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Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

Overview of attention for article published in Giga Science, July 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#11 of 709)
  • High Attention Score compared to outputs of the same age (99th percentile)

Mentioned by

news
3 news outlets
blogs
12 blogs
twitter
93 tweeters
patent
4 patents
peer_reviews
1 peer review site
facebook
3 Facebook pages
wikipedia
2 Wikipedia pages
googleplus
3 Google+ users
reddit
1 Redditor

Citations

dimensions_citation
441 Dimensions

Readers on

mendeley
1193 Mendeley
citeulike
12 CiteULike
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Title
Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species
Published in
Giga Science, July 2013
DOI 10.1186/2047-217x-2-10
Pubmed ID
Authors

Keith R Bradnam, Joseph N Fass, Anton Alexandrov, Paul Baranay, Michael Bechner, Inanç Birol, Sébastien Boisvert, Jarrod A Chapman, Guillaume Chapuis, Rayan Chikhi, Hamidreza Chitsaz, Wen-Chi Chou, Jacques Corbeil, Cristian Del Fabbro, T Roderick Docking, Richard Durbin, Dent Earl, Scott Emrich, Pavel Fedotov, Nuno A Fonseca, Ganeshkumar Ganapathy, Richard A Gibbs, Sante Gnerre, Élénie Godzaridis, Steve Goldstein, Matthias Haimel, Giles Hall, David Haussler, Joseph B Hiatt, Isaac Y Ho, Jason Howard, Martin Hunt, Shaun D Jackman, David B Jaffe, Erich D Jarvis, Huaiyang Jiang, Sergey Kazakov, Paul J Kersey, Jacob O Kitzman, James R Knight, Sergey Koren, Tak-Wah Lam, Dominique Lavenier, François Laviolette, Yingrui Li, Zhenyu Li, Binghang Liu, Yue Liu, Ruibang Luo, Iain MacCallum, Matthew D MacManes, Nicolas Maillet, Sergey Melnikov, Delphine Naquin, Zemin Ning, Thomas D Otto, Benedict Paten, Octávio S Paulo, Adam M Phillippy, Francisco Pina-Martins, Michael Place, Dariusz Przybylski, Xiang Qin, Carson Qu, Filipe J Ribeiro, Stephen Richards, Daniel S Rokhsar, J Graham Ruby, Simone Scalabrin, Michael C Schatz, David C Schwartz, Alexey Sergushichev, Ted Sharpe, Timothy I Shaw, Jay Shendure, Yujian Shi, Jared T Simpson, Henry Song, Fedor Tsarev, Francesco Vezzi, Riccardo Vicedomini, Bruno M Vieira, Jun Wang, Kim C Worley, Shuangye Yin, Siu-Ming Yiu, Jianying Yuan, Guojie Zhang, Hao Zhang, Shiguo Zhou, Ian F Korf

Abstract

The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly.

Twitter Demographics

The data shown below were collected from the profiles of 93 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 1,193 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 46 4%
Germany 11 <1%
United Kingdom 11 <1%
Brazil 10 <1%
Spain 7 <1%
Sweden 6 <1%
Netherlands 6 <1%
Canada 4 <1%
France 4 <1%
Other 41 3%
Unknown 1047 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 332 28%
Researcher 242 20%
Student > Master 176 15%
Student > Bachelor 112 9%
Professor > Associate Professor 69 6%
Other 206 17%
Unknown 56 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 700 59%
Biochemistry, Genetics and Molecular Biology 218 18%
Computer Science 112 9%
Medicine and Dentistry 13 1%
Environmental Science 13 1%
Other 62 5%
Unknown 75 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 164. 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 08 April 2020.
All research outputs
#104,638
of 14,991,077 outputs
Outputs from Giga Science
#11
of 709 outputs
Outputs of similar age
#1,052
of 158,663 outputs
Outputs of similar age from Giga Science
#1
of 1 outputs
Altmetric has tracked 14,991,077 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 709 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.4. This one has done particularly well, scoring higher than 98% 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 158,663 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 99% 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