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Universal Count Correction for High-Throughput Sequencing

Overview of attention for article published in PLoS Computational Biology, March 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
3 blogs
twitter
47 X users
wikipedia
2 Wikipedia pages
googleplus
1 Google+ user

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
194 Mendeley
citeulike
9 CiteULike
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Title
Universal Count Correction for High-Throughput Sequencing
Published in
PLoS Computational Biology, March 2014
DOI 10.1371/journal.pcbi.1003494
Pubmed ID
Authors

Tatsunori B. Hashimoto, Matthew D. Edwards, David K. Gifford

Abstract

We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called FIXSEQ. We demonstrate that FIXSEQ substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives.

X Demographics

X Demographics

The data shown below were collected from the profiles of 47 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 194 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 7%
Germany 3 2%
Canada 3 2%
Spain 2 1%
Denmark 2 1%
Norway 2 1%
Austria 1 <1%
Sweden 1 <1%
Finland 1 <1%
Other 9 5%
Unknown 157 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 62 32%
Student > Ph. D. Student 59 30%
Professor > Associate Professor 11 6%
Student > Master 11 6%
Professor 10 5%
Other 28 14%
Unknown 13 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 100 52%
Biochemistry, Genetics and Molecular Biology 46 24%
Computer Science 12 6%
Mathematics 5 3%
Environmental Science 3 2%
Other 12 6%
Unknown 16 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 45. 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 31 December 2019.
All research outputs
#942,839
of 25,706,302 outputs
Outputs from PLoS Computational Biology
#706
of 9,024 outputs
Outputs of similar age
#8,986
of 236,582 outputs
Outputs of similar age from PLoS Computational Biology
#14
of 140 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,024 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.2. This one has done particularly well, scoring higher than 92% 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 236,582 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.