↓ Skip to main content

Removing Noise From Pyrosequenced Amplicons

Overview of attention for article published in BMC Bioinformatics, January 2011
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

4 tweeters
5 patents
1 Facebook page
1 Wikipedia page


1182 Dimensions

Readers on

1095 Mendeley
21 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Removing Noise From Pyrosequenced Amplicons
Published in
BMC Bioinformatics, January 2011
DOI 10.1186/1471-2105-12-38
Pubmed ID

Christopher Quince, Anders Lanzen, Russell J Davenport, Peter J Turnbaugh


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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1,095 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 24 2%
United Kingdom 14 1%
Spain 10 <1%
Germany 7 <1%
France 7 <1%
Canada 6 <1%
Denmark 6 <1%
Italy 3 <1%
Sweden 3 <1%
Other 31 3%
Unknown 984 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 316 29%
Researcher 275 25%
Student > Master 137 13%
Student > Bachelor 66 6%
Professor > Associate Professor 48 4%
Other 157 14%
Unknown 96 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 574 52%
Biochemistry, Genetics and Molecular Biology 115 11%
Environmental Science 109 10%
Immunology and Microbiology 30 3%
Engineering 26 2%
Other 113 10%
Unknown 128 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 November 2020.
All research outputs
of 19,496,717 outputs
Outputs from BMC Bioinformatics
of 6,606 outputs
Outputs of similar age
of 102,832 outputs
Outputs of similar age from BMC Bioinformatics
of 1 outputs
Altmetric has tracked 19,496,717 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,606 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 102,832 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them