Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences

• Published in: BMC genomics 2020-01, Vol.21 (1), p.56-12, Article 56
• Main Authors: Narayan, Nicole R, Weinmaier, Thomas, Laserna-Mendieta, Emilio J, Claesson, Marcus J, Shanahan, Fergus, Dabbagh, Karim, Iwai, Shoko, DeSantis, Todd Z
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Anopheles; Clustering; Databases, Nucleic Acid; Datasets; DNA sequencing; Error analysis; Error correction; Error correction & detection; Gene sequencing; Genes; Genomes; Genomic databases; Genomics; Metagenomics; Metagenomics - methods; Microbial activity; Microorganisms; Parameters; Phylogenetic analysis; Phylogenetics; Phylogeny; Predictions; Researchers; Ribosomal RNA; RNA; RNA, Ribosomal, 16S - genetics; rRNA 16S; Sequence alignment; Sequence analysis; Sequence Analysis, DNA - methods; Shotgun sequencing; Software; Taxonomy; Test procedures
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-019-6427-1

Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters. To evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (