Taxonomic classification and abundance estimation using 16S and WGS—A comparison using controlled reference samples

•WGS allows for better taxonomic annotation of microbiomes in comparison to 16S.•16S based analysis suffers from database- and tool biases.•Distinct taxonomy assignment algorithms produce similar results when using WGS data.•Using 16S data for metagenomic investigations can lead to conclusions that...

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Bibliographic Details
Published in:Forensic science international : genetics 2020-05, Vol.46, p.102257-102257, Article 102257
Main Authors: Khachatryan, Lusine, de Leeuw, Rick H., Kraakman, Margriet E.M., Pappas, Nikos, te Raa, Marije, Mei, Hailiang, de Knijff, Peter, Laros, Jeroen F.J.
Format: Article
Language:English
Subjects:
16S sequencing
Bacteria - classification
Bacteria - genetics
Datasets as Topic
High-Throughput Nucleotide Sequencing
Metagenomics
Real-Time Polymerase Chain Reaction
RNA, Ribosomal, 16S - genetics
Skin microbiome
Taxonomic profiling
WGS sequencing
Whole Genome Sequencing
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Summary:•WGS allows for better taxonomic annotation of microbiomes in comparison to 16S.•16S based analysis suffers from database- and tool biases.•Distinct taxonomy assignment algorithms produce similar results when using WGS data.•Using 16S data for metagenomic investigations can lead to conclusions that are incorrect. The assessment of microbiome biodiversity is the most common application of metagenomics. While 16S sequencing remains standard procedure for taxonomic profiling of metagenomic data, a growing number of studies have clearly demonstrated biases associated with this method. By using Whole Genome Shotgun sequencing (WGS) metagenomics, most of the known restrictions associated with 16S data are alleviated. However, due to the computationally intensive data analyses and higher sequencing costs, WGS based metagenomics remains a less popular option. Selecting the experiment type that provides a comprehensive, yet manageable amount of information is a challenge encountered in many metagenomics studies. In this work, we created a series of artificial bacterial mixes, each with a different distribution of skin-associated microbial species. These mixes were used to estimate the resolution of two different metagenomic experiments - 16S and WGS - and to evaluate several different bioinformatics approaches for taxonomic read classification. In all test cases, WGS approaches provide much more accurate results, in terms of taxa prediction and abundance estimation, in comparison to those of 16S. Furthermore, we demonstrate that a 16S dataset, analysed using different state of the art techniques and reference databases, can produce widely different results. In light of the fact that most forensic metagenomic analysis are still performed using 16S data, our results are especially important.
ISSN:1872-4973
1878-0326
DOI:10.1016/j.fsigen.2020.102257