Background

In 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

Axel Perin

Axel Poulet

Flora Ponelle

Laurence Lafarge

Marie Ollier

Maud Privat

Nagi S. El Saghir

Nancy Uhrhammer

Sandrine Viala

Stephanie Decousus

Yannick Bidet

Yves-Jean Bignon

BioMed Research International

English

PubMed

Background

In 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. 



Results

AmpliconNoise 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. 



Conclusions

AmpliconNoise 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.</p

Quince, Christopher

Lanzen, Anders

Davenport, Russell J

Turnbaugh, Peter J.

Enlighten: Publications

Removing noise from pyrosequenced amplicons

Screening for BRCA mutations in women with familial risk of breast or ovarian cancer is an ideal situation for high-throughput sequencing, providing large amounts of low cost data. However, 454, Roche, and Ion Torrent, Thermo Fisher, technologies produce homopolymer-associated indel errors, complicating their use in routine diagnostics. We developed software, named AGSA, which helps to detect false positive mutations in homopolymeric sequences. Seventy-two familial breast cancer cases were analysed in parallel by amplicon 454 pyrosequencing and Sanger dideoxy sequencing for genetic variations of the BRCA genes. All 565 variants detected by dideoxy sequencing were also detected by pyrosequencing. Furthermore, pyrosequencing detected 42 variants that were missed with Sanger technique. Six amplicons contained homopolymer tracts in the coding sequence that were systematically misread by the software supplied by Roche. Read data plotted as histograms by AGSA software aided the analysis considerably and allowed validation of the majority of homopolymers. As an optimisation, additional 250 patients were analysed using microfluidic amplification of regions of interest (Access Array Fluidigm) of the BRCA genes, followed by 454 sequencing and AGSA analysis. AGSA complements a complete line of high-throughput diagnostic sequence analysis, reducing time and costs while increasing reliability, notably for homopolymer tracts

Directory of Open Access Journals

Improved Efficiency and Reliability of NGS Amplicon Sequencing Data Analysis for Genetic Diagnostic Procedures Using AGSA Software

Davenport, Russell J.

Warwick Research Archives Portal Repository

A: At least 1 in 20 16S rRNA sequence records currently held in public repositories is estimated to contain substantial anomalies. Appl Environ Microb

Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol

Alignment and clustering of phylogenetic markers - implications for microbial diversity studies.

AmpliconNoise Google Code Project.

Cheap third-generation sequencing. Nat Methods

Core Team: R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing,

Farmerie W: ESPRIT: estimating species richness using large collections of 16S rRNA pyrosequences. Nucleic Acids Res

FD: Hepatitis C Virus Transmission Bottlenecks Analyzed by Deep Sequencing.

Genome sequencing in microfabricated highdensity picolitre reactors. Nature

Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci

Herndl GJ: Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci USA

Hugenholtz B: PyroTagger: A fast, accurate pipeline for analysis of rRNA amplicon pyrosequence data. The Open Journal

Hugenholtz P: Bellerophon: a program to detect chimeric sequences in multiple sequence alignments. Bioinformatics

Hugenholtz P: Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates. Environ Microbiol

I: Characteristics of 454 pyrosequencing data-enabling realistic simulation with flowsim. Bioinformatics

Kunkel TA: DNA polymerase fidelity and the polymerase chain reaction.

NIH HMP Working Grp: The NIH Human Microbiome Project. Genome Res

Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins. Proc Natl Acad Sci USA

P: Metagenomic study of the oral microbiota by Illumina high-throughput sequencing.

Pattern Recognition and Machine Learning Springer:

QIIME allows analysis of high-throughput community sequencing data. Nat Methods

Raftery AE: How many clusters? Which clustering method? Answers via model-based cluster analysis.

Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions. Nat Methods

Reducing the impact of PCR-mediated recombination in molecular evolution and environmental studies using a newgeneration high-fidelity DNA polymerase. Biotechniques

Sogin ML: Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ Microbiol

Sogin ML: Microbial population structures in the deep marine biosphere. Science

T: MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res

The Human Microbiome Consortium,

WT: Accurate determination of microbial diversity from 454 pyrosequencing data. Nat Methods

&lt;b&gt;Background&lt;/b&gt;&lt;p&gt;&lt;/p&gt;

In 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. &lt;p&gt;&lt;/p&gt;



&lt;b&gt;Results&lt;/b&gt;&lt;p&gt;&lt;/p&gt;

AmpliconNoise 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. &lt;p&gt;&lt;/p&gt;



&lt;b&gt;Conclusions&lt;/b&gt;&lt;p&gt;&lt;/p&gt;

AmpliconNoise 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.&lt;p&gt;&lt;/p&gt

Enlighten

Background: In 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. Results: AmpliconNoise 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. Conclusions: AmpliconNoise 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.publishedVersio

Lanzén, Anders

University of Bergen

Removing Noise From Pyrosequenced Amplicons

International audienceScreening for BRCA mutations in women with familial risk of breast or ovarian cancer is an ideal situation for high-throughput sequencing, providing large amounts of low cost data. However, 454, Roche, and Ion Torrent, Thermo Fisher, technologies produce homopolymer-associated indel errors, complicating their use in routine diagnostics. We developed software, named AGSA, which helps to detect false positive mutations in homopolymeric sequences. Seventy-two familial breast cancer cases were analysed in parallel by amplicon 454 pyrosequencing and Sanger dideoxy sequencing for genetic variations of the BRCA genes. All 565 variants detected by dideoxy sequencing were also detected by pyrosequencing. Furthermore, pyrosequencing detected 42 variants that were missed with Sanger technique. Six amplicons contained homopolymer tracts in the coding sequence that were systematically misread by the software supplied by Roche. Read data plotted as histograms by AGSA software aided the analysis considerably and allowed validation of the majority of homopolymers. As an optimisation, additional 250 patients were analysed using microfluidic amplification of regions of interest (Access Array Fluidigm) of the BRCA genes, followed by 454 sequencing and AGSA analysis. AGSA complements a complete line of high-throughput diagnostic sequence analysis, reducing time and costs while increasing reliability, notably for homopolymer tracts

Poulet, Axel

Privat, Maud

Ponelle, Flora

Viala, Sandrine

Decousus, Stephanie

Perin, Axel

Lafarge, Laurence

Ollier, Marie

Saghir, Nagi, S El

Uhrhammer, Nancy

Bignon, Yves-Jean

Bidet, Yannick

HAL Clermont Université

Saghir, Nagi,

Archive Ouverte en Sciences de l'Information et de la Communication

Background: In 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. Results: AmpliconNoise 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. Conclusions: AmpliconNoise 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

Removing noise from pyrosequenced amplicons

Abstract

Similar works

Full text

Available Versions