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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 (#17 of 1,171)
  • High Attention Score compared to outputs of the same age (99th percentile)

Mentioned by

news
3 news outlets
blogs
12 blogs
twitter
97 X users
patent
9 patents
peer_reviews
1 peer review site
facebook
3 Facebook pages
wikipedia
3 Wikipedia pages
googleplus
3 Google+ users
reddit
1 Redditor

Citations

dimensions_citation
595 Dimensions

Readers on

mendeley
1297 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 97 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 1,297 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 45 3%
United Kingdom 11 <1%
Germany 10 <1%
Brazil 10 <1%
Spain 7 <1%
Sweden 6 <1%
Norway 4 <1%
Netherlands 4 <1%
Canada 4 <1%
Other 38 3%
Unknown 1158 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 332 26%
Researcher 260 20%
Student > Master 190 15%
Student > Bachelor 117 9%
Professor > Associate Professor 72 6%
Other 223 17%
Unknown 103 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 706 54%
Biochemistry, Genetics and Molecular Biology 246 19%
Computer Science 115 9%
Immunology and Microbiology 18 1%
Medicine and Dentistry 15 1%
Other 70 5%
Unknown 127 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 162. 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 28 November 2023.
All research outputs
#253,222
of 25,477,125 outputs
Outputs from Giga Science
#17
of 1,171 outputs
Outputs of similar age
#1,703
of 209,565 outputs
Outputs of similar age from Giga Science
#1
of 3 outputs
Altmetric has tracked 25,477,125 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 1,171 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.7. 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 209,565 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 3 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