Ironing out the wrinkles in the rare biosphere through improved OTU clustering

By Susan M. Huse, D. Welch, Hilary G. Morrison, Mitchell L Sogin. Environmental Microbiology, 2010

1,324 citations recorded by the source corpus.

Deep sequencing of PCR amplicon libraries facilitates the detection of low-abundance populations in environmental DNA surveys of complex microbial communities. At the same time, deep sequencing can lead to overestimates of microbial diversity through the generation of low-frequency, error-prone reads. Even with sequencing error rates below 0.005 per nucleotide position, the common method of generating operational taxonomic units (OTUs) by multiple sequence alignment and complete-linkage clustering significantly increases the number of predicted OTUs and inflates richness estimates. We show that a 2% single-linkage preclustering methodology followed by an average-linkage clustering based on pairwise alignments more accurately predicts expected OTUs in both single and pooled template preparations of known taxonomic composition. This new clustering method can reduce the OTU richness in environmental samples by as much as 30–60% but does not reduce the fraction of OTUs in long-tailed rank abundance curves that defines the rare biosphere.

Subfields: Ecology

View Paper: https://www.semanticscholar.org/paper/8593b2589a1abcead4c91967e154765693339e31

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

Citations in the corpus

Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates (2017, background): “While many rare OTUs are beyond doubt real biological entities 10 , an appreciable fraction of rare OTUs are likely errors from PCR and sequencing 11 .”
DECIPHER, a Search-Based Approach to Chimera Identification for 16S rRNA Sequences (2011, background): “Eliminating chimeric sequences is now understood by the research community as an important step to take before submitting sequences to public databases or before assembling sequences from short fragments produced by next-generation sequencing approaches (8, 10, 13).”
454 Pyrosequencing reveals bacterial diversity of activated sludge from 14 sewage treatment plants (2011, methodology): “Sequences were denoised using the ‘pre.cluster’ command in Mothur platform to remove sequences that are likely due to pyrosequencing errors (Huse et al., 2010; Roeselers et al., 2011).”
Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies (2011, methodology): “Two less computationally demanding denoising algorithms, single linkage pre-clustering and SeqNoise, have also been developed to remove sequencing errors [13,24].”
The rare bacterial biosphere. (2012, background): “Clustering will incorporate them to the correct OTU and alleviate the problem (Huse et al. 2010).”
Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences (2014, methodology): “During the last 10 years, various de novo approaches have facilitated OTU identification, including algorithms that typically use pairwise alignments to compute a distance matrix before grouping sequences into OTUs (DOTUR (Schloss and Handelsman, 2005), ESPRIT (Sun et al. , 2009), SLP (Huse et al. ,”
Growing Unculturable Bacteria (2012, background): “While DNA sequencing from mixed populations is known to be subject to artifacts that can inflate the apparent diversity, careful controls have minimized this phenomenon (34).”
EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data (2011, background): “Although the phylogenetic information contained in hypervariable region tags is generally concordant with information contained in full-length sequences at higher taxonomic levels [3,5], careful screening of read quality is necessary to avoid overestimating community diversity [34,35].”
Fungal community analysis by high-throughput sequencing of amplified markers – a user's guide (2013, background): “…Phytologist Trustwww.newphytologist.com Research New Phytologist294 similar sequences left to enter, makes them efficient in the handling of sequencing errors (Huse et al., 2010), and single-linkage clustering may to some extent replace more computationally intensive quality management programs.”
Updating the 97% identity threshold for 16S ribosomal RNA OTUs (2017, background): “These reads have errors due to PCR and sequencing which can cause large numbers of spurious OTUs (e.g., (Huse et al., 2010; Edgar and Flyvbjerg, 2014)).”
A Filtering Method to Generate High Quality Short Reads Using Illumina Paired-End Technology (2013, background): “Yet, random sequencing errors can inflate the number of observed Operational Taxonomic Units (OTUs) in analyses that rely upon de novo clustering methods and therefore challenge the accuracy of microbial diversity estimates [3,4].”
Accuracy and quality assessment of 454 GS-FLX Titanium pyrosequencing (2011, result): “We analyze here the 86,237 sequences that passed the quality filters, representing the six control DNA fragments from three 454 GS-FLX runs.”
Strengths and Limitations of 16S rRNA Gene Amplicon Sequencing in Revealing Temporal Microbial Community Dynamics (2014, neutral): “operational taxonomic units (OTUs) [9].”
Defining the Human Microbiome (2012, background): “Both sequencing error[38, 39] and issues related to alignment, especially multiple sequence alignment[40-43], can inflate the number of OTUs immensely.”