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Probabilistic alignment leads to improved accuracy and read coverage for bisulfite sequencing data

Overview of attention for article published in BMC Bioinformatics, November 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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5 X users
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1 Google+ user

Citations

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7 Dimensions

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47 Mendeley
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Title
Probabilistic alignment leads to improved accuracy and read coverage for bisulfite sequencing data
Published in
BMC Bioinformatics, November 2013
DOI 10.1186/1471-2105-14-337
Pubmed ID
Authors

Changjin Hong, Nathan L Clement, Spencer Clement, Saher Sue Hammoud, Douglas T Carrell, Bradley R Cairns, Quinn Snell, Mark J Clement, William Evan Johnson

Abstract

DNA methylation has been linked to many important biological phenomena. Researchers have recently begun to sequence bisulfite treated DNA to determine its pattern of methylation. However, sequencing reads from bisulfite-converted DNA can vary significantly from the reference genome because of incomplete bisulfite conversion, genome variation, sequencing errors, and poor quality bases. Therefore, it is often difficult to align reads to the correct locations in the reference genome. Furthermore, bisulfite sequencing experiments have the additional complexity of having to estimate the DNA methylation levels within the sample.

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 2%
Denmark 1 2%
France 1 2%
Canada 1 2%
Unknown 43 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 45%
Student > Bachelor 5 11%
Other 4 9%
Student > Ph. D. Student 4 9%
Student > Master 3 6%
Other 5 11%
Unknown 5 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 45%
Biochemistry, Genetics and Molecular Biology 8 17%
Computer Science 5 11%
Engineering 2 4%
Medicine and Dentistry 2 4%
Other 2 4%
Unknown 7 15%
Attention Score in Context

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 13 December 2013.
All research outputs
#7,148,720
of 23,340,595 outputs
Outputs from BMC Bioinformatics
#2,745
of 7,388 outputs
Outputs of similar age
#83,248
of 304,995 outputs
Outputs of similar age from BMC Bioinformatics
#35
of 103 outputs
Altmetric has tracked 23,340,595 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,388 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 61% 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 304,995 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 71% of its contemporaries.
We're also able to compare this research output to 103 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 65% of its contemporaries.