Removing Noise From Pyrosequenced Amplicons

By Christopher Quince, Anders Lanzén, Russell J. Davenport, Peter J. Turnbaugh. BMC Bioinformatics, 2011

1,533 citations recorded by the source corpus.

BackgroundIn many environmental genomics applications a homologous region of DNA from a diverse sample is first amplified by PCR and then sequenced. The next generation sequencing technology, 454 pyrosequencing, has allowed much larger read numbers from PCR amplicons than ever before. This has revolutionised the study of microbial diversity as it is now possible to sequence a substantial fraction of the 16S rRNA genes in a community. However, there is a growing realisation that because of the large read numbers and the lack of consensus sequences it is vital to distinguish noise from true sequence diversity in this data. Otherwise this leads to inflated estimates of the number of types or operational taxonomic units (OTUs) present. Three sources of error are important: sequencing error, PCR single base substitutions and PCR chimeras. We present AmpliconNoise, a development of the PyroNoise algorithm that is capable of separately removing 454 sequencing errors and PCR single base errors. We also introduce a novel chimera removal program, Perseus, that exploits the sequence abundances associated with pyrosequencing data. We use data sets where samples of known diversity have been amplified and sequenced to quantify the effect of each of the sources of error on OTU inflation and to validate these algorithms.ResultsAmpliconNoise outperforms alternative algorithms substantially reducing per base error rates for both the GS FLX and latest Titanium protocol. All three sources of error lead to inflation of diversity estimates. In particular, chimera formation has a hitherto unrealised importance which varies according to amplification protocol. We show that AmpliconNoise allows accurate estimates of OTU number. Just as importantly AmpliconNoise generates the right OTUs even at low sequence differences. We demonstrate that Perseus has very high sensitivity, able to find 99% of chimeras, which is critical when these are present at high frequencies.ConclusionsAmpliconNoise followed by Perseus is a very effective pipeline for the removal of noise. In addition the principles behind the algorithms, the inference of true sequences using Expectation-Maximization (EM), and the treatment of chimera detection as a classification or 'supervised learning' problem, will be equally applicable to new sequencing technologies as they appear.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2fffdffb89ae9f28fa3d32bef1606fe7a32bc738

How researchers see this paper

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

In Ecology

In Molecular Biology

Citations in the corpus

Special features of RAD Sequencing data: implications for genotyping (2012, background): “In recent years, many sources of noise in high throughput DNA and RNA sequencing data have been identiﬁed and either mitigated during library preparation or corrected during analysis (Aird et al. 2011; Quince et al. 2011, Meacham et al. 2011; Quail et al. 2008).”
Combining real-time PCR and next-generation DNA sequencing to provide quantitative comparisons of fungal aerosol populations (2014, methodology): “Results were trimmed to equivalent quality parameters as listed above, denoised (Quince et al., 2011), and OTUs were calculated at 97% similarity.”
Comparison of the Levels of Bacterial Diversity in Freshwater, Intertidal Wetland, and Marine Sediments by Using Millions of Illumina Tags (2012, methodology): “Even though we used the Illumina instrument with stringent quality controls to determine the tag sequences, there are still many biases introduced by PCR error, sequencing error, sequencing depth, and the bioinformatic pipeline (10, 12, 26, 32, 36).”
Experimental and analytical tools for studying the human microbiome (2011, background): “Different tools exist to detect and remove chimeric sequence”
ViVaMBC: estimating viral sequence variation in complex populations from illumina deep-sequencing data using model-based clustering (2015, background): “As a potential solution other authors suggested to incorporate quality scores when modeling the error distribution [10-13].”
Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data (2013, background): “…even the most effective quality filtering (Qu, Hashimoto & Morishita 2009; Schroder et al. 2009; Bravo & Irizarry 2010; Leek et al. 2010; Meacham et al. 2011; Minoche, Dohm & Himmelbauer 2011; Quince et al. 2011; Benjamini & Speed 2012; Victoria et al. 2012) will not produce error-free data sets.”
Conducting a Microbiome Study (2014, methodology): “, 2012), and Perseus (Quince et al., 2011).”
Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics (2012, methodology): “…ignored the impact of sequencing and PCR errors which can inflate OTU diversities [10], but even after the application of algorithms capable of removing those errors [11], observed diversities remain high in most environments and abundances are still skewed to low abundances in almost all [10,12].”
Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies (2015, methodology): “122 Despite the vast availability of algorithms and software for analysis of 16S metagenomics datasets, QIIME seems to be established as the “gold standard”.”
Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform (2013, background): “Numerous Numerous groups have worked to develop bioinformatic solutions that make the 454 platform a robust approach to characterizing microbial communities (9, 10).”
A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents (2013, methodology): “Following this initial quality filtering, we conducted additional denoising of flowgrams using a mothur implementation of Pyronoise [46] that uses an expectation-maximization algorithm to adjust flowgrams and translates them to DNA sequences (command shhh.”
Fungal community analysis by high-throughput sequencing of amplified markers – a user's guide (2013, methodology): “Different polymerases tend to differ in fidelity, and choosing a high-fidelity polymerase will reduce the number of nucleotide incorporation errors produced during PCR amplification.”
obitools: a unix‐inspired software package for DNA metabarcoding (2016, methodology): “Sequences annotated by OBICLEAN can be further postprocessed using chimeric sequences iden-tiﬁcation program like PERSEUS (Quince et al. 2011) to point out the most evident chimeric sequences.”