Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers

By Zongzhi Z. Liu, Todd Z. DeSantis, Gary L. Andersen, Rob Knight. Nucleic Acids Research, 2008

584 citations recorded by the source corpus.

The recent introduction of massively parallel pyrosequencers allows rapid, inexpensive analysis of microbial community composition using 16S ribosomal RNA (rRNA) sequences. However, a major challenge is to design a workflow so that taxonomic information can be accurately and rapidly assigned to each read, so that the composition of each community can be linked back to likely ecological roles played by members of each species, genus, family or phylum. Here, we use three large 16S rRNA datasets to test whether taxonomic information based on the full-length sequences can be recaptured by short reads that simulate the pyrosequencer outputs. We find that different taxonomic assignment methods vary radically in their ability to recapture the taxonomic information in full-length 16S rRNA sequences: most methods are sensitive to the region of the 16S rRNA gene that is targeted for sequencing, but many combinations of methods and rRNA regions produce consistent and accurate results. To process large datasets of partial 16S rRNA sequences obtained from surveys of various microbial communities, including those from human body habitats, we recommend the use of Greengenes or RDP classifier with fragments of at least 250 bases, starting from one of the primers R357, R534, R798, F343 or F517.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ef4fa0d54c3e0279200b6419ec1c950e44950ffc

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Citations in the corpus

EzBioCloud: a genome-driven database and platform for microbiome identification and discovery (2024, unknown): “This method facilitates further analysis with ANI and is particularly valuable for genome sequences that do not have or have only a partial 16S rRNA sequence, where a complete 16S rRNA sequence is often necessary for accurate taxonomic assignment [5, 6].”
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, background): “Setting a quality threshold remains at the researcher’s discretion; however, it is good practice to use only those sequences with the highest possible quality.”
A general species delimitation method with applications to phylogenetic placements (2013, methodology): “Numerous approaches exist for associating anonymous reads/ query sequences with known species, for instance, nearest-neighbor BLAST (Liu et al. , 2008) or the naı¨ve Bayesian classifier (Wang et al. , 2007).”
Meta-analyses of human gut microbes associated with obesity and IBD (2014, background): “Even computational differences, including which taxonomy database is used and which taxonomy assignment algorithm is used, can have surprisingly large effects [2].”
The Intestinal Microbiome in Early Life: Health and Disease (2014, background): “Although there is still no consensus on this, comparative analysis with full length Sanger sequencing of the 16S rRNA gene supports the use of primers that amplify the V4 region (26), as they yield the most taxonomically informative sequences (27).”
Assessing and Improving Methods Used in Operational Taxonomic Unit-Based Approaches for 16S rRNA Gene Sequence Analysis (2011, background): “Numerous studies have shown that diverse approaches to classification are robust (10, 16, 24).”
Taxonomic Classification of Bacterial 16S rRNA Genes Using Short Sequencing Reads: Evaluation of Effective Study Designs (2013, methodology): “Despite not being designed for short sequences, for a number of primers these classifiers showed excellent recovery and coverage even for 100 nt sequences [19].”
An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea (2011, methodology): “This is especially important because which taxonomy is used can have a larger effect on the results than which assignment method is used (Liu et al., 2008).”
Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies (2009, background): “In a recent work, the coverage of several known primers was surveyed using several sets of metagenomic data, and the primers with better performance were recommended for future work [21].”
A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses (2009, background): “This primer set is ‘universal’ for bacterial 16S rRNA and has been found to be well suited for the phylogenetic analysis of pyrosequencing reads (Liu et al., 2007, 2008).”
Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys (2011, background): “There are many taxonomic classification algorithms available (Liu et al., 2008), and several reference databases that can be used with any given algorithm.”