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Metassembler: merging and optimizing de novo genome assemblies

Overview of attention for article published in Genome Biology (Online Edition), September 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)

Mentioned by

50 tweeters
1 Facebook page
1 Google+ user


89 Dimensions

Readers on

239 Mendeley
2 CiteULike
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Metassembler: merging and optimizing de novo genome assemblies
Published in
Genome Biology (Online Edition), September 2015
DOI 10.1186/s13059-015-0764-4
Pubmed ID

Alejandro Hernandez Wences, Michael C. Schatz


Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence. We apply it to the four genomes from the Assemblathon competitions and show it consistently and substantially improves the contiguity and quality of each assembly. We also develop guidelines for meta-assembly by systematically evaluating 120 permutations of merging the top 5 assemblies of the first Assemblathon competition. The software is open-source at http://metassembler.sourceforge.net .

Twitter Demographics

The data shown below were collected from the profiles of 50 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 239 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Germany 2 <1%
Norway 2 <1%
France 1 <1%
Netherlands 1 <1%
Korea, Republic of 1 <1%
Ireland 1 <1%
Portugal 1 <1%
Australia 1 <1%
Other 7 3%
Unknown 218 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 80 33%
Researcher 57 24%
Student > Master 28 12%
Student > Bachelor 14 6%
Student > Doctoral Student 10 4%
Other 34 14%
Unknown 16 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 131 55%
Biochemistry, Genetics and Molecular Biology 55 23%
Computer Science 15 6%
Chemical Engineering 3 1%
Medicine and Dentistry 3 1%
Other 11 5%
Unknown 21 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 23 February 2017.
All research outputs
of 21,298,857 outputs
Outputs from Genome Biology (Online Edition)
of 4,017 outputs
Outputs of similar age
of 264,121 outputs
Outputs of similar age from Genome Biology (Online Edition)
of 1 outputs
Altmetric has tracked 21,298,857 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,017 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.1. This one has gotten more attention than average, scoring higher than 74% 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 264,121 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 92% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them