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Space-efficient and exact de Bruijn graph representation based on a Bloom filter

Overview of attention for article published in Algorithms for Molecular Biology, September 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#2 of 264)
  • High Attention Score compared to outputs of the same age (94th percentile)

Mentioned by

blogs
2 blogs
twitter
15 X users
patent
2 patents
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
298 Dimensions

Readers on

mendeley
217 Mendeley
citeulike
6 CiteULike
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Title
Space-efficient and exact de Bruijn graph representation based on a Bloom filter
Published in
Algorithms for Molecular Biology, September 2013
DOI 10.1186/1748-7188-8-22
Pubmed ID
Authors

Rayan Chikhi, Guillaume Rizk

Abstract

The de Bruijn graph data structure is widely used in next-generation sequencing (NGS). Many programs, e.g. de novo assemblers, rely on in-memory representation of this graph. However, current techniques for representing the de Bruijn graph of a human genome require a large amount of memory (≥30 GB).

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 217 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
France 4 2%
Germany 2 <1%
Bulgaria 1 <1%
Vietnam 1 <1%
Australia 1 <1%
Brazil 1 <1%
Korea, Republic of 1 <1%
South Africa 1 <1%
Other 3 1%
Unknown 196 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 53 24%
Researcher 40 18%
Student > Master 34 16%
Student > Bachelor 18 8%
Student > Doctoral Student 13 6%
Other 34 16%
Unknown 25 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 74 34%
Computer Science 56 26%
Biochemistry, Genetics and Molecular Biology 36 17%
Engineering 5 2%
Immunology and Microbiology 5 2%
Other 14 6%
Unknown 27 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 13 February 2024.
All research outputs
#1,078,165
of 23,073,835 outputs
Outputs from Algorithms for Molecular Biology
#2
of 264 outputs
Outputs of similar age
#9,326
of 180,276 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 4 outputs
Altmetric has tracked 23,073,835 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 264 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done particularly well, scoring higher than 99% 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 180,276 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 94% of its contemporaries.
We're also able to compare this research output to 4 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