases ( 50 – 52 ), classifiers and identifiers ( 53 , 54 ), or individual components of the classification scheme for application in microbiome studies. However, to our knowledge, there have been no studies that systematically compared combinations of all the components of a classification scheme (classifiers, databases, and identifiers) for species-level assignment from 16S amplicon experiments.…”

“…While database selection is recognized as an important decision in taxonomy assignment for amplicon sequencing studies, few studies have compared the effect of different databases on taxonomy assignment ( 47 , 49 , 55 , 56 ), and none have focused on bladder bacterial species. Park and Won recently compared database performance on mock microbial communities and similarly found that the Silva database outperformed the Greengenes database ( 55 ).…”

Accurate Five-category Classification for Colorectal Cancer Using Gut Microbiome 16S rRNA Sequencing

“…To overcome the absent species annotation, we performed 16S rRNA sequencing to profile the OTU level and genus-level abundance of gut bacteria. Moreover, considering inconsistent abundance changes among OTUs assigned as the same species 29,30 , we constructed classifiers using the relative OTUs and genera to capture the most informative taxonomies that could effectively distinguish CRC patients from healthy people.…”

“…We formalized the task of CRC or adenoma prediction as a classification problem and focused on operational classification units (OTUs). By using OTUs instead of taxonomic annotations as features, the problem that the same OTU may be inconsistently annotated when using different reference databases was avoided 29,30 .…”