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FlowClus: efficiently filtering and denoising pyrosequenced amplicons

Overview of attention for article published in BMC Bioinformatics, March 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)

Mentioned by

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10 tweeters
facebook
1 Facebook page

Citations

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21 Dimensions

Readers on

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49 Mendeley
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Title
FlowClus: efficiently filtering and denoising pyrosequenced amplicons
Published in
BMC Bioinformatics, March 2015
DOI 10.1186/s12859-015-0532-1
Pubmed ID
Authors

John M Gaspar, W Kelley Thomas

Abstract

Reducing the effects of sequencing errors and PCR artifacts has emerged as an essential component in amplicon-based metagenomic studies. Denoising algorithms have been designed that can reduce error rates in mock community data, but they change the sequence data in a manner that can be inconsistent with the process of removing errors in studies of real communities. In addition, they are limited by the size of the dataset and the sequencing technology used. FlowClus uses a systematic approach to filter and denoise reads efficiently. When denoising real datasets, FlowClus provides feedback about the process that can be used as the basis to adjust the parameters of the algorithm to suit the particular dataset. When used to analyze a mock community dataset, FlowClus produced a lower error rate compared to other denoising algorithms, while retaining significantly more sequence information. Among its other attributes, FlowClus can analyze longer reads being generated from all stages of 454 sequencing technology, as well as from Ion Torrent. It has processed a large dataset of 2.2 million GS-FLX Titanium reads in twelve hours; using its more efficient (but less precise) trie analysis option, this time was further reduced, to seven minutes. Many of the amplicon-based metagenomics datasets generated over the last several years have been processed through a denoising pipeline that likely caused deleterious effects on the raw data. By using FlowClus, one can avoid such negative outcomes while maintaining control over the filtering and denoising processes. Because of its efficiency, FlowClus can be used to re-analyze multiple large datasets together, thereby leading to more standardized conclusions. FlowClus is freely available on GitHub (jsh58/FlowClus); it is written in C and supported on Linux.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 6%
Germany 1 2%
United Kingdom 1 2%
Unknown 44 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 27%
Student > Master 11 22%
Student > Ph. D. Student 8 16%
Professor 3 6%
Student > Bachelor 3 6%
Other 6 12%
Unknown 5 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 43%
Biochemistry, Genetics and Molecular Biology 11 22%
Immunology and Microbiology 3 6%
Chemistry 2 4%
Environmental Science 2 4%
Other 4 8%
Unknown 6 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 August 2015.
All research outputs
#4,518,343
of 15,444,557 outputs
Outputs from BMC Bioinformatics
#1,960
of 5,640 outputs
Outputs of similar age
#67,043
of 225,884 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 15,444,557 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 5,640 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 63% 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 225,884 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% 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