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Improved base-calling and quality scores for 454 sequencing based on a Hurdle Poisson model

Overview of attention for article published in BMC Bioinformatics, November 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

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5 X users
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1 patent

Readers on

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61 Mendeley
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3 CiteULike
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Title
Improved base-calling and quality scores for 454 sequencing based on a Hurdle Poisson model
Published in
BMC Bioinformatics, November 2012
DOI 10.1186/1471-2105-13-303
Pubmed ID
Authors

Kristof De Beuf, Joachim De Schrijver, Olivier Thas, Wim Van Criekinge, Rafael A Irizarry, Lieven Clement

Abstract

454 pyrosequencing is a commonly used massively parallel DNA sequencing technology with a wide variety of application fields such as epigenetics, metagenomics and transcriptomics. A well-known problem of this platform is its sensitivity to base-calling insertion and deletion errors, particularly in the presence of long homopolymers. In addition, the base-call quality scores are not informative with respect to whether an insertion or a deletion error is more likely. Surprisingly, not much effort has been devoted to the development of improved base-calling methods and more intuitive quality scores for this platform.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 4 7%
United States 2 3%
Germany 1 2%
Sweden 1 2%
Kenya 1 2%
Spain 1 2%
Argentina 1 2%
Unknown 50 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 33%
Student > Ph. D. Student 15 25%
Student > Master 5 8%
Student > Bachelor 3 5%
Professor 3 5%
Other 11 18%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 61%
Mathematics 4 7%
Biochemistry, Genetics and Molecular Biology 4 7%
Computer Science 4 7%
Immunology and Microbiology 3 5%
Other 3 5%
Unknown 6 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 20 December 2023.
All research outputs
#6,268,107
of 25,093,754 outputs
Outputs from BMC Bioinformatics
#2,105
of 7,647 outputs
Outputs of similar age
#42,664
of 184,118 outputs
Outputs of similar age from BMC Bioinformatics
#28
of 102 outputs
Altmetric has tracked 25,093,754 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,647 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 71% 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 184,118 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 76% of its contemporaries.
We're also able to compare this research output to 102 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 73% of its contemporaries.