Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons.

By Brian J. Haas, Dirk Gevers, A. Earl, M. Feldgarden, D. Ward, G. Giannoukos, Dawn M. Ciulla, D. Tabbaa, S. Highlander, Erica Sodergren and 5 more. Genome Research, 2011

3,331 citations recorded by the source corpus.

Bacterial diversity among environmental samples is commonly assessed with PCR-amplified 16S rRNA gene (16S) sequences. Perceived diversity, however, can be influenced by sample preparation, primer selection, and formation of chimeric 16S amplification products. Chimeras are hybrid products between multiple parent sequences that can be falsely interpreted as novel organisms, thus inflating apparent diversity. We developed a new chimera detection tool called Chimera Slayer (CS). CS detects chimeras with greater sensitivity than previous methods, performs well on short sequences such as those produced by the 454 Life Sciences (Roche) Genome Sequencer, and can scale to large data sets. By benchmarking CS performance against sequences derived from a controlled DNA mixture of known organisms and a simulated chimera set, we provide insights into the factors that affect chimera formation such as sequence abundance, the extent of similarity between 16S genes, and PCR conditions. Chimeras were found to reproducibly form among independent amplifications and contributed to false perceptions of sample diversity and the false identification of novel taxa, with less-abundant species exhibiting chimera rates exceeding 70%. Shotgun metagenomic sequences of our mock community appear to be devoid of 16S chimeras, supporting a role for shotgun metagenomics in validating novel organisms discovered in targeted sequence surveys.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1d147321d1d598586906364a86935a16c79d50d9

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Citations in the corpus

Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis (2013, methodology): “Potentially chimeric sequences were removed using the ChimeraSlayer program (Haas et al., 2011).”
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights (2016, background): “available [57-59], and thus quantitative microbial signature are inherently less difficult to transfer across cohorts and populations.”
454 Pyrosequencing reveals bacterial diversity of activated sludge from 14 sewage treatment plants (2011, methodology): “PCR chimeras were filtered out using Chimera Slayer (Haas et al., 2011).”
The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression (2018, neutral): “The filtered sequences were clustered into OTUs based on a 97% similarity threshold using UCLUST algorithm (48) and the chimeric sequences were removed by ChimeraSlayer (49).”
Conducting a Microbiome Study (2014, methodology): “Use of error-correcting polymerases may reduce PCR error, and longer annealing times can also assist in reducing chimera formation (Haas et al., 2011) (see below).”
Inferring Correlation Networks from Genomic Survey Data (2012, background): “The first challenge is obtaining reliable and informative counts from 16S rRNA gene sequences by filtering spurious reads and grouping the remaining reads in a meaningful way [2], [3], [4].”
Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys (2011, background): “, 2009) of 408 135 chimera-filtered (Chimera Slayer; Haas et al., 2011) sequences in the GG database.”
Propionibacterium acnes strain populations in the human skin microbiome associated with acne (2013, methodology): “Suspected chimeras were identified using ChimeraSlayer and WigeoN (Haas et al., 2011).”