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CGAL: computing genome assembly likelihoods

Overview of attention for article published in Genome Biology, January 2013
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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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
2 blogs
twitter
49 X users
googleplus
1 Google+ user

Citations

dimensions_citation
78 Dimensions

Readers on

mendeley
253 Mendeley
citeulike
11 CiteULike
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Title
CGAL: computing genome assembly likelihoods
Published in
Genome Biology, January 2013
DOI 10.1186/gb-2013-14-1-r8
Pubmed ID
Authors

Atif Rahman, Lior Pachter

Abstract

Assembly algorithms have been extensively benchmarked using simulated data so that results can be compared to ground truth. However, in de novo assembly, only crude metrics such as contig number and size are typically used to evaluate assembly quality. We present CGAL, a novel likelihood-based approach to assembly assessment in the absence of a ground truth. We show that likelihood is more accurate than other metrics currently used for evaluating assemblies, and describe its application to the optimization and comparison of assembly algorithms. Our methods are implemented in software that is freely available at http://bio.math.berkeley.edu/cgal/.

X Demographics

X Demographics

The data shown below were collected from the profiles of 49 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 253 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 14 6%
Germany 5 2%
Canada 3 1%
Japan 2 <1%
Norway 2 <1%
United Kingdom 2 <1%
Korea, Republic of 1 <1%
Czechia 1 <1%
Austria 1 <1%
Other 4 2%
Unknown 218 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 68 27%
Student > Ph. D. Student 63 25%
Student > Master 27 11%
Student > Bachelor 22 9%
Professor > Associate Professor 15 6%
Other 40 16%
Unknown 18 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 146 58%
Computer Science 31 12%
Biochemistry, Genetics and Molecular Biology 30 12%
Mathematics 8 3%
Environmental Science 4 2%
Other 11 4%
Unknown 23 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 44. 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 17 August 2023.
All research outputs
#939,643
of 25,371,288 outputs
Outputs from Genome Biology
#657
of 4,467 outputs
Outputs of similar age
#7,723
of 290,732 outputs
Outputs of similar age from Genome Biology
#5
of 48 outputs
Altmetric has tracked 25,371,288 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 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 85% 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 290,732 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 97% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.