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Using expected sequence features to improve basecalling accuracy of amplicon pyrosequencing data

Overview of attention for article published in BMC Bioinformatics, April 2016
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

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Title
Using expected sequence features to improve basecalling accuracy of amplicon pyrosequencing data
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1032-7
Pubmed ID
Authors

Thomas S. Rask, Bent Petersen, Donald S. Chen, Karen P. Day, Anders Gorm Pedersen

Abstract

Amplicon pyrosequencing targets a known genetic region and thus inherently produces reads highly anticipated to have certain features, such as conserved nucleotide sequence, and in the case of protein coding DNA, an open reading frame. Pyrosequencing errors, consisting mainly of nucleotide insertions and deletions, are on the other hand likely to disrupt open reading frames. Such an inverse relationship between errors and expectation based on prior knowledge can be used advantageously to guide the process known as basecalling, i.e. the inference of nucleotide sequence from raw sequencing data. The new basecalling method described here, named Multipass, implements a probabilistic framework for working with the raw flowgrams obtained by pyrosequencing. For each sequence variant Multipass calculates the likelihood and nucleotide sequence of several most likely sequences given the flowgram data. This probabilistic approach enables integration of basecalling into a larger model where other parameters can be incorporated, such as the likelihood for observing a full-length open reading frame at the targeted region. We apply the method to 454 amplicon pyrosequencing data obtained from a malaria virulence gene family, where Multipass generates 20 % more error-free sequences than current state of the art methods, and provides sequence characteristics that allow generation of a set of high confidence error-free sequences. This novel method can be used to increase accuracy of existing and future amplicon sequencing data, particularly where extensive prior knowledge is available about the obtained sequences, for example in analysis of the immunoglobulin VDJ region where Multipass can be combined with a model for the known recombining germline genes. Multipass is available for Roche 454 data at http://www.cbs.dtu.dk/services/MultiPass-1.0 , and the concept can potentially be implemented for other sequencing technologies as well.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 3%
Kenya 1 3%
Unknown 27 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 28%
Student > Master 6 21%
Student > Ph. D. Student 6 21%
Student > Bachelor 3 10%
Professor 2 7%
Other 2 7%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 41%
Computer Science 5 17%
Medicine and Dentistry 2 7%
Biochemistry, Genetics and Molecular Biology 2 7%
Mathematics 1 3%
Other 2 7%
Unknown 5 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 28 April 2016.
All research outputs
#12,996,766
of 23,005,189 outputs
Outputs from BMC Bioinformatics
#3,782
of 7,312 outputs
Outputs of similar age
#137,505
of 299,476 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 106 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 299,476 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 53% of its contemporaries.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.