We are analyzing https://link.springer.com/article/10.1186/1471-2105-12-38.

Title:
Removing Noise From Pyrosequenced Amplicons | BMC Bioinformatics
Description:
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
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Keywords {🔍}

data, sequences, sequence, noise, otus, pcr, sets, reads, pyrosequencing, ampliconnoise, algorithm, figure, removal, chimera, set, number, diversity, chimeras, table, true, original, article, error, otu, titanium, reference, sequencing, denoised, chimeric, google, algorithms, clustering, pubmed, scholar, good, community, read, generated, flowgram, full, errors, results, shown, numbers, base, pyronoise, size, denoiser, observed, artificial,

Topics {✒️}

illumina high-throughput sequencing article download pdf full size image full size table exploiting rank-abundance distributions v4-v5 data sets v4-v5 error rates 16s rrna clones full access expectation-maximization algorithm initialised titanium data v4-v5 titanium v4-v5 data v5 'artificial community' related subjects standard base-calling procedure 16s rrna genes single-linkage preclustering algorithm 'artificial community' data 16s rrna pyrosequences illumina hiseq 2000 explore microbial diversity 16s rrna amplicons v2 'mock communities' v2 mock communities applying single-linkage preclustering data set size uk/~quince/data/ampliconnoise bmc bioinformatics 12 linearly scaled y-axis privacy choices/manage cookies 16s rrna diversity authors’ original file 16s rrna regions 16s rrna primers complete data likelihood biomed central gs flx increasing published data sets de novo classification complete data set complete linkage clustering high fidelity polymerases model-based cluster analysis author information authors test data sets pcr amplicon diversity high resolution studies dna polymerase fidelity article quince single-linkage preclustering

Questions {❓}

Fraley C, Raftery AE: How many clusters?

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         description: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. 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. 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.
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