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De novo likelihood-based measures for comparing genome assemblies

Overview of attention for article published in BMC Research Notes, August 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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
2 blogs
twitter
32 X users
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2 patents
facebook
1 Facebook page

Citations

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46 Dimensions

Readers on

mendeley
118 Mendeley
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7 CiteULike
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Title
De novo likelihood-based measures for comparing genome assemblies
Published in
BMC Research Notes, August 2013
DOI 10.1186/1756-0500-6-334
Pubmed ID
Authors

Mohammadreza Ghodsi, Christopher M Hill, Irina Astrovskaya, Henry Lin, Dan D Sommer, Sergey Koren, Mihai Pop

Abstract

The current revolution in genomics has been made possible by software tools called genome assemblers, which stitch together DNA fragments "read" by sequencing machines into complete or nearly complete genome sequences. Despite decades of research in this field and the development of dozens of genome assemblers, assessing and comparing the quality of assembled genome sequences still relies on the availability of independently determined standards, such as manually curated genome sequences, or independently produced mapping data. These "gold standards" can be expensive to produce and may only cover a small fraction of the genome, which limits their applicability to newly generated genome sequences. Here we introduce a de novo  probabilistic measure of assembly quality which allows for an objective comparison of multiple assemblies generated from the same set of reads. We define the quality of a sequence produced by an assembler as the conditional probability of observing the sequenced reads from the assembled sequence. A key property of our metric is that the true genome sequence maximizes the score, unlike other commonly used metrics.

X Demographics

X Demographics

The data shown below were collected from the profiles of 32 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 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 5%
Norway 2 2%
Germany 2 2%
Italy 1 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Sweden 1 <1%
Japan 1 <1%
New Zealand 1 <1%
Other 0 0%
Unknown 102 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 31%
Student > Ph. D. Student 29 25%
Student > Master 13 11%
Student > Bachelor 8 7%
Student > Postgraduate 7 6%
Other 18 15%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 63 53%
Computer Science 23 19%
Biochemistry, Genetics and Molecular Biology 15 13%
Mathematics 3 3%
Earth and Planetary Sciences 2 2%
Other 3 3%
Unknown 9 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 15 November 2022.
All research outputs
#1,076,425
of 25,446,666 outputs
Outputs from BMC Research Notes
#107
of 4,516 outputs
Outputs of similar age
#9,060
of 210,791 outputs
Outputs of similar age from BMC Research Notes
#4
of 69 outputs
Altmetric has tracked 25,446,666 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,516 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done particularly well, scoring higher than 97% 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 210,791 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 95% of its contemporaries.
We're also able to compare this research output to 69 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 95% of its contemporaries.