Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences

Wei, Ze-Gang; Zhang, Xiaodan; Cao, Ming; Liu, Fei; Qian, Yu; Zhang, Shao-Wu

doi:10.3389/fmicb.2021.644012

Cited by 28 publications

(25 citation statements)

References 133 publications

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“…GG has not been updated since 2013, is no longer maintained, and has the smallest taxonomic classification and thus least diversity out of the major reference databases [ 31 ]. Regardless, GG is still commonly used [ 19 , 26 , 36 , 43 , 45 , 46 ], and it has been shown to have higher accuracy than the Ribosomal Database Project (RDP) for characterizing some bacterial communities [ 3 ]. Its continued use also has practical reasons; researchers can compare their taxonomic results to a previous study that used GG, or they might already have an in-house alignment process setup that uses this database.…”

Section: Discussionmentioning

confidence: 99%

Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities

Dacey

Chain

2021

BMC Bioinformatics

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Background Taxonomic classification of genetic markers for microbiome analysis is affected by the numerous choices made from sample preparation to bioinformatics analysis. Paired-end read merging is routinely used to capture the entire amplicon sequence when the read ends overlap. However, the exclusion of unmerged reads from further analysis can result in underestimating the diversity in the sequenced microbial community and is influenced by bioinformatic processes such as read trimming and the choice of reference database. A potential solution to overcome this is to concatenate (join) reads that do not overlap and keep them for taxonomic classification. The use of concatenated reads can outperform taxonomic recovery from single-end reads, but it remains unclear how their performance compares to merged reads. Using various sequenced mock communities with different amplicons, read length, read depth, taxonomic composition, and sequence quality, we tested how merging and concatenating reads performed for genus recall and precision in bioinformatic pipelines combining different parameters for read trimming and taxonomic classification using different reference databases. Results The addition of concatenated reads to merged reads always increased pipeline performance. The top two performing pipelines both included read concatenation, with variable strengths depending on the mock community. The pipeline that combined merged and concatenated reads that were quality-trimmed performed best for mock communities with larger amplicons and higher average quality sequences. The pipeline that used length-trimmed concatenated reads outperformed quality trimming in mock communities with lower quality sequences but lost a significant amount of input sequences for taxonomic classification during processing. Genus level classification was more accurate using the SILVA reference database compared to Greengenes. Conclusions Merged sequences with the addition of concatenated sequences that were unable to be merged increased performance of taxonomic classifications. This was especially beneficial in mock communities with larger amplicons. We have shown for the first time, using an in-depth comparison of pipelines containing merged vs concatenated reads combined with different trimming parameters and reference databases, the potential advantages of concatenating sequences in improving resolution in microbiome investigations.

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Section: Discussionmentioning

confidence: 99%

Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities

Dacey

Chain

2021

BMC Bioinformatics

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“…Open reference methods employ a combination of both the above methods. First, closed‐reference clustering is performed to assign OTUs, and the unassigned sequences remaining after closed‐reference OTU picking are grouped using a de novo clustering method (Wei et al, 2021). DOTUR (Schloss & Handelsman, 2005), CD‐HIT (Fu et al, 2012), and VSEARCH (Rognes et al, 2016) are examples of OTU picking methods.…”

Section: Next‐generation Sequencing (Ngs): the Grand Scheme In Actionmentioning

confidence: 99%

A primer on metagenomics and next‐generation sequencing in fish gut microbiome research

Johny

Puthusseri

Bhat

2021

Aquaculture Research

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Fishes constitute a vital component of the aquatic ecosystem, that interacts with physical, chemical, and biological components of the ecosystem. They are found in almost all aquatic environments ranging from high mountain streams to the abyssal and hadal zones of the deepest oceans. With an origin dating back to 600 million years and an estimate of 34,600 species described till date (FishBase, 2021), fishes comprise nearly half of the total vertebrate diversity (Nelson, 2007).Fishes are associated with complex assemblages of microorganisms, comprising bacteria, archaea, viruses, yeasts, and protists, collectively known as 'microbiota' or 'microbiome' (Merrifield & Rodiles, 2015). The characteristic microbial community inhabiting a particular environment along with their genomes and theatre of activity is referred to as the 'microbiome' (Gilbert et al., 2016;Prescott, 2017). However, in certain contexts, the term 'microbiota' is used to define the collection of microorganisms and the term 'microbiome' to refers to the collection of microbial genomes (Burokas et al., 2015).Microorganisms take residence in almost every organ of the fish such as skin, gills, digestive tract, internal organs like the liver, kidney, spleen, and light-emitting organs (Austin, 2002). However, fish gut microflora is often acclaimed as an 'extra organ' owing to its significant contribution to important physiological functions of the host such as digestion, metabolism, reproduction, development, and immune response (Butt & Volkoff, 2019). Further, the composition of the gut microflora is affected by a plethora of factors such as habitat salinity, temperature, trophic level, taxonomy, feeding habits and host selective pressures within the gut, leading to the development of a unique and diverse microflora (Bevins & Salzman, 2011;Sullam et al., 2012). Therefore, it may be safely presumed that the fish gut microbiome, representing 'worlds within worlds' (Butt & Volkoff, 2019) is a biodiversity hotspot and a promising source for bioprospecting applications.

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“…Numerous OTU clustering algorithms have been integrated into the popular sequenceanalysis pipelines, such as QIIME2 (Bolyen et al 2019), mothur (Schloss et al 2009), and USEARCH (Edgar 2010). Overall, existing methods for grouping 16S rRNA gene amplicons into OTUs can be categorized in three ways: de novo, closed-reference, and open-reference (Wei et al 2021). However, none of these approaches produce the same results in terms of obtaining OTUs, even when using the same dataset (He et al 2015;Westcott and Schloss 2015).…”

Section: Introductionmentioning

confidence: 99%

OTUs clustering should be avoided for defining oral microbiome

Regueira-Iglesias

Vázquez-González

Balsa-Castro

et al. 2021

Preprint

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This in silico investigation aimed to: 1) evaluate a set of primer pairs with high coverage, including those most commonly used in the literature, to find the different oral species with 16S rRNA gene amplicon similarity/identity (ASI) values ≥97%; and 2) identify oral species that may be erroneously clustered in the same operational taxonomic unit (OTU) and ascertain whether they belong to distinct genera or other higher taxonomic ranks. Thirty-nine primer pairs were employed to obtain amplicon sequence variants (ASVs) from the complete genomes of 186 bacterial and 135 archaeal species. For each primer, ASVs without mismatches were aligned using BLASTN and their similarity values were obtained. Finally, we selected ASVs from different species with an ASI value ≥97% that were covered 100% by the query sequences. For each primer, the percentage of species-level coverage with no ASI≥97% (SC-NASI≥97%) was calculated. Based on the SC-NASI≥97% values, the best primer pairs were OP_F053-KP_R020 for bacteria (65.05%), KP_F018-KP_R002 for archaea (51.11%), and OP_F114-KP_R031 for bacteria and archaea together (52.02%). Eighty percent of the oral-bacteria and oral-archaea species shared an ASI≥97% with at least one other taxa, including Campylobacter, Rothia, Streptococcus, and Tannerella, which played conflicting roles in the oral microbiota. Moreover, around a quarter and a third of these two-by-two similarity relationships were between species from different bacteria and archaea genera, respectively. Furthermore, even taxa from distinct families, orders, and classes could be grouped in the same cluster. Consequently, irrespective of the primer pair used, OTUs constructed with a 97% similarity provide an inaccurate description of oral-bacterial and oral-archaeal species, greatly affecting microbial diversity parameters. As a result, clustering by OTUs impacts the credibility of the associations between some oral species and certain health and disease conditions. This limits significantly the comparability of the microbial diversity findings reported in oral microbiome literature.

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Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences

Cited by 28 publications

References 133 publications

Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities

Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities

A primer on metagenomics and next‐generation sequencing in fish gut microbiome research

OTUs clustering should be avoided for defining oral microbiome

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