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

Abstract

Background: Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lowercost 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. Results: 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 Piphillinpredicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2. Conclusions: Our results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.

PICRUSt2: An improved and customizable approach for metagenome inference

“…The most common approach for profiling communities is to sequence the highly 13 conserved 16S rRNA gene. Functional profiles cannot be directly identified from 16S rRNA 14 gene sequence data due to strain variation and because 16S rRNA genes are not unique among 15 microbes, but several approaches have been developed to infer approximate microbial 16 community functions from taxonomic profiles (and thus amplicon sequences) alone [1][2][3][4][5][6] . 17…”

“…The PICRUSt2 algorithm includes new steps that optimize genome prediction, which we 10 hypothesized would improve prediction accuracy (Fig 1). These are: (1) study sequences are now 11 placed into a pre-existing phylogeny rather than relying on discrete predictions limited to 12 reference OTUs (Fig 1b); (2) predictions are based off of a greatly increased number of 13 reference genomes and gene families (Fig 1c); (3) pathway abundance inference is now more 14 stringently performed (Supp Fig 1); (4) predictions can now be made for higher level 15 phenotypes; and (5) custom databases are easier to integrate into the prediction pipeline. 16…”

Metagenome gene prediction of naturally fermented milk products of India using PICRUSt2 and Piphillin

“…(b) Piphillin analysis (https://piphillin.secondgenome.com/): Alternatively, predictive functionality was also inferred using Piphillin [3], a web-server analysis pipeline. Clustered representative sequences (.fasta) and clustered abundance frequency table (.csv) denoised by both DADA2 and Deblur outputs were used in the analysis.…”

“…Recently, PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States version 2) has been improved for metagenomic gene prediction overcoming some of the limitations of its predecessor, PICRUSt and other similar pipelines [2]. Piphillin, another alternative tool, has also been improved to race with PICRUSt2 in gene functional prediction analysis using 16S rRNA marker gene [3]. PICRUSt and Piphillin software have been applied in studying functional gene prediction in fermented milk products [4,5,6,7].…”

From the Andes to the desert: First overview of the bacterial community in the Rimac river, the main source of water for Lima, Peru

“…We used this information to look for potential human and other animal pathogens in the Risk Group database of the American Biological Safety Association, specifically if species are pathogens for humans or other animals [28]. Finally, we used Piphillin [29,30] to predict functional content based on the frequency of the 16S rRNA sequences comparing them to annotated genomes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Predicted KEGG orthologues (KO) occurrence was retrieved from KEGG (October 2018) using a 97% cutoff threshold to create a gene feature table.…”